Miracle-dz/UniFAR-FTUs huggingface dataset:
PHYSICAL REVIEW B 99 , 125130 ( 2019 ) Frustrated magnetism from local moments in FeSe

Harrison Ruiz,1,2 Yao Wang,2,3,4 Brian Moritz,2,5 Andreas Baum,6,7 Rudi Hackl,6 and Thomas P. Devereaux2 1Department of Physics, Stanford University, Stanford, California 94305, USA 2Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory and Stanford University, Menlo Park, California 94025, USA 3Department of Applied Physics, Stanford University, Stanford, California 94305, USA 4Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA 5Department of Physics and Astrophysics, University of North Dakota, Grand Forks, North Dakota 58202, USA 6Walther Meissner Institut, Bayerische Akademie der Wissenschaften, 85748 Garching, Germany 7Fakultät für Physik E23, Technische Universität München, 85748 Garching, Germany
Automatic translation of wordplay

This essay tackles the subject of automatic humour translation, and the differences linked to the different translation engines that were used. The used data mainly comprises homographs, paronyms and portmanteau words. A large amount of data and homogeneity in its evaluation were necessary to criticise the AIs’ ability to creatively translate.
A Transient-Extracting Transform Assisted Intelligent Fault Detection and Classification Approach for UPFC Installed Transmission Line

The frequent changes in current and voltage pro-file during different operational modes of unified power flow controller (UPFC) limit the performance of traditional distance protection of transmission lines (TLs). This paper looks into the effect of the UPFC on the TL protection and propose a new fault detection and classification (FDC) technique to support the relay. The sample-to-sample difference of the transient-extracting transform (TET) operator of modal component of current for detection of fault, first. Later, the one-cycle post fault TET operators of three-phase currents and ground current are utilized as features for various classifier like back propagation neural network (BPNN), support vector machine (SVM), decision tree (DT) and random forest (RF) for classification of fault. This protection technique is tested by generating various cases during fault in a standard two-bus MATLAB/Simulink model and validated in OPAL-RT based real-time simulator. The low detection time and high accuracies of different classifier indicate the reliability of TET feature based FDC technique for such crucial TL.
Evaluation of Pulse Contour Markers using an A-Mode Ultrasound: Association with Carotid Stiffness Markers and Ageing

Vascular ageing is directly associated with the blood vessel wall structural and functional abnormalities. Pulse morphology carries information on these abnormalities, and pulse contour analysis (PCA) identifies key amplitudes and timing information on the pulse waveforms that has a prognostic value towards cardiovascular risk stratification. PCA markers derived from second derivative waveforms represent the accelerative and decelerative phase of an arterial pulse. In this work, second derivative diameter waveforms of central arteries such as carotid artery are obtained using an A-mode ultrasound device. The derived PCA markers (b/a, c/a, d/a, e/a, (b-c-d-e)/a) from diameter waveform is investigated for its association with central stiffness markers and aging. An observational and cross-sectional study on 106 subjects (51 male/55 females) was conducted for this investigation. The highest correlation (r = 0.5, P < 0.001) was observed between c/a and PWV, and the lowest correlation was between c/a and AC. Group average values of PCA markers for each age decade group were correlated strongly (r > 0.9, p < 0.001) with age. A change > 19% was observed between the group average values of PCA markers of the normotensive and hypertensive population. The applicability of aforesaid PCA markers on central pulse waveforms, measured using a noninvasive device in resource-limited field settings, would accelerate such large scale vascular screening that is essential to understanding the cardiovascular risks at a population level. Clinical Relevance– This study provides an investigation into using second derivative diameter waveforms obtained from the carotid artery to find its associations with arterial stiffness and ageing.
GAN-Based Augmentation for Gender Classification from Speech Spectrograms

