Miracle-dz/UniFAR-FTUs huggingface dataset:
Difference in Network Effects of Pulsatile and Galvanic Stimulation

Biphasic pulsatile stimulation is the present standard for neural prosthetic use, and it is used to understand connectivity and functionality of the brain in brain mapping studies. While pulses have been shown to drive behavioral changes, such as biasing decision making, they have deficits. For example, cochlear implants restore hearing but lack the ability to restore pitch perception. Recent work shows that pulses produce artificial synchrony in networks of neurons and non-linear changes in firing rate with pulse amplitude. Studies also show galvanic stimulation, delivery of current for extended periods of time, produces more naturalistic behavioral responses than pulses. In this paper, we use a winner-take-all decision-making network model to investigate differences between pulsatile and galvanic stimulation at the single neuron and network level while accurately modeling the effects of pulses on neurons for the first time. Results show pulses bias spike timing and make neurons more resistive to natural network inputs than galvanic stimulation at an equivalent current amplitude. Clinical Relevance- This establishes that pulsatile stimulation may disrupt natural spike timing and network-level interactions while certain parameterizations of galvanic stimulation avoid these effects and can drive network firing more naturally.
End-to-end Deep Learning of Polysomnograms for Classification of REM Sleep Behavior Disorder

Rapid eye movement (REM) sleep behavior disorder (RBD) is parasomnia and a prodromal manifestation of Parkinson's disease. The current diagnostic method relies on manual scoring of polysomnograms (PSGs), a procedure that is time and effort intensive, subject to interscorer variability, and requires high level of expertise. Here, we present an automatic and interpretable diagnostic tool for RBD that analyzes PSGs using end-to-end deep neural networks. We optimized hierarchical attention networks in a 5-fold cross validation directly to classify RBD from PSG data recorded in 143 participants with RBD and 147 age-and sex-matched controls. An ensemble model using logistic regression was implemented to fuse decisions from networks trained in various signal combinations. We interpreted the networks using gradient SHAP that attribute relevance of input signals to model decisions. The ensemble model achieved a sensitivity of 91.4 % and a specificity of 86.3 %. Interpretation showed that electroencephalography (EEG) and leg electromyography (EMG) exhibited most patterns with high relevance. This study validates a robust diagnostic tool for RBD and proposes an interpretable and fully automatic framework for end-to-end modeling of other sleep disorders from PSG data. Clinical relevance- This study presents a novel diagnostic tool for RBD that considers neurophysiologic biomarkers in multiple modalities.
Unsupervised Quasi-Silence based Speech Segmentation for Speaker Diarization

This paper presents a computationally efficient and accurate speech segmentation framework suitable for speaker diarization. The proposed approach solves the problem of increased false positive rate in order to compensate for reduced false negative rate during speaker change detection in the existing methods in literature. In this new approach, speaker change point detection is biased around detected quasi-silences, which reduces the severity of the trade-off between the missed detection and false detection rates. Additionally, the computational overhead is reduced due to the fact that the segmentation related processing happens only around the detected quasi-silences as opposed to during the entire speech signal. The change point detection accuracy of the proposed quasi-silence-based method is compared with the WinGrow method from literature that uses Bayesian Information Criterion (BIC) recursively. The results show a considerable improvement in the reduction of false positive rate at the segmentation stage while reducing the computational overhead. The proposed mechanism’s improved accuracy and reduced computation makes it a candidate for real-time speaker diarization especially when run on low-power embedded devices.
A Characteristic of Speaker's Audio in the Model Space Based on Adaptive Frequency Scaling

The separation and extraction of the speaker's personality in the speech is one of the key links to determine the performance of the system in the specific research of speaker identification. In this paper, an adaptive frequency scaling-based identifiable feature in the model space is proposed which is suitable for speaker identification. Firstly, an adaptive filter is designed by introducing F -ratio to increase the frequency resolution of high-discrimination sub-bands based on the non-uniform distribution of speaker information in different frequency bands. The F -ratio is introduced to weight the filters and the feature called Non-Uniform Filter Bank coefficients (NUFBank) is obtained. After that, the Echo State Network (ESN) is used to learn the NUFBank feature sequence to obtain the state trajectory sequence. And then it is used as the representation of the speech signal in the model space for the distance measurement. The proposed characteristic has shown good interpretability and stability.
Classification of Pap-smear cell images using deep convolutional neural network accelerated by hand-crafted features

