Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Data-Driven Fuzzy Algorithmic Volatility Forecasting Models with Applications to Algorithmic Trading

The explosion of algorithmic trading has been one of the most prominent trends in the finance industry. In this paper, two strategies for algorithmic trading such as Bollinger bands and the simple moving average (SMA) crossover strategy are studied in the fuzzy settings. The commonly used Bollinger bands trading strategy assumes that the difference between an asset’s price and its SMA is normally distributed. However, it is shown that a data-driven t distribution is more appropriate to model the difference between an asset’s price and its SMA. A novel data-driven fuzzy Bollinger bands strategy is proposed for algo trading. A good strategy should have a good algo return on investment with low algo volatility. Therefore, forecasting algo volatility and identifying an appropriate distribution of algo returns play a crucial role in algo trading. Sharpe Ratio (SR) is a measure of average algo return earned in excess of the risk-free rate per unit of algo volatility. For a class of SMA crossover strategies with varying window sizes, fuzzy estimates of SR are computed based on various risk measures including the data-driven volatility estimate (DDVE). SR fuzzy forecasts are computed using two recently proposed volatility forecasting models such as data-driven exponentially weighted moving average (DD-EWMA) and data-driven neuro volatility models. The main reason of using the fuzzy approach is to provide α-cuts (interval forecasts) of the SR. An empirical application on a set of widely traded technology stocks shows that the proposed models deliver forecasts of SR with small errors.
Mathematical Model and Experimental Design of Nanocomposite Proximity Sensors

A mathematical model of fringe capacitance for a nano-based proximity sensor, which takes the presence of different resistivities into account, is developed. An analytical solution obtained for a rectangular-shape sensor with applying of Gauss, Conversation of Charge and Ohm laws into Laplace’s equation <inline-formula> <tex-math notation="LaTeX">$\nabla ^{2}V\left ({x,y,z,t }\right)=0$ </tex-math></inline-formula> gives the electric potential distribution by which the fringe capacitance in a 2D domain area can be calculated. The calculated capacitance evidently decreases drastically due to the fringe phenomena while object moves toward the polymeric sensor. The model also asserts that the change of capacitance is under a noticeable influence of sensor resistivity, particularly in the range of <inline-formula> <tex-math notation="LaTeX">$10^{3}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$10^{5} \Omega $ </tex-math></inline-formula>. m, the initial capacitance varies from 0.045pF to 0.024 pF. The fabricated flexible nanocomposite sensors, Thermoplastic Polyurethane (TPU) reinforced by 1wt. % Carbon Nanotubes (CNTs) having resistivity <inline-formula> <tex-math notation="LaTeX">$10^{5} \Omega $ </tex-math></inline-formula>. m, are capable of detecting presence of an external object in a wide range of distance and indicating remarkable correlation with the mathematical solution. Our proximity sensor fabrication is straightforward and relatively simple. An unprecedented detection range of measurement reveals promising ability of this proximity sensor in applications of motion analysis and healthcare systems.
Prefrontal Cortical Activation While Doing Mindfulness Task: a Pilot Functional Near-Infrared Spectroscopy Study

Mindfulness interventions are increasingly used in clinical settings. Neurophysiological mechanisms underlying mindfulness offer objective evidence that can help us evaluate the efficacy of mindfulness. Recent advances in technology have facilitated the use of functional Near-Infrared Spectroscopy (fNIRS) as a light weight, portable, and relatively lower cost neuroimaging device as compared to functional Magnetic Resonance Imaging (fMRI). In contrast to numerous fMRI studies, there are scanty investigations using fNIRS to study mindfulness. Hence, this study was done to investigate the feasibility of using a continuous-wave multichannel fNIRS system to study cerebral cortex activations on a mindfulness task versus a baseline task. NIRS data from 14 healthy Asian subjects were collected. A statistical parametric mapping toolbox specific for statistical analysis of NIRS signal called NIRS_SPM was used to study the activations. The results from group analysis performed on the contrast of the mindfulness versus baseline tasks showed foci of activations on the left and central parts of the prefrontal cortex. The findings are consistent with prevailing fMRI studies and show promise of using fNIRS system for studying real-time neurophysiological cortical activations during mindfulness practice.
EEG Signal Classification Using Convolutional Neural Networks on Combined Spatial and Temporal Dimensions for BCI Systems

