Miracle-dz/UniFAR-FTUs huggingface dataset:
Chern-Simons theory on Seifert manifold and matrix model

Chern-Simons (CS) theories with rank $N$ and level $k$ on Seifert manifold are discussed. The partition functions of such theories can be written as a function of modular transformation matrices summed over different integrable representations of affine Lie algebra $u(N)_k$ associated with boundary Wess-Zumino-Witten (WZW) model. Using properties of modular transform matrices we express the partition functions of these theories as a unitary matrix model. We show that, the eigenvalues of unitary matrices are discrete and proportional to hook lengths of the corresponding integrable Young diagram. As a result, in the large $N$ limit, the eigenvalue density develops an upper cap. We consider CS theory on $S^2\times S^1$ coupled with fundamental matters and express the partition functions in terms of modular transformation matrices. Solving this model at large $N$ we find the dominant integrable representations and show how large $N$ representations are related to each other by transposition of Young diagrams as a result of level rank duality. Next we consider $U(N)$ CS theory on $S^3$ and observed that in Seifert framing the dominant representation is no longer an integrable representation after a critical value of 't Hooft coupling. We also show that CS on $S^3$ admits multiple (two-gap phase) large $N$ phases with the same free energy.
Ultralow-power nonlinear optics using tapered optical fibers in metastable xenon

We demonstrate nanowatt-level saturated absorption using a sub-wavelength diameter tapered optical fiber (TOF) suspended in a gas of metastable xenon atoms. This ultralow-power nonlinearity is enabled by a small optical mode area propagating over a relatively long distance through the Xe gas. The use of inert noble gasses in these kinds of TOF experiments may offer practical advantages over the use of reactive alkali vapors such as rubidium.
Low-field electromagnet for a high-resolution MRI system

This paper presents the design and experimental characterization of a 1 T electromagnet tailored to meet the demands of a Magnetic Resonance Imaging (MRI) system conceived for spatial resolutions at the level of tens of microns. For high image quality, MRI requires an homogeneous magnetic field over the Field of View (FoV) where the sample is imaged. We measure the relative inhomogeneity of the field generated by our magnet to be well below 100 parts per million over a spherical FoV of 20 mm diameter, while strongly constraining fringe field lines to avoid interference with other devices. The magnet performance closely follows our expectations from numerical simulations in all the experimental tests carried out. Additionally, we present the solutions adopted for thermal management and the design of a mechanical structure to distribute the weight and integrate the platform to move the sample in and out of the magnet.
Cardiovascular Age and Central Blood Pressure assessment based on Peripheral Blood Pressure Morphology and Artificial Neural Networks

Cardiovascular health can be assessed from central blood pressure waveform and arterial stiffness, highly associated with Arterial Age (AA). However, the acquirement of these parameters is challenging. This paper proposes the estimation of Systolic Central Blood Pressure (SBPc) and classification of Chronological Age (CHA) by groups, as a substitute of AA, by means of non-invasive cuff-pressure Arterial Pulse Waveform (APW) acquisition and further pulse analysis, using regression analysis for SBPc and classification for CHA. A set of Artificial Neural Network (ANN) for each respective outcome was trained and validated with an in-silico database (n=4374) from a One-Dimensional (1-D) model. As a result, a Root Mean Squared Error (RMSE) of 0.39 mmHg and Mean Absolute Percentage Error (MAPE) of 0.61% was obtained, while an accuracy of 97.8% was achieved for classification. Following this, an in-vivo dataset (n=32) was used to evaluate the performance of both ANN obtaining an RMSE of 5.85mmHg and MAPE of 4.3%, while the accuracy decreased to 68.9%. The proposed methodology could have the potential to determine the AA of a subject using only a single peripheral APW. Furthermore, a populated in-vivo evaluation remains to be conducted.
Error Analysis and Experimental Study of Twelve-branch Orthogonal Parallel Six-axis Force Sensor

