Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational Fluid Dynamic Model of Left Atrium to Analyze Hemodynamic Manifestation during Atrial Fibrillation

In this paper, we present a computational fluid dynamic (CFD) model of left atrium (LA) to analyze the manifestation and progression of atrial fibrillation (AF) in terms of hemodynamic metrics. We propose a coupled lumped-CFD (0d-3d) pipeline to model and predict the pulsatile flow and pressure fields of three-dimensional cardiac chamber under the influence of sinus rhythm, high frequency AF (HF-AF) and LA remodeled AF, considering the interactions between the heart and the arterial system through a separately modeled 0d lumped hemodynamic cardiac model. A novel rhythm generator is modeled to generate modulated cardiac chamber compliance and decoupled auricular and ventricular contraction rate to synthesize variation in sinus rhythm and subsequent AF generation. CFD simulation were solved using subject specific CT scan. Systemic and pulmonary flow and pressure along with metrics related to wall shear stress in LA were derived. Left ventricular (LV) hemodynamic parameters associated with global cardio vascular evaluation like ejection fraction, stroke volume, cardiac output, etc. were also generated for all the rhythmic disturbance under consideration. The proposed 0d-3d coupled hemodynamic model of the LA can provide useful insights on the dynamics of AF manifestation and predict vulnerable regions in the cardiac chambers as well as arterial vasculature for probable thrombogenic plaque formation that leads to stroke and infraction, leading to heart failure.
A group field theory for 3d quantum gravity coupled to a scalar field

We present a new group field theory model, generalising the Boulatov model, which incorporates both 3-dimensional gravity and matter coupled to gravity. We show that the Feynman diagram amplitudes of this model are given by Riemannian quantum gravity spin foam amplitudes coupled to a scalar matter field. We briefly discuss the features of this model and its possible generalisations.
White Matter Lesion Segmentation for Multiple Sclerosis Patients implementing deep learning

The aim of this work is to address the problem of White Matter Lesion (WML) segmentation employing Magnetic Resonance Imaging (MRI) images from Multiple Sclerosis (MS) patients through the application of deep learning. A U-net based architecture containing a contrastive path and an expanding path prior to the final pixel-wise classification is implemented. The data are provided by the Ippokratio Radiology Center of Ioannina and include Fluid-Attenuated Inversion Recovery (FLAIR) MRI images from 30 patients in three phases, baseline and two follow ups. The prediction results are quite significant in terms of pixel-wise classification. The implemented deep learning model demonstrates Dice coefficient 0.7292, Precision 75.92% and Recall 70.16% in 2D slices of FLAIR MRI non-skull stripped images.
Narrow Linewidth RLC Resonator Design Using Positive Feedback Loop

This paper presents a simple idea for improving the linewidth or quality factor (or Q-factor) of an RLC resonant circuit using positive feedback loop. This is achieved by considering a second-order resonant circuit which implements a band-pass filter structure (i.e. having a single zero at the origin). This allowed the loop to be closed without affecting the resonance frequency while at the same time the quality factor is modified. The larger the open-loop gain the more the improvement on the quality factor. It is shown that although in principle this approach has no upper limit to how much improvement can be made on the quality factor, it has the practical limitation which is dictated by the op-amp’s gain-bandwidth product used in the feedback loop.
A Highly Pattern-Reconfigurable Planar Antenna with 360° Single-and Multi- Beam Steering

A novel single-fed horizontally polarized pattern-reconfigurable antenna is proposed with the key feature that is highly pattern-reconfigurable. The antenna is able to generate a large variety of radiation patterns including omnidirectional, single-directive and multi-beam patterns without using phase shifters. The antenna is based on a planar geometry which consists of an Alford loop and four compact parasitic pixel rings with 60 integrated controlling units utilizing PIN diodes. Experimental and simulation results are provided for the proposed antenna design operating at 5 GHz. The results show that the single-beam steering with 7.3 dBi gain through 360° in the azimuth plane is achieved. Moreover, flexible dual-beam steering with one beam fixed and the other beam scanning independently among 360° in the azimuth plane is also demonstrated where each beam has 5 dBi gain. Other radiation modes such as omnidirectional mode, tri-beam steering mode and elevation plane steering mode are also verified. The features of high pattern reconfigurability, 360° single and multi-beam steering ability, full 3D space scanning, planar geometry and compatibility with a digital controller make it useful in various sub-6 GHz wireless application such as wireless power transfer, RF sensing and analog precoding.
Recent advances in chemical and magnetic imaging of surfaces and interfaces by XPEEM