EEG signal classification is an important task to build an accurate Brain Computer Interface (BCI) system. Many machine learning and deep learning approaches have been used to classify EEG signals. Besides, many studies have involved the time and frequency domain features to classify EEG signals. On the other hand, a very limited number of studies combine the spatial and temporal dimensions of the EEG signal. Brain dynamics are very complex across different mental tasks, thus it is difficult to design efficient algorithms with features based on prior knowledge. Therefore, in this study, we utilized the 2D AlexNet Convolutional Neural Network (CNN) to learn EEG features across different mental tasks without prior knowledge. First, this study adds spatial and temporal dimensions of EEG signals to a 2D EEG topographic map. Second, topographic maps at different time indices were cascaded to populate a 2D image for a given time window. Finally, the topographic maps enabled the AlexNet to learn features from the spatial and temporal dimensions of the brain signals. The classification performance was obtained by the proposed method on a multiclass dataset from BCI Competition IV dataset 2a. The proposed system obtained an average classification accuracy of 81.09%, outperforming the previous state-of-the-art methods by a margin of 4% for the same dataset. The results showed that converting the EEG classification problem from a (1D) time series to a (2D) image classification problem improves the classification accuracy for BCI systems. Also, our EEG topographic maps enabled CNN to learn subtle features from spatial and temporal dimensions, which better represent mental tasks than individual time or frequency domain features.
Multi-Tissue Cancer Classification of Gene Expressions using Deep Learning

Cancer classification using gene expressions is extremely challenging given the complexity and high dimensionality of the data. Current classification methods typically rely on samples collected from a single tissue type and perform a prerequisite of gene feature selection to avoid processing the full set of genes. These methods fall short in taking advantage of genome-wide Next Generation Sequencing technologies that provide a snapshot of the whole transcriptome rather than a predetermined subset of genes. We propose a deep learning framework for cancer diagnosis by developing a multi-tissue cancer classifier based on whole-transcriptome gene expressions collected from multiple tumor types. We introduce a new Convolutional Neural Network architecture specifically designed to address the complex nature of whole-transcriptome gene expressions with capabilities of detecting genetic alterations driving cancer progression by learning genomic signatures across multiple tissue types without requiring the prerequisite of gene feature selection. Our model achieves 98.9% classification accuracy on human samples representing 33 different cancer tumor types across 26 organ sites.
Optimal Filtering of Single Channel EEG Data using Linear Filters

Electroencephalogram (EEG) is a flexible diagnostic tool used to study the underlying neural activities of the brain. However, these EEG datasets are often contaminated by artefacts that reduce the quality of these acquired bio-signals and are difficult to remove and cause misinterpretation of the underlying neural information. Moreover, no universally applicable denoising technique exists nor is there any set standard for comparison of denoising methods. A method to compare denoising methods based on Signal to Noise Ratio (SNR) and Cross-Correlation analysis using an ideally filtered signal was proposed in this paper. The ideally filtered signal was achieved using Blind Source Separation (BSS) technique. BSS techniques are computationally intensive, require greater number of EEG channels and require manual supervision for appreciable performance. Thus, in order to achieve comparable denoising performance on single-channel EEG data simple universally applicable linear filters were used. Filter order optimization was performed for various linear filters for maximal SNR performance. Signals filtered using linear filters were compared to BSS filtered signal using Cross-Correlation Analysis. It is seen that for most filters, their performances correlates to that of the optimal signal achieved by BSS, at their least orders. hereby overcoming the limitations of BSS.
Virtual Full-Duplex Relaying in a Buffer-Aided Multi-Hop Cooperative Network