The classification of cells extracted from Pap-smears is in most cases done using neural network architectures. Nevertheless, the importance of features extracted with digital image processing is also discussed in many related articles. Decision support systems and automated analysis tools of Pap-smears often use these kinds of manually extracted, global features based on clinical expert opinion. In this paper, a solution is introduced where 29 different contextual features are combined with local features learned by a neural network so that it increases classification performance. The weight distribution between the features is also investigated leading to a conclusion that the numerical features are indeed forming an important part of the learning process. Furthermore, extensive testing of the presented methods is done using a dataset annotated by clinical experts. An increase of 3.2% in F1-Score value can be observed when using the combination of contextual and local features. Clinical Relevance — Analysis of images extracted from digital Pap-test using modern machine learning tools is discussed in many scientific papers. The manual classification of the cells can be time-consuming and expensive which requires a high amount of manual labor. Furthermore the result of the manual classification can also be uncertain due to interobserver variability. Considering these, any result that can lead to a more reliable highly accurate classification method is considered valuable in the field of cervical cancer screening.
Detection of Osteoarthritis from Multimodal Hand Data

Osteoarthritis (OA) describes a degenerative joint disorder that is prevalent among older people and typically results in swollen and inflamed joints. The aim of this paper is to develop a method using images, videos and thermal data of 100 patients taken at Keio University Hospital to detect OA in hands. By using hand pose estimation on the video data, joint angles can be calculated and subsequently transformed into feature vectors. For the thermal and RGB images, hand keypoint detectors were trained to identify and crop the appropriate joints within the images. The resulting extracted features are combined and further trained on Support Vector Machines and Convolutional Neural Networks to obtain the final binary classification for each joint. While the proposed method generally shows favorable accuracy and F1-scores on the Proximal (PIP) and Distal Interphalangeal (DIP) joints, the performance on the Metacarpophalangeal (MCP) joints is limited by the low occurrence of affected joints in the dataset. We further compare the different modalities and found that, apart from the combined approach, using video data provides the best results. Clinical Relevance— The proposed method shows promising first results for the usage of visual and thermal data in combination with machine learning in order to detect OA
Variational Inference Data-driven Gait Model for Biped Trajectory Generation

Biped robot trajectory generation is a very complex task for real-world uneven terrain. This work presents the gait model based on data-driven to address the issue of traipse ground conditions. The data-driven approach efficiently extracts valuable information regarding the joint relationship efficiently. However, the models can suffer from the model-bias issue. Therefore, the model bias is addressed by considering the uncertainty into the model itself under Bayesian framework. In addition, the new objective function for training of data-driven model based on the integration variational inference with standard error is proposed. It helps the training algorithm to precisely follow the lower variance data-point along the gait cycle. Lastly, the proposed model is analysed based on the two scenarios: (a) constant speed 1m/s with varying incline, and (b) constant incline 0 degrees with varying speed.
Multi-Class Flat Classification of Lung Diseases Utilizing Deep Learning

Early diagnosis of diseases is very critical for recovery. However, this is not always feasible due to the limited available staff or expensive and inadequate tools as we have witnessed in the recent COVID-19 pandemic. Lung diseases are life-threatening, but fortunately, they can be detected from X-ray images, which are cost-effective approaches. However, they need experts who are sometimes unavailable. Thus, using cutting-edge technology to diagnose diseases automatically and fast is the key solution to saving people’s lives. In this research, deep learning techniques have been utilized to classify several lung diseases in a cost-saving, time-saving, and efficient manner. Examples of lung diseases studied in this research are COVID-19, Lung Opacity, Pneumonia, and Tuberculosis. Several pre-trained deep learning models have been employed for flat multi-class classification of these lung diseases instead of using binary classification to recognize one disease from normal cases, as most state-of-the-art studies carry out. The models’ performance has been evaluated on imbalanced data of X-ray images with various resolutions and types. Finally, multiple measurements metrics have been utilized to evaluate the performance. The best accuracy achieved in this research is 95.643%.
General Relativity: An Introduction for Physicists

1. The spacetime of special relativity 2. Manifolds and coordinates 3. Vector calculus on manifolds 4. Tensor calculus on manifolds 5. Special relativity revisited 6. Electromagnetism 7. The equivalence principle and spacetime curvature 8. The gravitational field equations 9. The Schwarzschild geometry 10. Experimental tests of general relativity 11. Schwarzschild black holes 12. Further spherically-symmetric geometries 13. The Kerr geometry 14. The Friedmann-Robertson-Walker geometry 15. Cosmological models 16. Inflationary cosmology 17. Linearised general relativity 18. Gravitational waves 19. A variational approach to general relativity.
Optimization of Population Mobility Model of Artificial Society Based on Big Data Analytics