Synchrotron-based photoemission electron microscopy (XPEEM) is one of the most powerful spectro-microscopic techniques for the investigation of surfaces, interfaces, thin films and buried layers. By exploiting the tunability and polarizability of x-ray sources as well as progress in electron optics design, modern XPEEM instruments can perform several x-ray spectroscopic investigations with a lateral resolution of a few tens of nanometres. We review here the latest developments in XPEEM, illustrating the state of the art capabilities of the technique. The usefulness of chemical and magnetic imaging XPEEM methods is demonstrated by examples of fundamental and applied studies in surface and material sciences, as well as other fields of application ranging from magnetism to biology and geology.
A Deep Graph Cut Model For 3D Brain Tumor Segmentation

Brain tumor segmentation plays a key role in tumor diagnosis and surgical planning. In this paper, we propose a solution to the 3D brain tumor segmentation problem using deep learning and graph cut from the MRI data. In particular, the probability maps of a voxel to belong to the object (tumor) and background classes from the UNet are used to improve the energy function of the graph cut. We derive new expressions for the data term, the region term and the weight factor balancing the data term and the region term for individual voxels in our proposed model. We validate the performance of our model on the publicly available BRATS 2018 dataset. Our segmentation accuracy with a dice similarity score of 0.92 is found to be higher than that of the graph cut and the UNet applied in isolation as well as over a number of state of the art approaches.
COVID-19 Vaccine Infodemiology using Unsupervised Natural Language Processing

COVID-19 pandemic has resulted in excess mortality globally and presented an unprecedented challenge to people's lives. Despite the benefits of getting a COVID-19 vaccine, there have been arguments against the available vaccines and vaccine hesitancy worldwide. In this work, we analyze the information published by the public on Reddit as a digital forum, using unsupervised natural language processing to discover useful insights from the collected data related to COVID-19 vaccines, and validate the results of our study using Google Trends. Our results show that the government's contributions to the vaccination process, vaccine side-effects, and opposition to vaccine mandate and lock-downs are the main concerns shared by the public on digital forums. Moreover, we provide our collected data publicly available for further infodemiology studies by researchers and practitioners.
Machine Learning Approaches to Classify Anatomical Regions in Rodent Brain from High Density Recordings

Identifying different functional regions during a brain surgery is a challenging task usually performed by highly specialized neurophysiologists. Progress in this field may be used to improve in situ brain navigation and will serve as an important building block to minimize the number of animals in preclinical brain research required by properly positioning implants intraoperatively. The study at hand aims to correlate recorded extracellular signals with the volume of origin by deep learning methods. Our work establishes connections between the position in the brain and recorded high-density neural signals. This was achieved by evaluating the performance of BLSTM, BGRU, QRNN and CNN neural network architectures on multisite electrophysiological data sets. All networks were able to successfully distinguish cortical and thalamic brain regions according to their respective neural signals. The BGRU provides the best results with an accuracy of 88.6 % and demonstrates that this classification task might be solved in higher detail while minimizing complex preprocessing steps.
Sub-surface Laser Engraving in Thick Hexagonal Scintillation Crystals