In this paper, we propose a virtual full-duplex (VFD) multi-hop cooperative relaying network using buffer-aided halfduplex (HD) relays. Firstly, for a given multi-hop network, the links are partitioned into three groups and then information is transmitted over a single time slot using two selected best links, which enhances the network coverage and reliability of the system. The Markov chain approach is used to analyze the state transition matrix which models the evolution of buffer states. An analytical expression of steady state probability is obtained, using which the outage probability of the system is evaluated. Numerical results validate our analytical findings and it shows that the proposed relaying scheme offers a better outage performance as compared to that of the conventional buffer-aided max-link relay selection scheme in a multi-hop communication system.
Pulse Oximetry at the Wrist During Sleep: Performance, Challenges and Perspectives

The state-of-the-art non-invasive measurement of peripheral oxygen saturation (SpO2) during sleep is mainly based on pulse oximetry at the fingertip. Although this approach is noninvasive, it can still be obtrusive and cumbersome to apply, in particular for ambulatory monitoring over several nights.We developed a wrist-worn reflectance pulse oximetry device which can be embedded in a watch, making it less obtrusive and easy to apply. This device was tested in an ongoing clinical study on 57 subjects (33 patients and 24 healthy volunteers) undergoing a full overnight polysomnography recording. The accuracy was evaluated against state-of-the-art fingertip SpO2 measurements.In the 54 subjects available for analysis we obtained an SpO2 accuracy (ARMS) of 3.4 % when automatically rejecting 17.7 % of signals due to low quality. When further excluding measurements suffering from insufficient contact of the watch with the skin an ARMS of 2.7 % was obtained while rejecting a total of 23.2 % measurements. These accuracies comply with the ISO standard and the FDA guidance for pulse oximeters.The present results are promising and pave the way for unobtrusive and continuous monitoring of SpO2 to screen for sleep disordered breathing. Nonetheless, contact pressure and venous blood have shown to adversely affect the SpO2 estimation and remain a challenge for wrist-based reflectance pulse oximetry.
Different glucose metabolic brain networks between Subjective Cognitive Decline and Health Control based on graph theory*

Recently, more evidences manifest that the subjective cognitive decline (SCD) of unimpaired individual may represent first symptom of Alzheimer’s disease (AD). This study investigated the differences of intrinsic glucose metabolic functional connectivity between SCD and healthy subject (HC) groups from the perspective of brain network topology. In this study we attained 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) scans from Xuanwu Hospital, Beijing, China, including 85 SCD subjects (male = 16, mean age = 66, MMSE = 28.4) and 74 HC subjects (male = 37, mean age = 65,MMSE=29.0). Graph theory method has been used in this study. Network parameters, including global efficiency, local efficiency, characteristic path length, clustering coefficient, betweenness centrality, sigma and modularity were calculated and compared between two groups. As a result, both SCD and HC groups showed the small-world property. Meanwhile, SCD showed loss of small-world properties, for example, sigma in SCD was significantly lower than HC (p<0.05). In addition, the clustering coefficient and local efficiency of SCD were both higher than HC significantly (p<0.05). In contrast, the characteristic path length and global efficiency of SCD were lower than HC, which led to the regularization of brain network in SCD group. Furthermore, we found global modularity of SCD was lower than HC and the number of modules also decreased. Our findings suggested that there exist differences in glucose metabolic brain network between two groups, demonstrating that the graph theory analysis method could be useful and helpful to predict risks in the preclinical stage of AD.
Selection of Plantar-Pressure and Ankle-Acceleration Features for Freezing of Gait Detection in Parkinson's Disease using Minimum-Redundancy Maximum-Relevance