Six-axis force sensors are widely used in precision fields such as aerospace, robotics, biomechanics and so on. In order to improve the bearing capacity of the sensor and ensure the measurement accuracy, a parallel six-axis force sensor with bidirectional decoupling of tension and compression in orthogonal structure is proposed. By adopting the spherical contact structure as the measuring branch, the sliding friction between the branches of the six-axis force sensor can be changed into rolling friction to reduce the coupling error and improve the accuracy, more suitable for the field of precision measurement. At the same time, according to the force balance equation and the measurement principle of the six-axis force sensor, considering the rolling friction between the branches and the new coupling output error caused by the orthogonal structure, the measurement model of the designed orthogonal structure tension-compression bidirectional decoupling six-axis force sensor is established. Based on the analysis results of numerical examples, the relationship between the coupling error outputs and the corresponding design parameters of the sensor is discussed. Finally, the loading calibration experiment is carried out on the sensor prototype.
Fixed-time Stabilization of Pehlivan System via Adaptive Backstepping Method

In this paper, the problem of fixed-time control for Pehlivan chaotic system with unknown parameters are investigated. On the basis of the Lyapunov function and fixed-time theory, an adaptive backstepping strategy is presented. The control scheme suppresses the chaotic phenomenon in a fixed time. In the process of design, the unknown parameters are approximated by adaptive control technology, and the piecewise function is proposed to deal with the singular problem appeared in the virtual control law. Simulation results are presented to validate the theory analysis.
An 88% fractional bandwidth reconfigurable power amplifier for NB-IoT and LTE-M in 22 nm CMOS FDSOI

A wideband power amplifier (PA), with 88% fractional bandwidth operating in the 700 MHz to 1.8 GHz range, which covers most of the standardized narrowband internet-of-things (NB-IoT) and LTE-M bands, is fully integrated in 22 nm CMOS FDSOI. A single-stage differential amplifier core, combined with an on-chip balun, realizes a single-ended output. A stacked circuit architecture, with 3.3 V LDMOS transistors as output devices and thin-oxide flipped-well transistors as the input common-source (CS) stage, enable a wide bandwidth and reliable operation at a supply voltage of 2.55 V. Capacitor banks with stacked thin-oxide transistor switches tune the PA frequency characteristics over the wide frequency range. The PA delivers a saturated output power (Psat) of up to +24.4 dBm with up to 45% power-added efficiency, which is compliant with the current 3GPP standard for NB-IoT and LTE-M. The maximum power-gain varies between 30.7 dB and 29.2 dB over the whole frequency range. The PA core circuit occupies 0.32 mm2 of silicon area, of which 0.2 mm2 is occupied by the low loss balun.
Energetics of the quantum graphity universe

Quantum graphity is a background independent model for emergent geometry, in which space is represented as a dynamical graph. The high-energy pregeometric starting point of the model is usually considered to be the complete graph; however, we also consider the empty graph as a candidate pregeometric state. The energetics as the graph evolves from either of these high-energy states to a low-energy geometric state is investigated as a function of the number of edges in the graph. Analytic results for the slope of this energy curve in the high-energy domain are derived, and the energy curve is determined exactly for small number of vertices N. To study the whole energy curve for larger (but still finite) N, an epitaxial approximation is introduced. This work may open the way to compare predictions from quantum graphity with observations of the early Universe, making the model falsifiable.
A Novel Algorithm for Detecting Spasmodic Dysphonia Voice Pathology using Random Forest Frame Work

Spasmodic dysphonia, a rare voice disorder is detected in the current work using Random Forest frame work. Voice pathology is related to the vocal tract area affecting the quality of speech. Numerous voice pathologies have been over the years of them are unnoticed as the symptoms are not significant. Even the symptoms are known the nature of the disorder is difficult to identify due to the over lapping nature of the symptoms. The existing algorithms for voice pathology detection are capable of classifying between normal and affected subjects, while the nature of the disorder has been considered in the proposed algorithm. Computational complexity has been reduced due to the incorporation of finite significant energy features estimated over non overlapping frames. Classification of accuracy of 93.5 has been seen with a population of 100 trees.
Automated Bone Segmentation Using a Hybrid Model of DRLSE and Faster R-CNN

