An intelligent system for trading signal of cryptocurrency based on market tweets sentiments

The purpose of this study is to examine the efficacy of an online stock trading platform in enhancing the financial literacy of those with limited financial knowledge. To this end, an intelligent system is proposed which utilizes social media sentiment analysis, price tracker systems, and machine learning techniques to generate cryptocurrency trading signals. The system includes a live price visu�alization component for displaying cryptocurrency price data and a prediction function that provides both short-term and long-term trading signals based on the sentiment score of the previous day’s cryptocurrency tweets. Additionally, a method for refining the sentiment model result is outlined. The results illustrate that it is feasible to incorporate the Tweets sentiment of cryptocurrencies into the system for generating reliable trading signals

NUTMEG:Open Source Software for M/EEG Source Reconstruction

Neurodynamic Utility Toolbox for Magnetoencephalo- and Electroencephalography (NUTMEG) is an open-source MATLAB-based toolbox for the analysis and reconstruction of magnetoencephalography/electroencephalography data in source space. NUTMEG includes a variety of options for the user in data import, preprocessing, source reconstruction, and functional connectivity. A group analysis toolbox allows the user to run a variety of inferential statistics on their data in an easy-to-use GUI-driven format. Importantly, NUTMEG features an interactive five-dimensional data visualization platform. A key feature of NUTMEG is the availability of a large menu of interference cancelation and source reconstruction algorithms. Each NUTMEG operation acts as a stand-alone MATLAB function, allowing the package to be easily adaptable and scripted for the more advanced user for interoperability with other software toolboxes. Therefore, NUTMEG enables a wide range of users access to a complete “sensor-to- source-statistics” analysis pipeline

Minimizing the Adverse Effects of Electric Fields in Magnetic Resonance Imaging using Optimized Gradient Encoding and Peripheral Nerve Models

Magnetic Resonance Imaging (MRI) is an important imaging modality in both the clinic and in research. MRI technology has been trending toward increasing field strengths to improve the signal-to-noise ratio of the MR signal and fast excitation/encoding strategies to more flexible target anatomical regions during excitation to reduce the total imaging time. While largely successful, both strategies rely on the application of increasingly strong and rapidly switched magnetic fields: the radio frequency (RF) field for excitation and the gradient field for encoding. The technology for generating these fields (and rapidly switching them) has advanced to the point that we are limited by biological responses to the switching fields. For the gradient field, the electric field generated in the tissue causes peripheral nerve stimulation (PNS) causing mild but bothersome sensations at low levels, up to pain or cardiac malfunction at higher levels. The electric fields created by the much faster time-varying RF cause heat deposition, ultimately denaturing proteins and causing tissue damage. In this thesis, methods are presented to characterize and minimize these two problems associated with the switched magnetic fields in MRI. The deposited RF energy (Specific Absorption Rate, SAR) incurred during shaped excitations can be significantly reduced by optimizing gradient and RF waveforms for inner-volume excitations that allow imaging of a sub-volume of the body without wrapping artifacts. The adverse effects of the switching gradient fields are addressed by designing time-optimal gradient encoding waveforms and by developing a method to predict and characterize PNS using field simulations and a full-body nerve model allowing these critical effects to be addressed at the gradient coil design stage. In the first part, time-optimal gradient trajectories are demonstrated that use the gradient hardware at the maximum available performance. The skeleton of the trajectory is defined by a set of k-space control points. The method optimizes gradient waveforms that traverse the k-space control points in the minimum possible amount of time. By using an analytic representation of the gradients (piece-wise linear), the design process is fast and numerically robust. The resulting trajectories sample k-space efficiently while using the gradient system at maximum performance. Compared to the leading Optimal Control method, the proposed method generates gradient waveforms that are 9.2% shorter. The computation process is ∼100x faster and does not suffer from numerical instabilities such as oscillations. In the second part, a method is developed that jointly optimizes parallel transmission RF and gradient waveforms for fast and robust 3-D inner-volume excitation of the MRI signal in minimal time and with minimal energy deposition. The optimization of the k-space trajectories is based on a small number of shape parameters that are well-suited for joint optimization with the RF waveforms. Within each iteration of the trajectory optimization, a small tip-angle least-squares RF pulse design problem is solved. Using optimized 3-D cross (shells) trajectories, a cube shape (brain shape) region was excited with 3.4% (6.2%) NRMSE in less than 5 ms using a 7 T scanner with 8 Tx channels and a clinical gradient system (Gmax = 40 mT/m, Smax = 150 T/m/s). Incorporation of off-resonance robustness in the pulse design significantly altered the k-space trajectory solutions and improved the practical performance of the pulses. In the final part, a framework is presented that simulates PNS thresholds for realistic gradient coil geometries and thus allows, for the first time, to directly address PNS in the coil design process. The PNS framework consists of an accurate body model for simulation of the induced electric fields, an atlas of peripheral nerves, and a neurodynamic model to predict the nerve responses to imposed electric fields. With this model, measured PNS thresholds of two leg/arm solenoid coils and three commercial actively-shielded MR gradient coils could be reproduced with good accuracy. The proposed method can be used to assess the PNS capability of gradient coils during the design phase, without building expensive prototype coils