Freezing of gait (FOG) is a major hindrance to daily mobility and can lead to falling in people with Parkinson's disease. While wearable accelerometers and gyroscopes have been commonly used for FOG detection, foot plantar pressure distribution could also be considered for this application, given its usefulness in previous gait-based classification. This research examined 325 plantar-pressure based features and 132 acceleration-based features extracted from the walking data of five males with Parkinson's disease who experienced FOG. A set of 61 features calculated from the time domain, Fast Fourier transform (FFT), and wavelet transform (WT) were extracted from multiple input signals; including, total ground reaction force, foot centre of pressure (COP) position, COP velocity, COP acceleration, and 3D ankle acceleration. Minimum-redundancy maximum relevance (mRMR) feature selection was used to rank all features. Plantar-pressure based features accounted for 4 of the top 5 features (ranks 2, 3, 4, 5); the remaining feature was an ankle acceleration based feature (rank 1). The three highest ranked features were the freeze index (calculated from ankle acceleration), total power in the frequency domain (calculated using the FFT from COP velocity), and mean of the WT detail coefficients (calculated from COP velocity). This preliminary analysis demonstrated that features calculated from plantar pressure, specifically COP velocity, performed comparably to ankle acceleration features. Thus, feature sets for FOG detection may benefit from plantar-pressure based features.
Wind farm noise suppresses territorial defense behavior in a songbird

In birds, anthropogenic noise has been linked to reduced breeding densities and success, but the mechanisms by which this occurs are currently unclear. In this study, we investigated whether wind turbine noise, an increasingly common source of anthropogenic noise in remote and rural locations, affects territory defense in a songbird, the European robin (Erithacus rubecula). We showed that robins increase low-frequency song elements in response to territorial intrusion under quiet conditions but that this response did not occur in the presence of wind turbine noise. These results are particularly interesting in light of previous work implicating low-frequency song in aggression, perhaps as a signal of body size. Thus, anthropogenic noise may affect their ability to deter an intruder, leading to expenditure of extra time and energy, to increased risks of injury and, as a consequence, reduced breeding success. Our study contributes to understanding the impact of anthropogenic noise on birds by linking disruptive noise and territorial behavior, which may be a mechanism underlying the reduction in bird breeding densities and success that has been reported in noisy areas.
Near Infrared and Aquaphotomic analysis of water absorption in lactate containing media

Increased concentrations of lactate levels in blood are often seen in patients with life-threatening cellular hypoperfusion or infections. State-of-the-art techniques used in clinical practice for measuring serum lactate concentrations rely on intermittent blood sampling and do not permit continuous monitoring of this all important parameter in critical care environments.In recent years, Near Infrared (NIR) Spectroscopy has been established as a possible alternative to existing methods that can mitigate these constraints and be used for non-invasive continuous monitoring of lactate. Nevertheless, the dominant absorption of -OH overtone bands of water in the NIR presents a challenge and complicates the accurate detection of other absorbers such as lactate. For this reason, comprehensive analysis of the -OH overtone bands with systematic lactate concentration changes is essential. This paper reports on the analysis of NIR spectra of two aqueous systems of varying concentrations of lactate in saline and whole blood using the principles of Aquaphotomics.The results show distinctive conformational and structural differences in lactate-water binding, which arise due to the molecular interactions of bonds present in respective solvents.
Differentiating Motor Coordination and Position Sense in Children with Cerebral Palsy and Typically Developing Populations Through Robotic Assessments*

Motor function and coordination improve as children age. Robotic assessments of motor function and coordination have been shown to be repeatable, objective, and accurate. Additionally, robotic assessments have been used to measure and quantify deficits in motor function and coordination in children with cerebral palsy (CP). Normative models of motor function and coordination based on age have not been used widely to differentiate impaired performance from typical performance. This study presents preliminary results of identifying deficits in motor function and coordination assessed with a robotic reaching task and using a normative model of typical performance that accounts for age, sex, and handedness. The models were compared with data from three participants with CP to evaluate whether the models could be used to identify deficits in motor function. The models indicated motor deficits in one participant when performing a visually guided reaching task with respect to initial speed and distance ratios. There was no evidence of motor control deficits in the other two participants. Future work will refine the models to be able to better identify and quantify motor control impairments with the potential to target therapy around quantifiable goals.
Diabetic Peripheral Neuropathy Risk Assessment using Digital Fundus Photographs and Machine Learning

