The Exact N-point Generating Function in Polyakov-Burgers Turbulence

We find the exact N-point generating function in Polyakov's approach to Burgers turbulence.Comment: 7 pages,Latex,no figure

The structural connectome constrains fast brain dynamics

Brain activity during rest displays complex, rapidly evolving patterns in space and time. Structural connections comprising the human connectome are hypothesized to impose constraints on the dynamics of this activity. Here, we use magnetoencephalography (MEG) to quantify the extent to which fast neural dynamics in the human brain are constrained by structural connections inferred from diffusion MRI tractography. We characterize the spatio-temporal unfolding of whole-brain activity at the millisecond scale from source-reconstructed MEG data, estimating the probability that any two brain regions will significantly deviate from baseline activity in consecutive time epochs. We find that the structural connectome relates to, and likely affects, the rapid spreading of neuronal avalanches, evidenced by a significant association between these transition probabilities and structural connectivity strengths (r = 0.37, p<0.0001). This finding opens new avenues to study the relationship between brain structure and neural dynamics

ПАРАЛЛЕЛЬНАЯ ВЕРСИЯ ДЕТЕКТОРА ЭКСТРЕМАЛЬНЫХ ОСОБЫХ ТОЧЕК ИЗОБРАЖЕНИЙ

The article presents a parallel version of the detector of extremal key points, which are used to describe, analyze and compare digital images by local descriptors. Local descriptors are determined in neighborhoods of the extremal key points. The orientation of the descriptors are found with aid of Histograms of Oriented Gradient. The specificity of the parallel architecture of NVIDIA graphics cards has been taken into account in the developed version, oriented to the implementation on CUDA. It accelerated the calculation of the extremal key points by several orders. Computation of the not oriented extremal key points for images of the FullHD-size on the budget graphics card takes 5–6 ms. The oriented extremal key points are computed within 11–12 ms.Рассматривается параллельная версия детектора особых (ключевых, характерных) точекэкстремумов, применяемых для описания, анализа и сравнения изображений с помощью локальных дескрипторов, которые вычисляются в окрестностях найденных точек. Для задания ориентации дескрипторов предлагается использовать локальные гистограммы ориентированного градиента. В версии, предназначенной для выполнения на программно-аппаратной архитектуре CUDA, учтена специфика графических процессоров фирмы NVIDIA, что позволило ускорить вычисление экстремальных особых точек на несколько порядков. Вычисление неориентированных экстремальных особых точек изображения FullHD-размера на бюджетной видеокарте занимает 5–6 мс, ориентированных – 11–12 мс

Алгоритм быстрого вычисления локальных гистограмм изображения на видеокарте

An algorithm of parallel computation of image histograms of different types, including brightness and oriented gradient ones, on video cards of various types is presented. Now local histograms are used for solution of some tasks of image processing and recognition, but their application is restricted due to the long computational time. One of the difficulties appearing during parallel computations of this vector feature is the large number of conflicts of simultaneous access to video memory sells. In the developed version, the number of conflicts of simultaneous access are many times reduced. It accelerated the computations. For instance, 9D vectors of histograms of oriented gradient for all 256×256 windows of a HD image are calculated on the GPU NVIDIA GeForce GTX 1060 within 1,9 msec, whereas the same computations made by the CPU Intel Core i7-6700 take 151 msec.Рассматривается алгоритм параллельного вычисления гистограмм различных типов, в том числе яркости и ориентированного градиента, предназначенный для выполнения на видеокартах, которые поддерживают массивные параллельные вычисления. В настоящее время локальные гистограммы используются для решения задач обработки и распознавания изображений, однако их применение ограничено из-за большого времени вычисления для всех пикселов изображения. Одна из основных трудностей, возникающих при вычислении этих векторных признаков, – большое число конфликтов одновременного доступа к ячейкам видеопамяти, в которые записываются одинаковые значения характеристики. В предложенном алгоритме существенно уменьшено число конфликтов одновременного доступа, что позволило значительно уменьшить время его выполнения. Так, например, девятимерные векторы локальных гистограмм ориентированного градиента для всех 256×256 окон изображения размера HD вычисляются на видеокарте GPU NVIDIA GeForce GTX 1060 за 1,9 мс, в то время как на процессоре Intel Core i7-6700 c частотой 3,4 ГГц – за 151 мс

