Accuracy and precision in super-resolution MRI: Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution

Diffusion MRI (dMRI) is a useful probe of tissue microstructure but suffers from low signal-to-noise ratio (SNR) whenever high resolution and/or high diffusion encoding strengths are used. Low SNR leads not only to poor precision but also poor accuracy of the diffusion-weighted signal, as the rectified noise floor gives rise to a positive signal bias. Recently, super-resolution techniques have been proposed for signal acquisition at a low spatial resolution but high SNR, whereafter a higher spatial resolution is recovered by image reconstruction. In this work, we describe a super-resolution reconstruction framework for dMRI and investigate its performance with respect to signal accuracy and precision. Using strictly controlled phantom experiments, we show that the super-resolution approach improves accuracy by facilitating a more beneficial trade-off between spatial resolution and diffusion encoding strength before the noise floor affects the signal. Moreover, precision is shown to have a less straightforward dependency on acquisition, reconstruction, and intrinsic tissue parameters. Indeed, we find that a gain in precision from super-resolution reconstruction (SRR) is substantial only when some spatial resolution is sacrificed. We also demonstrated the value of SRR in the challenging combination of high resolution and spherical b-tensor encoding at ultrahigh b-values -- a configuration that produces a unique contrast that emphasizes tissue in which diffusion is restricted in all directions. We conclude that SRR is most valuable in low-SNR conditions, where it can suppress rectified noise floor effects and recover signal with high accuracy. The in vivo application showcases a vastly superior image contrast when using SRR compared to conventional imaging, facilitating investigations of brain tissue that would otherwise have prohibitively low SNR, resolution or required non-conventional MRI hardware.

Evolution of honest reward signal in flowers

Some flowering plants signal the abundance of their rewards by changing their flower colour, scent or other floral traits as rewards are depleted. These floral trait changes can be regarded as honest signals of reward states for pollinators. Previous studies have hypothesized that these signals are used to maintain plant-level attractiveness to pollinators, but the evolutionary conditions leading to the development of honest signals have not been well investigated from a theoretical basis. We examined conditions leading to the evolution of honest reward signals in flowers by applying a theoretical model that included pollinator response and signal accuracy. We assumed that pollinators learn floral traits and plant locations in association with reward states and use this information to decide which flowers to visit. While manipulating the level of associative learning, we investigated optimal flower longevity, the proportion of reward and rewardless flowers, and honest- and dishonest-signalling strategies. We found that honest signals are evolutionarily stable only when flowers are visited by pollinators with both high and low learning abilities. These findings imply that behavioural variation in learning within a pollinator community can lead to the evolution of an honest signal even when there is no contribution of rewardless flowers to pollinator attractiveness.

PREREQUISITES FOR THE CREATION OF A CO-HERENT PUBLIC COMMUNICATION NETWORK – THE BASIS OF END-TO-END DIGITAL TECHNOLOGIES

This article is a logical continuation and concretization of the authors’ work [1]. Dedicated to the consid-eration of the prerequisites and possibilities of creating a coher-ent public communication network in the interests of end-to-end digital technologies. Justification and use of the core of the time-frequency support of the fixed network as a basis for the syn-chronization system of 5G and 6G standards communication networks. Based on the ITU-T Recommendations, an analysis was carried out on the current state of the primary reference sources of frequency and time (Primary Reference Timing and Clock – PRTC), the basic requirements for them in terms of fre-quency and time signal accuracy, the possibility of implementing promising PRTC and enhanced PRTC – ePRTC on domestic equipment. Precision characteristics of frequency and time sig-nals on the network sections from the sources of the State Time and Frequency Service to the ePRTC, between the PRTC core of the backbone network and the wireless communication network synchronization system. The norms for the permissible errors of the main nodes of the network elements are given, their feasibil-ity is shown.

Equilibrium Selection under the Bayes-Based Strategy Updating Rules

In this paper, first, an evolutionary game model for Bayes-based strategy updating rules was constructed, in which players can only observe a signal that reveals a strategy type instead of the strategy type directly, which deviates from the strategy type of players. Then, the equilibrium selection of populations in the case of the asymmetric game, the Battle of the Sexes (BoS), and the case of a symmetric coordination game was studied where individuals make decisions based on the signals released by each player. Finally, it was concluded that in the BoS game, when the accuracy of the signal is low, the population eventually reaches an incompatible state. If the accuracy of the signal is improved, the population finally reaches a coordinated state. In a coordination game, when the accuracy of the signal is low, the population will eventually choose a payoff-dominated equilibrium. With the improvement of signal accuracy, the equilibrium of the final selection of the population depends on its initial state.