In this work, we demonstrate a novel approach to assessing the risk of Diabetic Peripheral Neuropathy (DPN) using only the retinal images of the patients. Our methodology consists of convolutional neural network feature extraction, dimensionality reduction and feature selection with random projections, combination of image features to case-level representations, and the training and testing of a support vector machine classifier. Using clinical diagnosis as ground truth for DPN, we achieve an overall accuracy of 89% on a held-out test set, with sensitivity reaching 78% and specificity reaching 95%.
Altered Proprioceptive Feedback Influences Movement Kinematics in a Lifting Task

Movement control process can be considered to take place on at least two different levels: a high, more cognitive level and a low, sensorimotor level. On a high level processing a motor command is planned accordingly to the desired goal and the sensory afference, mainly proprioception, is used to determine the necessary adjustments in order to minimize any discrepancy between predicted and executed action. On a lower level processing, the proprioceptive feedback later employed in high level regulations, is generated by Ia sensory fibers positioned in muscle main proprioceptors: muscle spindles. By entraining the activity of these spindle fibers through 80Hz vibration of triceps distal tendon, we show the intriguing possibility of inducing kinematics adjustments due to negative feedback corrections, during a lifting task.
The effect of Nasonov based dispensers on honey bee behavior and on pollination in blueberry, apple, and cherry

THE EFFECT OF NASONOV BASED DISPENSERS ON HONEY BEE BEHAVIOR AND ON POLLINATION IN BLUEBERRY, APPLE, AND CHERRY By Julie Anna Adams Honey bee attractants are not novel, but the time is right to continue to investigate their importance in the growing fruit industry. Due to pollinator declines, potential honey bee attractants have been used for the purpose of improving and increasing pollination services by bees. PolynateTM is a potential product manufactured for this purpose that contains a synthetic mixture similar to that of the Nasonov pheromone in honey bees. In this thesis, two main objectives are investigated: 1) to determine the ability of Nasonov based dispensers to increase fruit set and visitation in blueberries, apples and cherries and 2) to quantify behavioral changes associated with dispenser application. The first objective was accomplished by assessing the differences in forager visitation rates and fruit yield in crops treated and untreated with Nasonov based dispensers. The second objective was accomplished by directly observing the responses of honey bees in a hoop house when exposed to Nasonov based dispensers on artificial feeders. Dispensers did not increase fruit set or forager activity in blueberries, apples or cherries. A half rate of Polynate also did not cause a difference between treated and untreated areas. Given a choice between two identical feeders, one treated with a dispenser and one not, the honey bees had no visible preference. Copyright by JULIE ANNA ADAMS 2014
Ultrasonic Hydrogel Biochemical Sensor System

In this work, we present a proof-of-concept hydrogel-based sensor system capable of wireless biochemical sensing through measuring backscattered ultrasound. The system consists of silica-nanoparticle embedded hydrogel deposited on a thin glass substrate, presenting two interfaces for backscattering (tissue/hydrogel and hydrogel/glass), which allows for system output to be invariant under the change in acoustic properties (e.g. attenuation, reflection) of the intervening biological tissue. We characterize the effect of silica nanoparticles (acoustic contrast agents) loading on the hydrogel’s swelling ratio and its ultrasonic backscattering properties. We demonstrate a wireless pH measurement using dual modes of interrogations, reflection ratio and time delay. The ultrasonic hydrogel pH sensor is demonstrated with a sensing resolution of 0.2 pH level change with a wireless sensing distance around 10 cm.