Positron Emission Tomography detectors are commonly dense pixelated inorganic scintillators. These are embedded in a reflector whose thickness hinders the sensitivity. Pixel arrays manufacturing is laborious and expensive. The extensively known laser processing can be employed as an alternative. A grid of laser induced microcracks at the focal spot creates a semitransparent optical barrier to guide scintillation light towards the photosensor. In this work, a cost-effective nanosecond laser has been used to validate this approach. Its design flexibility has been exploited to develop hexagonal patterns of 1.25 mm of edge to serve a hexagonal photodetector, building block of an innovative quasi-spherical scanner. Field flood images of individual readout in LYSO blocks of 5 mm, 7 mm and 10 mm of thickness are presented alongside the pixel resolvability. Depth of interaction has been developed with two shifted layers of 5 mm in a 10 mm crystal. The crystal resolvability and energy resolution confirm the validity of the engraving solution in a complete system.
Time Domain and Frequency Domain Heart Rate Variability Analysis on Electrocardiograms and Mechanocardiograms from Patients with Valvular Diseases

Heart rate variability (HRV) is a physiological phenomenon of the variation of a cardiac interval (interbeat) over time that reflects the activity of the autonomic nervous system. HRV analysis is usually based on electrocardiograms (ECG signals) and has found many applications in the diagnosis of cardiac diseases, including valvular diseases. This analysis could also be performed on seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) that provide information on cardiac cycles and the state of heart valves. In our study, we sought to evaluate the influence of valvular heart disease on the correlations between HRV indices obtained from electrocardiograms, seismocardiograms, and gyrocardiograms and to compare the HRV indices obtained from the three aforementioned cardiac signals. The results of HRV analysis in the time domain and frequency domain of the ECG, SCG, and GCG signals are within the standard deviation and have a strong linear correlation. This means that despite the influence of VHDs on the SCG and GCG waveforms, the HRV indices are valid. Clinical Relevance—Cardiac mechanical signals (seismocar-diograms and gyrocardiograms) can be applied to evaluate heart rate variability despite the influence of valvular diseases on the morphology of cardiac mechanical signals
Growth Behavior of IMCs on <001>-oriented and Large-grain electroplated Cu substrate

In recent years, electronic components have become more and more miniaturized, intelligent and multifunctional. This has been followed by a more complex problem that the working environments of electronic components are getting harsher and worse. It is well known that the aggressive growth of intermetallic compounds (IMCs) formed at the interface of solder/substrate during soldering can extremely degrade the mechanical properties and reliability of the resultant solder joints in electronic packaging. In this study, we prepared a <001>-oriented and large-grain electroplated Cu as substrate. Then the growth behavior of the IMCs at Sn/electroplated Cu as well as Sn/rolling-annealed Cu interfaces was investigated. By observing the IMC morphology, it was found that the Cu6Sn5 thickness on the electroplated Cu was smaller than that on the rolling-annealed Cu, while the Cu3Sn thickness was not significantly different for both Cu substrates. Also, no Kirkendall voids were formed at the Sn/electroplated Cu interface after reflow, implying good reliability for the solder joints based on the electroplated Cu substrate. Growth kinetics analysis reviewed that the Cu6Sn5 growth on the electroplated Cu substrate was mainly controlled by grain boundary diffusion.
Restriction of some unitary representations of O(1,N) to symmetric subgroups

We find the complete branching law for the restriction of certain unitary representations of $O(1,n+1)$ to the subgroups $O(1,m+1)\times O(n-m)$, $0\leq m\leq n$. The unitary representations we consider belong either to the unitary spherical principal series, the spherical complementary series or are unitarizable subquotients of the spherical principal series. In the crucial case $0<m<n$ the decomposition consists of a continuous part and a discrete part. The continuous part is given by a direct integral of unitary principal series representations whereas the discrete part consists of finitely many representations which either belong to the complementary series or are unitarizable subquotients of the principal series. The explicit Plancherel formula is computed on the Fourier transformed side of the non-compact realization of the representations by using the spectral decomposition of a certain hypergeometric type ordinary differential operator. The main tool connecting this differential operator with the representations are second order Bessel operators which describe the Lie algebra action in this realization. To derive the spectral decomposition of the ordinary differential operator we use Kodaira's formula for the spectral decomposition of Schr\"odinger type operators.
K-Band Circularly Polarized Beam Steerable Reflectarray Enabling Internet of Space: Conceptualization and Validation