The computer aided diagnosis systems from radiological images has been of interest to researchers mostly for detection of bone fracture or dislocation. The accuracy highly depends on bone segmentation. Any improvement of such systems, particularly for noisy X-ray images, is very valuable. Classical image segmentation depending on image homogeneity are time consuming and require pixel-wise labelling. On the other hand, saliency map based approaches fail to detect the region around the fracture or segment the dislocated bones. In our research we have used transfer learning to train the faster regional convolutional neural network (FCNN) alongside distance regularized level set evolution (DRLSE) to have accurate bone segmentation without any pixel-wise labelling enabling segmentation of the region around the fracture and dislocated bones. We applied the proposed method to a number of hand X-ray images and achieved accuracy values of 95% and average precision-recall of 0.96.
THE EFFECT OF STARBURST METALLICITY ON BRIGHT X-RAY BINARY FORMATION PATHWAYS

We investigate the characteristics of young (<20 Myr) and bright (LX > 1 × 1036 erg s−1) high-mass X-ray binaries (HMXBs) and find the population to be strongly metallicity dependent. We separate the model populations among two distinct formation pathways: (1) systems undergoing active Roche lobe overflow (RLO) and (2) wind accretion systems with donors in the (super)giant stage, which we find to dominate the HMXB population. We find metallicity to primarily affect the number of systems which move through each formation pathway, rather than the observable parameters of systems which move through each individual pathway. We discuss the most important model parameters affecting the HMXB population at both low and high metallicities. Using these results, we show that (1) the population of ultra-luminous X-ray sources can be consistently described by very bright HMXBs which undergo stable RLO with mild super-Eddington accretion and (2) the HMXB population of the bright starburst galaxy NGC 1569 is likely dominated by one extremely metal-poor starburst cluster.
THE CHEMISTRY OF POPULATION III SUPERNOVA EJECTA. I. FORMATION OF MOLECULES IN THE EARLY UNIVERSE

We study the formation and destruction of molecules in the ejecta of Population III supernovae (SNe) using a chemical kinetic approach to follow the evolution of molecular abundances from day 100 to day 1000 after explosion. The chemical species included in the study range from simple diatomic molecules to more complex dust precursor species. All relevant molecule formation and destruction processes that are unique to the SN environment are considered. Our work focuses on zero-metallicity progenitors with masses of 20, 170, and 270 M☉, and we study the effect of different levels of heavy element mixing and the inward diffusion of hydrogen and helium on the ejecta chemistry. We show that the ejecta chemistry does not reach a steady state within the relevant timespan (∼3 yr) for molecule formation, thus invalidating previous results relying on this assumption. The primary species formed in the harsh SN environment are O2, CO, SiS, and SO. The SiO, formed as early as 200 days after explosion, is rapidly depleted by the formation of silica molecular precursors in the ejecta. The rapid conversion of CO to C2 and its thermal fractionation at temperatures above 5000 K allow for the formation of carbon chains in the oxygen-rich zone of the unmixed models, providing an important pathway for the formation of carbon dust in hot environments where the C/O ratio is less than 1. We show that the fully mixed ejecta of a 170 M☉ progenitor synthesizes 11.3 M☉ of molecules, whereas 20 M☉ and 270 M☉ progenitors produce 0.78 M☉ and 3.2 M☉ of molecules, respectively. The admixing of 10% of hydrogen into the fully mixed ejecta of the 170 M☉ progenitor increases its molecular yield to ∼47 M☉. The unmixed ejecta of a 170 M☉ progenitor SN without hydrogen penetration synthesizes ∼37 M☉ of molecules, whereas its 20 M☉ counterpart produces ∼1.2 M☉. This smaller efficiency at forming molecules is due to the large fraction of He+ in the outer mass zone of the ejecta. Finally, we discuss the cosmological implication of molecule formation by Pop III SNe in the early universe.
Diseño de clasificadores de dígitos manuscritos generados por redes GAN