It has become a promising research topic that using the Artificial Society (AS) solves social governance problems such as emergency management, environmental protection, and urban planning. The unceasing evolution of the AS requires high demands for the accuracy of the basic models in AS. With the development of the IoT and data science, researchers can refine the models in AS by contrasting the big data harvested from real society. This paper aims to improve the accuracy of the population mobility model in AS based on the analysis of the real-world dataset about urban population mobility and the comparison between the real-world data and the data generated from AS. To this end, firstly, the datasets are processed and analyzed by Spark. To improve the efficiency in this phase, we design the RDD structure for population mobility data and propose a novel query method, named partition traversal method. Then, the population mobility patterns are analyzed and compared, which are extracted from the datasets of real-world and AS, respectively. Based on the diversity between the two patterns, the optimization scheme of the population mobility model is finally proposed by adjusting the workplace selection. This study is an important reference for the modeling and optimization of population mobility in AS.
Deep Learning-based Low-dose Cardiac Gated SPECT: Implementation in Projection Space vs. Image Space

The reduction of radiation exposure in SPECT-MPI is an important research topic. However, lowering the injected activity degrades image quality, thus impacting the diagnostic accuracy of this modality. In this study, we enrolled a total of 335 clinical gated SPECT-MPI images from a dedicated cardiac SPECT scanner acquired in list-mode format. All patients underwent a two-day rest/stress protocol and the obtained gated images were retrospectively used to convert low-dose to standard-dose images in both projection and image spaces. A deep generative adversarial network was employed to predict standard-dose images from 50% low-dose images. The proposed network was evaluated using quantitative metrics, such as the peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), and structural similarity index measure (SSIM). Moreover, a Pearson correlation coefficient analysis was performed on the half-dose and predicted standard-dose images with respect to the reference standard-dose images. The results demonstrated that the highest PSNR (46.30 ± 2.23) and SSIM (0.98 ± 0.01), and the lowest RMSE (1.32 ± 0.54) were obtained from the image space implementation. Pearson analysis showed that the predicted standard-dose images yielded ρ = 0.960 ± 0.011 and ρ = 0.947 ± 0.027 in the image and projection spaces, respectively. Overall, considering the quantitative metrics, the noise was effectively suppressed in the predicted standard-dose images for both implementations. Yet, standard-dose image estimation in the image space resulted in superior quantitative accuracy and image quality.
Simulations of the Radiative Lifetime in Surface Acoustic Wave driven Piezoelectric Semiconductor Devices

The traveling wave potential wells that are generated from a surface acoustic wave in the piezoelectric materials of semiconductor efficiently trap the created electron hole pairs, which is a potential method of the single-photon detectors. In this study, we report on a model that is used to study the correlation between radiative lifetime and periodical potential wells. We devised a Python program to numerically find the eigenstates in 1D periodical potential well system. The Schrödinger equation is solved to obtain electron and hole wave functions that are related to charge distribution. Then, we analyzed the impact of the potential well on the corresponding radiative lifetime of the system.
A Comparison Between a Simple Drag Formula and Experimental Drag Data for Bodies of Revolution

Abstract : A simplified drag formula to predict the drag of a small number of axisymmetric forms is systematically applied to seven series of model forms comprising nearly fifty bodies. Calculations of form (or residual) drag are compared to available experimental data in order to determine the usefulness of the method for predictive purposes. The formula is shown to exhibit very little sensitivity to changes in most body parameters such as the length-of-stern to diameter ratio, nose and tail radii parameters, and the prismatic coefficient. For some parameters, such as length to diameter ratio, the length of bow section to diameter ratio, however, the formula is sometimes able to discriminate bodies having high values of form drag. It is concluded that the simple drag formula may not be reliably used for estimating the relative form drags of bodies of revolution.
Constant-on-time-Controlled Buck Converter using Switching Period Estimator

This paper presents a novel control method for constant on-time (COT) buck converters. The converter employs a high-accuracy switching period estimator in conjunction with a constant on-time generator to stabilize the system, eliminating the need for inductor current sensing or particular ripple injection. The proposed switching period estimator can adaptively adjust the switching frequency according to the load conditions, enabling high-speed output voltage regulation. Since there is no need to sense current nor any offset compensation, system complexity is reduced and efficiency is improved. A 3.3V input, 1.8V output buck converter designed using the proposed control method has been validated for various output current capabilities. The performance shows a maximum output voltage ripple of 20 mV, and recovery fall and rise times of 3.9 μsec and 4.8 μsec, respectively, under a 500 mA load change. The peak efficiency is >95% and the maximum undershoot and overshoot voltages are 49 mV and 57 mV, respectively.
Unknown-length motif discovery methods in environmental monitoring time series