Low-cost and lightweight CubeSats have opened new possibilities to realize Internet of Space (IoS), a vision that aims to provide global network access, particularly for remote areas. A key challenge for IoS realization is the development of an antenna that can meet the gain, bandwidth, and polarization requirements for IoS while complying with the small form factor of CubeSats. The challenge is further magnified by the need to dynamically reconfigure the antenna’s beam location to maintain connectivity as the satellite orbits. This work addresses these challenges by proposing a beam steerable reflectarray antenna that can potentially be integrated with CubeSats to enable IoS. A novel reflectarray unit cell containing multiple Archimedean spiral arms is designed to support broadband circularly polarized (CP) operation, and its operational principle is discussed in depth. The unit cell provides four states of phase shift that are electronically switchable, allowing the reflectarray to achieve the required aperture phase for the desired beam scan. Representative reflectarray prototypes are designed, fabricated, and measured. CP beam scan up to 60° in the plane of offset across the targeted frequency band of 17.8–20.2 GHz is achieved.
Holography and (1+1)-dimension non-relativistic Quantum Mechanics

I generalize classical gravity/quantum gauge theory duality in AdS/CFT correspondence to (1+1)-dimensional non-relativistic quantum mechanical system. It is shown that (1+1)-dimensional non-relativistic quantum mechanical system can be reproduced from holographic projection of (2+1)-dimension classical gravity at semiclassical limit. In this explanation every quantum path in 2-dimension corresponds to a classical path of 3-dimension gravity under definite holographic projection. I consider free particle and harmonic oscillator as two examples and find their dual gravity description.
Conversion from unipolar to bipolar resistance switching by inserting Ta2O5 layer in Pt/TaOx/Pt cells

We observed unipolar resistance switching in Pt/TaOx/Pt cells. We could make the cell have the bipolar resistance switching by inserting a stoichiometric Ta2O5 layer between Pt and TaOx layers. Bipolar resistance switching in Pt/Ta2O5/TaOx/Pt cells occurred reliably without applying an external compliance current. With increase in the Ta2O5 layer thickness, the current value at the low-resistance state became decreased but the forming voltage became increased. We could explain these intriguing phenomena using the interface-modified random circuit breaker network model.
Investigating Temporal Patterns of Glycemic Control around Holidays

Maintaining good glycemic control is a central part of diabetes care. However, it can be a tedious task because many factors in daily living can affect glycemic control. To support management, a growing number of people living with diabetes are now being prescribed continuous glucose monitors (CGMs) for real-time tracking of their blood glucose levels. However, routine use of CGMs is also an invaluable source of patient-generated data for individual and population-level studies. Prior research has shown that festive periods such as holidays can be a notable contributor to overeating and weight gain. Thus, in this work, we sought to investigate patterns of glycemic control around the holidays, particularly Thanksgiving, Christmas, and New Year, by using 3-months of CGM data from 14 patients with Type 1 Diabetes. We leveraged clinically validated metrics for quantifying glycemic control from CGM data and well-established statistical tests to compare diabetes management on holiday weeks versus non-holiday weeks. Based on our analysis, we found that 86% of subjects (12 out of 14) had worse glycemic control (i.e., more ad-verse glycemic events) during holiday weeks compared to non-holiday weeks. This general trend was prevalent amongst most subjects, however, we also observed unique individual patterns of glycemic control. Our findings provide a basis for further research on temporal patterns in diabetes management and data-driven interventions to support patients and caregivers with maintaining good glycemic control all year round.
Machine-Learning Based High Efficiency Rate Control for AV1

Recent years have witnessed the increasing demand of video coding technologies, which have been continuously developed to meet various requirements in video-related applications. Developed by Alliance for Open Media (AOM), the AOMedia Video 1 (AVl) is an open-source and royalty-free standard. Herein, we achieve high efficiency rate control for AVI based on the machine-learning model, which establishes the rate-quantization relationship in a data-driven manner. More specifically, the Supporting Vector Regression (SVR) is used for rate model parameter estimation. The model is trained using sufficient training data, and incorporated in the encoder. Compared to the default rate control scheme in AV 1, experimental results have shown that 2.01% bitrate could be saved with tolerable bitrate error.
A 0.32-V 151-nW Voltage Reference for Smart IoT Applications