En el presente trabajo se diseñan y evalúan varios sistemas encargados de clasificar automáticamente, en dos categorías, imágenes de "dígitos" y "no dígitos" que fueran generadas por dos redes adversarias generativas (Generative Adversarial Networks o GAN – por sus siglas en inglés–). Ambas redes fueron entrenadas para generar imágenes de números manuscritos, pero colateralmente también pueden producir imágenes que no podrían clasificarse como dígitos. Así, se diseñaron distintos métodos semi-supervisados para clasificar las imágenes no etiquetadas, con el objeto de medir el desempeño de dichas redes. A partir de un conjunto de imágenes elegidas al azar, generadas por las redes GAN, se realizó una clasificación manual. Se utilizaron dichas imágenes clasificadas para entrenar los distintos métodos semi-supervisados. A su vez, se definieron diferentes grupos de datos de entrenamiento y prueba, que llevaron a otras tantas variantes de cada clasificador. Se explica el proceso de diseño y los detalles de implementación de cada uno de estos clasificadores. Se muestran los mejores resultados obtenidos con cada uno de ellos, para finalmente elegir aquel con mejor desempeño para esta tarea, de acuerdo con las pruebas realizadas.
An intelligent decision support system to anticipate the fall of a structure for paraplegic patients

The Human posture is defined by the ability to maintain a stable vertical position of balance while keeping the feet fixed relative to the ground. An accurate understanding of the equilibrium conditions is an essential point to dimension and model the forces in the exoskeleton structures. Several experiments were performed in this sense. However, the resulting data are quite vague and uncertain, which could contribute to the error in the equilibrium description. In this paper, we focus on the folding. Our objective is to find the factors and mechanisms that influence this fallback in order to improve its anticipation. We have considered an approach based on fuzzy logic to better explain the uncertain and ambiguous aspect of the data, and on data mining algorithms to find the factors that influenced the fall. The results of our methodology showed promising associations to anticipate this type of fall.
Spinon heat transport and spin–phonon interaction in the spin-1/2 Heisenberg chain cuprates Sr2CuO3 and SrCuO2

We have investigated the thermal conductivity κmag of high-purity single crystals of the spin chain compound Sr2CuO3, which is considered an excellent realization of the one-dimensional spin-1/2 antiferromagnetic Heisenberg model. We find that the spinon heat conductivity κmag is strongly enhanced as compared to previous results obtained on samples with lower chemical purity. The analysis of κmag allows the computation of the spinon mean free path lmag as a function of temperature. At low temperature we find lmag ∼ 0.5 µm, corresponding to more than 1200 chain unit cells. Upon increasing the temperature, the mean free path decreases strongly and approaches an exponential decay ∼ (1/T)exp(Tu*/T), which is characteristic for Umklapp processes with the energy scale kBTu*. Based on Matthiessen’s rule we decompose lmag into a temperature-independent spinon–defect scattering length l0 and a temperature-dependent spinon–phonon scattering length lsp(T). By comparing lmag(T) of Sr2CuO3 with that of SrCuO2, we show that the spin–phonon interaction, as expressed by lsp, is practically the same in both systems. The comparison of the empirically derived lsp with model calculations for the spin–phonon interaction of the one-dimensional spin-1/2 XY model yields reasonable agreement with the experimental data.
Polarization measurement analysis III. Analysis of the polarization angle dispersion function with high precision polarization data

High precision polarization measurements open new opportunities for the study of the magnetic field structure as traced by polarimetric measurements of the interstellar dust emission. Polarization parameters suffer from bias in the presence of measurement noise. It is critical to take into account all the information available in the data in order to accurately derive these parameters. The goal of this paper is to characterize the bias on the polarization angle dispersion function that is used to study the spatial coherence of the polarization angle. We characterize, for the first time, the bias on the conventional estimator of the polarization angle dispersion function (S hereafter) and show that it can be positive or negative depending on the true value. Monte Carlo simulations are performed in order to explore the impact of the noise properties of the polarization data, as well as the impact of the distribution of the true polarization angles on the bias. We show that in the case where the ellipticity of the noise in (Q, U) varies by less than 10 percent, one can use simplified, diagonal approximation of the noise covariance matrix. In other cases, the shape of the noise covariance matrix should be taken into account in the estimation of S. We also study new estimators such as the dichotomic and the polynomial estimators. Though the dichotomic estimator cannot be directly used to estimate S, we show that, on the one hand, it can serve as an indicator of the accuracy of the conventional estimator and, on the other hand, it can be used for deriving the polynomial estimator. We propose a method for determining the upper limit of the bias on the conventional estimator of S. The method is applicable to any linear polarization data set for which the noise covariance matrices are known.
Second Order Perturbative Effects on the Acoustic Geometry