Structural subnetwork evolution across the life-span: rich-club, feeder, seeder

The impact of developmental and aging processes on brain connectivity and the connectome has been widely studied. Network theoretical measures and certain topological principles are computed from the entire brain, however there is a need to separate and understand the underlying subnetworks which contribute towards these observed holistic connectomic alterations. One organizational principle is the rich-club - a core subnetwork of brain regions that are strongly connected, forming a high-cost, high-capacity backbone that is critical for effective communication in the network. Investigations primarily focus on its alterations with disease and age. Here, we present a systematic analysis of not only the rich-club, but also other subnetworks derived from this backbone - namely feeder and seeder subnetworks. Our analysis is applied to structural connectomes in a normal cohort from a large, publicly available lifespan study. We demonstrate changes in rich-club membership with age alongside a shift in importance from 'peripheral' seeder to feeder subnetworks. Our results show a refinement within the rich-club structure (increase in transitivity and betweenness centrality), as well as increased efficiency in the feeder subnetwork and decreased measures of network integration and segregation in the seeder subnetwork. These results demonstrate the different developmental patterns when analyzing the connectome stratified according to its rich-club and the potential of utilizing this subnetwork analysis to reveal the evolution of brain architectural alterations across the life-span

Effects of 17β-Estradiol on Distribution of Pituitary Isoforms of Luteinizing Hormone and Follicle-Stimulating Hormone during the Follicular Phase of the Bovine Estrous Cycle

The objective of this study was to examine the influence of 17β-estradiol (E2) on distribution of LH and FSH isoforms during the follicular phase of the bovine estrous cycle prior to the preovulatory surges of LH and FSH. On Day 16 of the estrous cycle (Day 0 = estrus), intact controls (CONT; n = 4) were treated with prostaglandin F2α (PGF2α) to induce luteal regression and initiation of the follicular phase. Other cows were also treated with PGF2α and either ovariectomized (OVX; n = 5) or ovariectomized and given E2 implants (OVXE; n = 6) to mimic the pattern of increasing E2 concentrations during the follicular phase of the estrous cycle. Pituitaries were collected 40 h after treatment with PGF2α, or ovariectomy (0 h). Aliquots of pituitary extracts were chromatofocused on pH 10.5-4.0 gradients. The LH resolved into thirteen isoforms (designated A-L and S, beginning with the most basic form) while FSH resolved into nine isoforms (designated I-IX, beginning with the most basic form). The percentage of LH as isoform F (elution pH = 9.32 + 0.01) was greater (p \u3c 0.05) in the OVX group (48.5%) than in the OVXE group (45.0% ). LH isoforms I (elution pH = 6.98 ± 0.01) and J (elution pH = 6.48 ± 0.01) were more abundant (p \u3c 0.05) in cows from the OVXE (2.3 and 5.8%, respectively) than the OVX group (1.4 and 3.7%, respectively). Distribution of LH isoforms in cows from the three groups did not differ (p \u3e 0.10). Distribution of FSH isoforms were similar (p \u3e 0.05) among all groups. In summary, removal of the ovary (OVX) resulted in a slight increase in percentage of the basic LH isoform F, while removal of the ovary and administration of E2 (OVXE) in a pattern that mimicked increasing concentrations of E2 during the follicular phase of the estrous cycle resulted in a slight increase in the percentage of acidic LH isoforms (I and J). There was no influence of ovariectomy or treatment with E2 on distribution of FSH isoforms in the pituitary. Thus, gonadotropin heterogeneity does not appear to change significantly during the follicular phase of the bovine estrous cycle

Genetic influences on cost-efficient organization of human cortical functional networks

The human cerebral cortex is a complex network of functionally specialized regions interconnected by axonal fibers, but the organizational principles underlying cortical connectivity remain unknown. Here, we report evidence that one such principle for functional cortical networks involves finding a balance between maximizing communication efficiency and minimizing connection cost, referred to as optimization of network cost-efficiency. We measured spontaneous fluctuations of the blood oxygenation level-dependent signal using functional magnetic resonance imaging in healthy monozygotic (16 pairs) and dizygotic (13 pairs) twins and characterized cost-efficient properties of brain network functional connectivity between 1041 distinct cortical regions. At the global network level, 60% of the interindividual variance in cost-efficiency of cortical functional networks was attributable to additive genetic effects. Regionally, significant genetic effects were observed throughout the cortex in a largely bilateral pattern, including bilateral posterior cingulate and medial prefrontal cortices, dorsolateral prefrontal and superior parietal cortices, and lateral temporal and inferomedial occipital regions. Genetic effects were stronger for cost-efficiency than for other metrics considered, and were more clearly significant in functional networks operating in the 0.09–0.18 Hz frequency interval than at higher or lower frequencies. These findings are consistent with the hypothesis that brain networks evolved to satisfy competitive selection criteria of maximizing efficiency and minimizing cost, and that optimization of network cost-efficiency represents an important principle for the brain's functional organization