In this article, a low-voltage low-power voltage reference used for smart IoT devices is presented. The proposed volt-age reference is supported by subthreshold MOSFETs with compensation of proportional-to-absolute-temperature (PTAT) voltage and complementary-to-absolute-temperature (CTAT) gate-source voltage. A voltage doubler boosts the input voltage so that the reference can operate at an extra low voltage. Implemented with 180 nm CMOS technology, simulation results show that the voltage reference obtains a TC of 12 ppm $/^{\circ}\mathrm{C}$ under 0.32 V supply within $\text{-}20\ ^{\circ}\mathrm{C}$ to 120 $^{\circ}\mathbf{C}$. The voltage reference has a power consumption of 151 nW, with a total noise of 0.9 $\mu \mathbf{V}$ rms(0.5–10M Hz) and PSRR of −50 dB.
Residual convolutional autoencoder combined with a non-negative matrix factorization to estimate fetal heart rate

The fetal heart rate (fHR) plays an important role in the determination of the good health of the fetus. Beside the traditional Doppler ultrasound technique, non-invasive fetal electrocardiography (fECG) has become an interesting alternative. However, extracting clean fECG from abdominal ECG (aECG) recordings is a challenging task due to the presence of the maternal ECG component and various noise sources. In this context, we propose a deep residual convolutional autoencoder network trained on synthetic aECG simulations followed by a transfer learning phase on real aECG recordings to extract the cleanest fECG. Afterwards, we propose to use a non-negative matrix factorization based approach on the obtained fECG to estimate the fHR. Our method is evaluated on three publicly available databases demonstrating that it can provide significant performance improvement against comparative methodologies. Clinical relevance— The presented method has the advantage of estimating the fetal heart rate from a single-channel abdominal electrocardiogram without prior knowledge on the noise sources nor the maternal R-peak locations.
Aerial Image Classification through Thin Lensless Camera

FlatCam - a lensless camera, is characterized by its thin form factor, flexibility, and low power consumption. These are the ideal characteristics for imaging system in an Unmanned Aerial Vehicle. FlatCam captures the scene as sensor measurements and requires computational algorithms to reconstruct the scene. In this paper, we propose a reconstruction-free frequency domain deep learning based aerial image classification system. The system directly learns from sensor measurements thereby eliminating the computational cost associated with image reconstruction. The proposed approach computes the Discrete Cosine Transform of the sensor measurement and organizes it into subbands to learn an effective embedding for classification with a two-stage training using softmax and triplet loss. A triplet sampling strategy that uses label splitting is used for triplet loss learning. Our system is lightweight and the simulation results demonstrate its effectiveness.
A Deep Multi-Label Segmentation Network For Eosinophilic Esophagitis Whole Slide Biopsy Diagnostics

Eosinophilic esophagitis (EoE) is an allergic inflammatory condition of the esophagus associated with elevated numbers of eosinophils. Disease diagnosis and monitoring require determining the concentration of eosinophils in esophageal biopsies, a time-consuming, tedious and somewhat subjective task currently performed by pathologists. Here, we developed a machine learning pipeline to identify, quantitate and diagnose EoE patients' at the whole slide image level. We propose a platform that combines multi-label segmentation deep network decision support system with dynamics convolution that is able to process whole biopsy slide. Our network is able to segment both intact and not-intact eosinophils with a mean intersection over union (mIoU) of 0.93. This segmentation enables the local quantification of intact eosinophils with a mean absolute error of 0.611 eosinophils. We examined a cohort of 1066 whole slide images from 400 patients derived from multiple institutions. Using this set, our model achieved a global accuracy of 94.75%, sensitivity of 94.13%, and specificity of 95.25% in reporting EoE disease activity. Our work provides state-of-the-art performances on the largest EoE cohort to date, and successfully addresses two of the main challenges in EoE diagnostics and digital pathology, the need to detect several types of small features simultaneously, and the ability to analyze whole slides efficiently. Our results pave the way for an automated diagnosis of EoE and can be utilized for other conditions with similar challenges.