The focus of this study is on gender classification from speech signals produced by adults. Automatic estimation of gender has a broad variety of applications ranging from forensics, authentication systems, diarization of meetings, or user-centered interactive agents, and plays a crucial role also in ‘internal’ technological solutions aimed at improving model accuracy, such as selection of gender-specific acoustic models. In this study, we explore scenarios where only a limited amount of real training data is made available for training of a 2-D convolutional neural network classifier. To address sparsity of the training data, the training set is augmented by synthetically generated samples produced by a generative adversarial network. The adversarial network is given access only to the same limited real-world training dataset as the gender classifier. We demonstrate that even when 80% of the already limited training data are removed and replaced by synthetic spectrograms, the gender classifier models can still be successfully trained thanks to the augmentation and maintain competitive performance.
Improving Deep Learning-based Cardiac Abnormality Detection in 12-Lead ECG with Data Augmentation

Automated Electrocardiogram (ECG) classification using deep neural networks requires large datasets annotated by medical professionals, which is time-consuming and expensive. This work examines ECG augmentation as a method for enriching existing datasets at low cost. First, we introduce three novel augmentations: Limb Electrode Move and Chest Electrode Move both simulate a minor electrode mislocation during signal measurement, and Heart Vector Transform generates an ECG by modeling a rotated main heart axis. These techniques are then combined with nine time series signal augmentations from literature. Evaluation was performed on ICBEB, PTB-XL Diagnostic, PTB-XL Rhythm, and PTB-XL Form datasets. Compared to models trained without data augmentation, area under the receiver operating characteristic curve (AUC) was increased by 3.5%, 1.7%, 1.4% and 3.5%, respectively. Our experiments demonstrated that data augmentation can improve deep learning performance in ECG classification. Analyses of the individual augmentation effects established the efficacy of the three proposed augmentations.
Short-term Pulse Rate Variability to Assess Psychophysiological Changes during Online Trier Social Stress Test (TSST)

Mental stress and unpleasant emotions are significant issues at the moment due to the COVID-19 pandemic, which has been spreading globally for more than a year. During the pandemic, every daily activity was oriented around utilizing an online platform to speak with one another within a company or regarding personal matters. However, how the psychophysiological state is perpetuated while engaging in online engagement is currently limited. Previous research has established a strong correlation between psychophysiological characteristics and diverse contexts. Four university students participated in this study. Each subject needed to follow four stages during an experimental design procedure called Trier Social Stress Test (TSST) namely R1, R2, R3, and R4. We retrieved the physiological and psychological data during the experiment. We observed that the mental and emotional changes that occur during the TSST procedure correlate with physiological features measured using short-term pulse rate variability in both linear and non-linear time series analysis. During an online interview, we discovered that the variation of peak-to-peak intervals was more remarkable in the post-interview session (R3) than during the baseline (R1), and pre-interview session (R2), particularly for the pSD2 parameter, which changed at a rate of 4.235 (0.017 to 0.089 seconds) on a subject. Additionally, we discovered a substantial correlation between negative emotions as measured by PANAS scores and stress level scales $(\mathrm{r}=0.81$, p<0.01), indicating that stress and negative emotion have similar attributes. Our findings indicated that caution should be considered when utilizing online platforms for daily activities and employment, even in a new-normal life period.
Deep Learning using Pre-Brachytherapy MRI to Automatically Predict Applicator Induced Complex Uterine Deformation