Better-than-chance classification for signal detection

Summary The estimated accuracy of a classifier is a random quantity with variability. A common practice in supervised machine learning, is thus to test if the estimated accuracy is significantly better than chance level. This method of signal detection is particularly popular in neuroimaging and genetics. We provide evidence that using a classifier’s accuracy as a test statistic can be an underpowered strategy for finding differences between populations, compared to a bona fide statistical test. It is also computationally more demanding than a statistical test. Via simulation, we compare test statistics that are based on classification accuracy, to others based on multivariate test statistics. We find that the probability of detecting differences between two distributions is lower for accuracy-based statistics. We examine several candidate causes for the low power of accuracy-tests. These causes include: the discrete nature of the accuracy-test statistic, the type of signal accuracy-tests are designed to detect, their inefficient use of the data, and their suboptimal regularization. When the purpose of the analysis is the evaluation of a particular classifier, not signal detection, we suggest several improvements to increase power. In particular, to replace V-fold cross-validation with the Leave-One-Out Bootstrap.

Adaptive Bat Algorithm Optimization Strategy for Observation Matrix

Bat algorithm, as an optimization strategy of the observation matrix, has been widely used. Observation matrix has a direct impact on the reconstructed signal accuracy as a projection transformation matrix, and it has been widely used in various algorithms. However, for the traditional experimental process, randomly generated observation matrices often result in a larger reconstruction error and unstable reconstruction results. Therefore, it is a challenge to retain more feature information of the original signal and reduce reconstruction error. To obtain a more accurate reconstruction signal and less memory space, it is important to select an effective compression and reconstruction strategy. To solve this problem, an adaptive bat algorithm is proposed to optimize the observation matrix in this paper. For the adaptive bat algorithm, we design a dynamic adjustment strategy of the optimal radius to improve its global convergence ability. The results of our simulation experiments verify that, compared with other algorithms, it can effectively reduce the reconstruction error and has stronger robustness.

Improve performance of the digital sinusoidal generator in FPGA by memory usage optimization

<p>This paper presented the improvement in the performance of the digital sinusoidal signal generator, which was implemented in FPGA, by optimizing the usage of the available memory onboard. The sine wave was generated by using a Lookup Table method, where its pre-calculated values were stored in the onboard memory, and its frequency can be adjustable by changing the incremental step value of the memory address. In this proposed research, the memory stores only 25000 samples of the first quarter from a period of a sine wave and thus, the output signal accuracy was increased and the output frequency range was expanded, compared to the previous research. The proposed design was successfully developed and implemented in ALTERA Cyclone III DE0 FPGA Development Board, and its functionality was validated via functional simulation in Modelsim and also hardware experimental results observation in SignalTap II.</p>

ANALISIS TEKNIKAL SAHAM DENGAN INDIKATOR GABUNGAN WEIGHTED MOVING AVERAGE DAN STOCHASTIC OSCILLATOR

The Stochastic Oscillator which is one of the leading indicators has the disadvantage of opening the gap for false signals. To minimize false signals, the smoothing process is carried out using the Moving Average. Stochastic Oscillator is usually combined with SMA (Simple Moving Average). But SMA has the disadvantage of giving the same weight to all data, even though in reality the data that best reflects the next value is the last data. This makes the basis of weighting the WMA (Weighted Moving Average) method.This study aims to test the combination of Stochastic Oscillator with SMA and WMA and use the best combination to predict the trends that will occur and trading decisions taken from the results of these predictions. The research samples were ANTM, BBRI, and GIAA stocks from November 9 2015 to November 9, 2018.The results show a combination of Stochastic Oscillator and WMA is a better combination of predictions than Stochastic Oscillator and SMA because it has a smaller MSE value. Based on the comparison of signal accuracy based on Overbought and Oversold, the best period of combination of Stochastic Oscillator and WMA is period 25. From the predicted trend that will occur with a combination of Stochastic Oscillator and WMA period 25 a decision is made to buy shares for ANTM shares, sell shares for BBRI shares, and waiting for a buy signal for GIAA shares.Keywords: Stochastic Oscillator, SMA, WMA, Predictions, Trends