Визуальная навигация автономно летящего БПЛА с целью его возвращения в точку старта

An autonomous visual navigation algorithm is considered, designed for “home“ return of unmanned aerial vehicle (UAV) equipped with on-board video camera and on-board computer, out of GPS and GLONASS navigation signals. The proposed algorithm is similar to the well-known visual navigation algorithms such as V-SLAM (simultaneous localization and mapping) and visual odometry, however, it differs in separate implementation of mapping and localization processes. It calculates the geographical coordinates of the features on the frames taken by on-board video camera during the flight from the start point until the moment of GPS and GLONASS signals loss. After the loss of the signal the return mission is launched, which provides estimation of the position of UAV relatively the map created by previously found features. Proposed approach does not require such complex calculations as V-SLAM and does not accumulate errors over time, in contrast to visual odometry and traditional methods of inertial navigation. The algorithm was implemented and tested with use of DJI Phantom 3 Pro quadcopter.Рассматривается алгоритм автономной визуальной навигации, предназначенный для возвращения в точку старта беспилотного летательного аппарата (БПЛА), оборудованного одной бортовой видеокамерой и бортовым вычислителем, без использования навигационных сигналов GPS и ГЛОНАСС. Предлагаемый алгоритм схож с широко известными алгоритмами визуальной навигации, такими как одновременная локализация и картографирование (V-SLAM) и визуальная одометрия, однако отличается от них раздельным выполнением процессов картографирования и локализации. Он вычисляет географические координаты признаков, найденных на кадрах, снятых бортовой видеокамерой при полете от точки старта до потери сигналов GPS и ГЛОНАСС. После потери сигнала запускается миссия возвращения и вычисляется лишь положение БПЛА относительно построенной на основе найденных ранее признаков карты, которая используется для возвращения в точку старта. Предложенный подход не требует таких сложных вычислений, как V-SLAM, и не накапливает со временем ошибки в отличие от визуальной одометрии и традиционных методов инерциальной навигации. Алгоритм был реализован и протестирован с помощью квадрокоптера DJI Phantom 3 Pro

Two-Loop Renormalization Group Analysis of the Burgers-Kardar-Parisi-Zhang Equation

A systematic analysis of the Burgers--Kardar--Parisi--Zhang equation in $d+1$ dimensions by dynamic renormalization group theory is described. The fixed points and exponents are calculated to two--loop order. We use the dimensional regularization scheme, carefully keeping the full $d$ dependence originating from the angular parts of the loop integrals. For dimensions less than $d_c=2$ we find a strong--coupling fixed point, which diverges at $d=2$, indicating that there is non--perturbative strong--coupling behavior for all $d \geq 2$. At $d=1$ our method yields the identical fixed point as in the one--loop approximation, and the two--loop contributions to the scaling functions are non--singular. For $d>2$ dimensions, there is no finite strong--coupling fixed point. In the framework of a $2+\epsilon$ expansion, we find the dynamic exponent corresponding to the unstable fixed point, which describes the non--equilibrium roughening transition, to be $z = 2 + {\cal O} (\epsilon^3)$, in agreement with a recent scaling argument by Doty and Kosterlitz. Similarly, our result for the correlation length exponent at the transition is $1/\nu = \epsilon + {\cal O} (\epsilon^3)$. For the smooth phase, some aspects of the crossover from Gaussian to critical behavior are discussed.Comment: 24 pages, written in LaTeX, 8 figures appended as postscript, EF/UCT--94/3, to be published in Phys. Rev. E

The bounds of education in the human brain connectome

Inter-individual heterogeneity is evident in aging; education level is known to contribute for this heterogeneity. Using a cross-sectional study design and network inference applied to resting-state fMRI data, we show that aging was associated with decreased functional connectivity in a large cortical network. On the other hand, education level, as measured by years of formal education, produced an opposite effect on the long-term. These results demonstrate the increased brain efficiency in individuals with higher education level that may mitigate the impact of age on brain functional connectivity.This work was funded by the European Commission (FP7): “SwitchBox” (Contract HEALTH-F2-2010-259772) and co-financed by the Portuguese North Regional Operational Program (ON.2 – O Novo Norte) under the National Strategic Reference Framework (QREN), through the European Regional Development Fund (FEDER). José M. Soares, Paulo Marques, and Nadine C. Santos are supported by fellowships of the project “SwitchBox”; Ricardo Magalhães is supported by a fellowship from the project FCT ANR/NEU-OSD/0258/2012 funded by FCT/MEC (www.fct.pt) and by ON.2 – ONOVONORTE – North Portugal Regional Operational Programme 2007/2013, of the National Strategic Reference Framework (NSRF) 2007/2013, through FEDER