The search for information of interest in massive time series is crucial in many industrial applications. Companies need their data to be analyzed or modeled in real time, which often requires to extract some patterns, also referred as motifs. However, for diverse and ever more signals, human expertise is overwhelmed by time and by huge amount of data. It is the case for environmental monitoring where it is question to detect radiological phenomena from environmental signals. In this paper, we propose an unsupervised and unknown length motif discovery method based on the Matrix Profile with a low computational cost. Its performance is evaluated on a dataset of simulated radiological signals dedicated to environmental monitoring, and compared to a similarity DTW based method and to a classical standard deviation based method. The advantages and drawbacks of each method are highlighted in terms of performance, runtime, accuracy and robustness to different types of noisy signals.
Monte Carlo Simulation of a Preclinical CZT PET System to Investigate Multi-Interaction Events

This work summarizes findings from GATE (GEANT4 Applications for Emission Tomography) simulations with the incorporation of certain experimentally-measured, ‘realistic' parameters in the data analysis. The simulation models a large volume cadmium zinc telluride (CZT) preclinical PET system with sub 9% energy resolution and sub-mm spatial resolution. 2 of the 4 heads of the final design have been constructed, each consisting of a 4x3 matrix of dual-CZT detector modules. Each detector module comprises two 40x40x5 mm3 CZT crystals with 39 anode strips (100 um width, 1mm pitch each) on the top face and 8 cathode strips (4.9 mm width, 5 mm pitch each) on the bottom face. The crystals are stacked in an anode-cathode-cathode-anode configuration to form the detector module. Photons enter in an "edge-on" orientation, traversing a minimum of 40 mm of CZT which increases detection efficiency. A simulation of the system was constructed in GATE, and data were recorded as "hits," which are ideal, non-blurred interactions, to retain multiple interaction event information within the modeled detector modules. A preliminary energy system response function was generated using (a) photo-peak centroid vs FWHM for Ge-68, Ba-133, and Cs-137 point source data to form an energy-dependent normal distribution to randomly blur energy per simulation interaction, and (b) a Ge-68 point source-measured depth corrected energy vs cathode to anode (C/A) ratio energy plot which was used as a mask over a simulated C/A vs energy plot, isolating allowable C/A ratio-energy combinations. The energy resolution of the system measured with the Ge-68 point source was found to be 8.1%. After applying the measured energy system response function, the simulated energy resolution was found to be 6.3%. The extracted average number of interactions per 511 keV photon after application of the various experimental parameters was 1.05.
Beware the Black-Box of Medical Image Generation: an Uncertainty Analysis by the Learned Feature Space

Deep neural networks (DNNs) are the primary driving force for the current development of medical imaging analysis tools and often provide exciting performance on various tasks. However, such results are usually reported on the overall performance of DNNs, such as the Peak signal-to-noise ratio (PSNR) or mean square error (MSE) for imaging generation tasks. As a black-box, DNNs usually produce a relatively stable performance on the same task across multiple training trials, while the learned feature spaces could be significantly different. We believe additional insightful analysis, such as uncertainty analysis of the learned feature space, is equally important, if not more. Through this work, we evaluate the learned feature space of multiple U-Net architectures for image generation tasks using computational analysis and clustering analysis methods. We demonstrate that the learned feature spaces are easily separable between different training trials of the same architecture with the same hyperparameter setting, indicating the models using different criteria for the same tasks. This phenomenon naturally raises the question of which criteria are correct to use. Thus, our work suggests that assessments other than overall performance are needed before applying a DNN model to real-world practice.
Short Term Glucose Prediction in Patients with Type 1 Diabetes Mellitus

Glucose prediction is used in diabetes self-management as it allows to take suitable actions for proper glycemic regulation of the patient. The aim of this work is the short-term personalized glucose prediction in patients with Type 1 diabetes mellitus (T1DM). In this scope, we compared two different models, an autoregressive moving average (ARMA) model and a long short-term memory (LSTM) model for different prediction horizons. The comparison of two models was performed using the evaluation metrics of root mean square error (RMSE) and mean absolute error (MAE). The models were trained and tested in 29 real patients. The results shown that the LSTM model had better performance than ARMA with RMSE 3.13, 6.41 and 8.81 mg/dL and MAE 1.98, 5.06 and 6.47 mg/dL for 5-, 15- and 30-minutes prediction horizon.