This novel deep-learning (DL) algorithm addresses the challenging task of predicting uterine shape and location when deformed from its natural anatomy by the presence of an intrauterine (tandem)/intravaginal (ring) applicator during brachytherapy (BT) treatment for locally advanced cervical cancer. Paired pelvic MRI datasets from 92 subjects, acquired without (pre-BT) and with (at-BT) applicators, were used. We propose a novel automated algorithm to segment the uterus in pre-BT MR images using a deep convolutional neural network (CNN) incorporated with autoencoders. The proposed neural net is based on a pre-trained CNN Inception V4 architecture. It predicts a compressed vector by applying a multi-layer autoencoder, which is then back-projected into the segmentation contour of the uterus. Following this, another transfer learning approach using a modified U-net model is employed to predict the at-BT uterus shape from pre-BT MRI. The complex and large deformations of the uterus are quantified using free form deformation method. The proposed algorithm yielded an average Dice Coefficient (DC) of $94.1\pm 3.3$ and an average Hausdorff Distance (HD) of $4.0\pm 3.1$ mm compared to the manually defined ground truth by expert clinicians. Further, the modified U-net prediction of the at-BT uterus resulted in a DC accuracy of $88.1\pm 3.8$ and HD of $5.8\pm 3.6$ mm. The mean uterine surface point-to-point displacement was 25.0 [10.0–62.5] mm from the pre-BT position. Our unique DL method can thus successfully predict tandem-deformed uterine shape and position from MR images taken before the BT implant procedure i.e. without the applicator in place. Clinical relevance—The proposed DL-based framework can be incorporated as an automatic prediction tool of uterine deformation due to applicator insertion for personalized BT treatments. It holds promise for more streamlined clinical/technical decision-making before BT applicator insertion resulting in improved dosimetric outcomes.
Runge-Kutta methods for stochastic differential equations

In this thesis, high order stochastic Runge-Kutta methods are developed for the numerical solution of (Stratonvich) stochastic differential equations and numerical results are presented. The problems associated with non-communativity of stochastic differential equation systems are addressed and stochastic Runge-Kutta methods particularly suited for such systems are derived. The thesis concludes with a discussion on various implementation issues, along with numerical results from variable stepsize implementation of a stochastic embedded pair of Runge-Kutta methods.
A Low-Noise Injection-Locked Ring Oscillator for biomedical implants applications

This paper presents an Injection Locked Ring Oscillator (ILRO) for use in biomedical implants applications. A pulse injection locking method has been desired for ring oscillator performance enhancement and phase noise cancellation. In addition, the frequency is controlled independently of the biasing circuit using an inversion mode MOS voltage controlled capacitor (varactor). Designed in a 0.18-μm CMOS, this ILRO works inthe ISM band of 902–928 GHz and consumes a lowpower of 4.62 mW under 1.8V power supply. The phase noise of -121.6dBc /Hz at 1MHzoffset frequency was obtained at 76 MHz inputinjection pulse.The proposed circuit can achieve a FoM of -174.19 dBc/Hz.
A Wearable Wideband Analog Bio-Impedance Analyzer for Real-Time Monitoring of Human Physiology

Continuous monitoring of electrophysiological activities of the human body is a significant step toward the effective prognosis, diagnosis, and management of functional disorders and cardiovascular diseases. This paper presents the development of a wireless system for the real-time acquisition of hemodynamics data and ambulatory monitoring of body composition based on electrical bio-impedance (Bio-Z) analysis. The developed system is composed of a low-power wearable unit and a stationary unit connected to a computer. The system conducts the non-radiative non-invasive Bio-Z analysis over a wide bandwidth of 1 MHz through four independent channels. The proposed analog approach detects the physiological activity by extracting the magnitude of the mixed Bio-Z signal, in real-time. A graphical user interface was designed for monitoring, analysis, and storage of the processed data. Moreover, the amplitude and frequency of the electrical excitation signals can be instructed through the user interface, wirelessly. Bench-top validation of the system demonstrated the delivery of current signals over a wide frequency range of 1 kHz - 1 MHz and peak-to-peak amplitude of up to 20 mA. Besides, the system was able to detect the magnitude of the envelope of the mixed signal with amplitude modulation depths as low as 0.1 %. Clinical Relevance- The system provides the real-time monitoring of cardiac activity and blood pulsation in human arteries. In addition, due to the configurability of the frequency and amplitude of the current injection circuit, the system is an excellent candidate to be utilized for real-time medical imaging through electrical bio-impedance tomography as well as electrical bio-impedance spectroscopy.
Tele-Impedance Control Approach Using Wearable Sensors