In this work, we have studied the effect of higher order perturbations, particularly the second order in details, on the sonic horizon. We have considered two different schemes of perturbations which are velocity potential perturbation and mass acceleration rate perturbation. These two schemes give us qualitatively similar behaviour. We have found that the analogue gravity formalism also holds for the higher order perturbations.
Accretion flows with comparable radiation and gas pressures

By taking into account photon absorption, we investigate the vertical structure of accretion flows with comparable radiation and gas pressures. We consider two separate energy equations for matter and radiation in the diffusion limit. In order to solve the set of radiation hydrodynamic equations in steady state and axisymmetric configuration, we employ self-similar technique in the radial direction. We need the reflection symmetry about the mid-plane to find gas density at the equator. For a typical solution, we assume that the gas pressure has 10-50\% portion of the total pressure. In this paper, since the radiation energy is involved directly, we are able to estimate how much energy of viscous heating is transported in the radial direction and advected towards the central object. Our results show that although the mass accretion rate does not approach the Eddington limit, the energy advection is rather high. Moreover, in a disc with greater accretion rate and less portion of gas pressure at the total pressure, more energy is advected to its center. In addition, as we expect the accretion flow becomes thicker with greater values of gas pressure. Based on Solberg-HAžiland conditions, we notice that the flow is convectively stable in all parts of such a disc.
Efficient, high-speed ablation of soft tissue with few-microjoule, femtosecond pulse bursts

Femtosecond pulses hold great promise for high-precision tissue removal. However, ablation rates are severely limited by the need to keep average laser power low to avoid collateral damage due to heat accumulation. Furthermore, previously reported pulse energies preclude delivery in flexible fibers, hindering in vivo operation. Both of these problems can be addressed through use of groups of high-repetition-rate pulses, or bursts. Here, we report a novel fiber laser and demonstrate ultrafast burst-mode ablation of brain tissue at rates approaching 1 mm$^3$/min, an order of magnitude improvement over previous reports. Burst mode operation is shown to be superior in terms of energy required and avoidance of thermal effects, compared to uniform repetition rates. These results can pave the way to in vivo operation at medically relevant speeds, delivered via flexible fibers to surgically hard-to-reach targets, or with simultaneous magnetic resonance imaging.
Towards the use of artificial intelligence and machine learning in material scientist field

Currently, the Artificial Intelligence (AI) and Machine Learning (ML) are used several engineering applications. They allow to reduce the human workload and to improve the quality of life. In addition, nearly every sector in the world intends to use artificial intelligence and machine learning. So, machine learning is a field of artificial intelligence that consists of programming a machine to learn to perform tasks by studying examples of these tasks. In other words, it consists of developing a model using an optimization algorithm to minimize the errors between the model and the data. The aim of this paper is to describe the role of artificial intelligence and machine learning and its current applications in material scientist field. The principle of deep learning and Convolutional Neural Networks (CNNs) and their use in image classification is explained.
A Privacy Protection based Countermeasure for E-commerce Price Discrimination

Price discrimination is a pricing strategy of differentiated prices for different customers usually employed by the provider of goods or services to obtain excess profits. Its essence is a monopolistic pricing behavior. In this paper, we propose a price discrimination countermeasure based on the Randomized Response (RR) mechanism of Local Differential Privacy (LDP). First, we select shopping records of $N$ customers from the database of the e-commerce platform as a large set P, and then add noise to randomly selected shopping records of $K$ customers. Due to the randomness, the merchant cannot deduce the shopping record of the customer from the randomized output, thereby protecting the shopping information of the customer. In addition, we use data desensitization technology to project the sensitive personal information of customers. The scheme evaluating results show that the scheme can help customers fight against the price discrimination well.