How can a recurrent neurodynamic predictive coding model cope with fluctuation in temporal patterns? Robotic experiments on imitative interaction

The current paper examines how a recurrent neural network (RNN) model using a dynamic predictive coding scheme can cope with fluctuations in temporal patterns through generalization in learning. The conjecture driving this present inquiry is that a RNN model with multiple timescales (MTRNN) learns by extracting patterns of change from observed temporal patterns, developing an internal dynamic structure such that variance in initial internal states account for modulations in corresponding observed patterns. We trained a MTRNN with low-dimensional temporal patterns, and assessed performance on an imitation task employing these patterns. Analysis reveals that imitating fluctuated patterns consists in inferring optimal internal states by error regression. The model was then tested through humanoid robotic experiments requiring imitative interaction with human subjects. Results show that spontaneous and lively interaction can be achieved as the model successfully copes with fluctuations naturally occurring in human movement patterns

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis

As a newly emerging field, connectomics has greatly advanced our understanding of the wiring diagram and organizational features of the human brain. Generative modeling-based connectome analysis, in particular, plays a vital role in deciphering the neural mechanisms of cognitive functions in health and dysfunction in diseases. Here we review the foundation and development of major generative modeling approaches for functional magnetic resonance imaging (fMRI) and survey their applications to cognitive or clinical neuroscience problems. We argue that conventional structural and functional connectivity (FC) analysis alone is not sufficient to reveal the complex circuit interactions underlying observed neuroimaging data and should be supplemented with generative modeling-based effective connectivity and simulation, a fruitful practice that we term “mechanistic connectome.” The transformation from descriptive connectome to mechanistic connectome will open up promising avenues to gain mechanistic insights into the delicate operating principles of the human brain and their potential impairments in diseases, which facilitates the development of effective personalized treatments to curb neurological and psychiatric disorders

Superpixel-based Fast Fuzzy C-Means Clustering for Color Image Segmentation

A great number of improved fuzzy c-means (FCM) clustering algorithms have been widely used for grayscale and color image segmentation. However, most of them are time-consuming and unable to provide desired segmentation results for color images due to two reasons. The first one is that the incorporation of local spatial information often causes a high computational complexity due to the repeated distance computation between clustering centers and pixels within a local neighboring window. The other one is that a regular neighboring window usually breaks up the real local spatial structure of images and thus leads to a poor segmentation. In this work, we propose a superpixel-based fast FCM clustering algorithm (SFFCM) that is significantly faster and more robust than state-of-the-art clustering algorithms for color image segmentation. To obtain better local spatial neighborhoods, we firstly define a multiscale morphological gradient reconstruction (MMGR) operation to obtain a superpixel image with accurate contour. In contrast to traditional neighboring window of fixed size and shape, the superpixel image provides better adaptive and irregular local spatial neighborhoods that are helpful for improving color image segmentation. Secondly, based on the obtained superpixel image, the original color image is simplified efficiently and its histogram is computed easily by counting the number of pixels in each region of the superpixel image. Finally, we implement FCM with histogram parameter on the superpixel image to obtain the final segmentation result. Experiments performed on synthetic images and real images demonstrate that the proposed algorithm provides better segmentation results and takes less time than state-of-the-art clustering algorithms for color image segmentation.China Postdoctoral Science Foundation; National Natural Science Foundation of China; National Science Foundation of Shangha