Tele-operational tasks often suffer from instability issues and limited reliability during unpredictable interactions. We propose a real-time control law reproducing the impedance and kinematic behaviour of a subject's arm (shoulder and elbow) on a remote avatar in a 2-DoF task. The human arm impedance and kinematics are estimated respectively from EMG and M-IMU data and then mapped into the avatar arm through an impedance control. Contrary to literature methods, our portable tele-impedance controller relies only on wearable sensors and enables an easy use in unstructured environments. The good performance (R2> 0.7) of the muscle model used to map on the robot the human stiffness of five healthy subjects indicates the possibility of applying the proposed algorithm for a tele-impedance control.
A Brief Review of The Different Approaches for Mood Disorders Detection and Diagnosis

Mood disorders are a mental disease that affects seriously the feelings and the thinking manner of people. They consist of severely emotional state fluctuations which is different from normal fluctuations. It seems that early detection is the best way to deal with this problem and take the adequate treatment for these disorders. The emergence of computer aided diagnosis applications, in recent years, has given an efficient help for psychiatrist to detect mood fluctuations and diagnose the pathological state in the adequate time. In this context, recent studies apply artificial intelligence in order to detect emotional states from one or more combined attributes. Four attributes are commonly used to detect a mood disorder: facial expressions, speech voice signals, body movements and texts shared in social media. This paper reviews recent research studies conducted for the detection and diagnosis of mood disorders using these attributes. This paper firstly introduces CAD systems in medicine and specifically in mental health domain. Then, it presents a brief explanation of the most used artificial intelligence approaches in CAD systems. After that, It reviews the recent studies performed to detect mood disorders from the four attributes mentioned above. The last section discusses results from the presented approaches.
The second law, maximum entropy production and Liouville's theorem

In 1965 Jaynes provided an intuitively simple proof of the 2nd law of thermodynamics as a general requirement for any macroscopic transition to be experimentally reproducible. His proof was based on Boltzmann's formula S = klnW and the dynamical invariance of the phase volume W for isolated systems (Liouville's theorem). Here Jaynes' proof is extended to show that Liouville's theorem also implies maximum entropy production (MaxEP) for the stationary states of open, non-equilibrium systems. According to this proof, MaxEP stationary states are selected because they can exist within a greater number of environments than any other stationary states. Liouville's theorem applied to isolated systems also gives an intuitive derivation of the fluctuation theorem in a form consistent with an earlier conjecture by Jaynes on the probability of violations of the 2nd law. The present proof of MaxEP, while largely heuristic, suggests an approach to establishing a more fundamental basis for MaxEP using Jaynes' maximum entropy formulation of statistical mechanics.
3D Facial Landmark Localization for cephalometric analysis

Cephalometric analysis is an important and routine task in the medical field to assess craniofacial development and to diagnose cranial deformities and midline facial abnormalities. The advance of 3D digital techniques potentiated the development of 3D cephalometry, which includes the localization of cephalometric landmarks in the 3D models. However, manual labeling is still applied, being a tedious and time-consuming task, highly prone to intra/inter-observer variability. In this paper, a framework to automatically locate cephalometric landmarks in 3D facial models is presented. The landmark detector is divided into two stages: (i) creation of 2D maps representative of the 3D model; and (ii) landmarks' detection through a regression convolutional neural network (CNN). In the first step, the 3D facial model is transformed to 2D maps retrieved from 3D shape descriptors. In the second stage, a CNN is used to estimate a probability map for each landmark using the 2D representations as input. The detection method was evaluated in three different datasets of 3D facial models, namely the Texas 3DFR, the BU3DFE, and the Bosphorus databases. An average distance error of 2.3, 3.0, and 3.2 mm were obtained for the landmarks evaluated on each dataset. The obtained results demonstrated the accuracy of the method in different 3D facial datasets with a performance competitive to the state-of-the-art methods, allowing to prove its versability to different 3D models. Clinical Relevance— Overall, the performance of the landmark detector demonstrated its potential to be used for 3D cephalometric analysis.
Anomalous social network event detection based on Higher-order networks

Anomaly detection has been an important area of research, both in the natural and social-economic spheres. In the study of random sequence problems in complex systems, traditional network analysis methods only consider first-order Markovian properties between sequence data, and they cannot capture non-Markovian properties of higher-order sequence information. In this paper, we apply a higher-order network modeling method, in which the individual physical nodes in a traditional network are represented as sequences of ordered entities, and the interactions between sequences of ordered entities are connected into edges. It can effectively represent the complex temporal interaction patterns between network nodes, and maximize the retention of order dependencies in the data to reveal higher-order Markovian properties in network dynamics. The experiment shows that higher-order relationships can improve the accuracy of anomaly detection in complex networks and facilitate the dynamics analysis of complex systems.
A Swarm Control Model Based on Individual Information Interaction

A new swarm control model is proposed in this study to address the problem that swarm-robot systems have difficulty in effectively avoiding obstacles and maintaining formation in unknown and complex environments. The proposed swarm control model combines the virtual leader method and the artificial potential field method to solve the problem of easy collision and separation during the movement of a swarm-robot system. In addition, in the proposed control model, the individuals in the swarm-robot system can emerge swarm movement only by some simple behavior rules, thus improving the intelligence of the swarm-robot system. At last, the study sets two repre-sentative environments (a no-obstacles environment and a static and dynamic mixed obstacle environment), which illustrates the performance of the proposed model.
Failure Analysis of Thin Film Resistors in High Temperature and High Humidity Environment

Ni-Cr thin film resistors are widely used for low-temperature coefficient, high precision, and stability. As we know, thin-film resistors are unstable in high temperature and high humidity environments. There is an urgent request for further research on the cause of the damage to Ni-Cr thin-film resistors under high temperature and high humidity. This paper welded thin-film resistors on the PCBs and used aging tests under 85°C and 85% RH to test their reliability and record the resistance value and change rate. In the aging tests, the resistance value of the thin film resistor shows an increasing trend. Over time the resistance value drifts or even opens in the aging test. In this paper, both non-destructive and destructive methods are used for failure analysis of the failed resistors, such as 3D confocal laser microscopy, 2D X-ray imaging, scanning electron microscopy (SEM), and energy dispersive analysis (EDS), and focused ion beam microscopy (FIB)The results show that the terminal electrode of the thin film resistor has a severe failure. The failure resistor will gradually crack due to the long-term water vapor erosion at the junction of the terminal electrode and the protective layer. These cracks will cause the resistance value to increase continuously. With the aging time, the trials continue to expand, resulting in the interlayer or intralayer fractures in the terminal electrodes, resulting in an open circuit of the resistor. In addition, the S-containing substances in the air will also enter the inside of the resistors and gradually vulcanize the surface electrode (Ag). At the same time, the resistance value of the resistor also increases, and the Ag layer becomes discontinuous when the surface electrode is vulcanized. It will also cause an open circuit in the resistor.
User Group Profiling through Mobile Application Usage Behavior

Smartphones come pre-loaded with a relatively similar set of applications (abbr. apps) for all users of that particular smartphone brand. They treat users as if they are all part of one big group. This is a simplistic supposition that all smartphone users are alike and have similar usage characteristics. Furthermore, mobile user interfaces are static and do not adapt to the way apps are used. The device maker determines the app icon and app groups on the pre-existing widgets and they stay intact in their current state regardless of the user’s behavior. In this regard, based on app usage behavior, there is a clear need to first identify the groups of user behaviors and then adapt the interface for each user group. In this paper, we propose an ML-based approach to discover users’ group behaviors based on their mobile application usage, with the objective of delivering an appropriate interface adaptation for each user group. To this end, we tested our method on a real dataset that we collected from real users over a one-month period. We discuss in this paper the discovered clusters of users’ behavior and we outline the parties who might benefit from our findings.