Miracle-dz/UniFAR-FTUs huggingface dataset:
Exact stationary state of a run-and-tumble particle with three internal states in a harmonic trap

We study the motion of a one-dimensional run-and-tumble particle with three discrete internal states in the presence of a harmonic trap of stiffness The three internal states, corresponding to positive, negative and zero velocities respectively, evolve following a jump process with rate . We compute the stationary position distribution exactly for arbitrary values of and which turns out to have a finite support on the real line. We show that the distribution undergoes a shape-transition as is changed. For the distribution has a double-concave shape and shows algebraic divergences with an exponent both at the origin and at the boundaries. For the position distribution becomes convex, vanishing at the boundaries and with a single, finite, peak at the origin. We also show that for the special case the distribution shows a logarithmic divergence near the origin while saturating to a constant value at the boundaries.
A quasi-Monte Carlo Method for an Optimal Control Problem Under Uncertainty

We study an optimal control problem under uncertainty, where the target function is the solution of an elliptic partial differential equation with random coefficients, steered by a control function. The robust formulation of the optimization problem is stated as a high-dimensional integration problem over the stochastic variables. It is well known that carrying out a high-dimensional numerical integration of this kind using a Monte Carlo method has a notoriously slow convergence rate; meanwhile, a faster rate of convergence can potentially be obtained by using sparse grid quadratures, but these lead to discretized systems that are non-convex due to the involvement of negative quadrature weights. In this paper, we analyze instead the application of a quasi-Monte Carlo method, which retains the desirable convexity structure of the system and has a faster convergence rate compared to ordinary Monte Carlo methods. In particular, we show that under moderate assumptions on the decay of the input random field, the error rate obtained by using a specially designed, randomly shifted rank-1 lattice quadrature rule is essentially inversely proportional to the number of quadrature nodes. The overall discretization error of the problem, consisting of the dimension truncation error, finite element discretization error and quasi-Monte Carlo quadrature error, is derived in detail. We assess the theoretical findings in numerical experiments.
No-signalling, Contextuality and the Arrow of Time

Bell's seminal paper shows that some correlations in quantum theory are not reconcilable with hidden variables and the classical notion of locality. Yet, a weaker notion of locality, known as no-signalling, survives the no-go-result. Here, we study its restrictiveness by considering the full set of local quantum observables. This leads to a much larger set of no-signalling constraints than usually considered, which is enough to exclude PR-boxes and other types of super-quantum correlations. We emphasise the close connection with contextuality, in particular, we show how no-signalling arises naturally from context composition. Finally, we prove a one-to-one correspondence between quantum states and collections of (non-signalling) probability distributions over product contexts under an additional consistency condition between time arrows in subsystems.
Unsupervised identification of topological phase transitions using predictive models

Machine-learning driven models have proven to be powerful tools for the identification of phases of matter. In particular, unsupervised methods hold the promise to help discover new phases of matter without the need for any prior theoretical knowledge. While for phases characterized by a broken symmetry, the use of unsupervised methods has proven to be successful, topological phases without a local order parameter seem to be much harder to identify without supervision. Here, we use an unsupervised approach to identify boundaries of the topological phases. We train artificial neural nets to relate configurational data or measurement outcomes to quantities like temperature or tuning parameters in the Hamiltonian. The accuracy of these predictive models can then serve as an indicator for phase transitions. We successfully illustrate this approach on both the classical Ising gauge theory as well as on the quantum ground state of a generalized toric code.
Image Processing in DNA

The main obstacles for the practical deployment of DNA-based data storage platforms are the prohibitively high cost of synthetic DNA and the large number of errors introduced during synthesis. In particular, synthetic DNA products contain both individual oligo (fragment) symbol errors as well as missing DNA oligo errors, with rates that exceed those of modern storage systems by orders of magnitude. These errors can be corrected either through the use of a large number of redundant oligos or through cycles of writing, reading, and rewriting of information that eliminate the errors. Both approaches add to the overall storage cost and are hence undesirable. Here we propose the first method for storing quantized images in DNA that uses signal processing and machine learning techniques to deal with error and cost issues without resorting to the use of redundant oligos or rewriting. Our methods rely on decoupling the RGB channels of images, performing specialized quantization and compression on the individual color channels, and using new discoloration detection and image inpainting techniques. We demonstrate the performance of our approach experimentally on a collection of movie posters stored in DNA.
Multiphase flow prediction with deep neural networks

This paper proposes a deep neural network approach for predicting multiphase flow in heterogeneous domains with high computational efficiency. The deep neural network model is able to handle permeability heterogeneity in high dimensional systems, and can learn the interplay of viscous, gravity, and capillary forces from small data sets. Using the example of carbon dioxide (CO2) storage, we demonstrate that the model can generate highly accurate predictions of a CO2 saturation distribution given a permeability field, injection duration, injection rate, and injection location. The trained neural network model has an excellent ability to interpolate and to a limited extent, the ability to extrapolate beyond the training data ranges. To improve the prediction accuracy when the neural network model needs to extrapolate, we propose a transfer learning (fine-tuning) procedure that can quickly teach the neural network model new information without going through massive data collection and retraining. Based on this trained neural network model, a web-based tool is provided that allows users to perform CO2-water multiphase flow calculations online. With the tools provided in this paper, the deep neural network approach can provide a computationally efficient substitute for repetitive forward multiphase flow simulations, which can be adopted to the context of history matching and uncertainty quantification.
How spatial frequencies and color drive object search in real-world scenes: A new eye-movement corpus

When studying how people search for objects in scenes, the inhomogeneity of the visual field is often ignored. Due to physiological limitations, peripheral vision is blurred and mainly uses coarse-grained information (i.e., low spatial frequencies) for selecting saccade targets, whereas high-acuity central vision uses fine-grained information (i.e., high spatial frequencies) for analysis of details. Here we investigated how spatial frequencies and color affect object search in real-world scenes. Using gaze-contingent filters, we attenuated high or low frequencies in central or peripheral vision while viewers searched color or grayscale scenes. Results showed that peripheral filters and central high-pass filters hardly affected search accuracy, whereas accuracy dropped drastically with central low-pass filters. Peripheral filtering increased the time to localize the target by decreasing saccade amplitudes and increasing number and duration of fixations. The use of coarse-grained information in the periphery was limited to color scenes. Central filtering increased the time to verify target identity instead, especially with low-pass filters. We conclude that peripheral vision is critical for object localization and central vision is critical for object identification. Visual guidance during peripheral object localization is dominated by low-frequency color information, whereas high-frequency information, relatively independent of color, is most important for object identification in central vision.
Revisiting Effects of Nitrogen Incorporation and Graphitization on Conductivity of Ultra-nano-crystalline Diamond Films

Detailed structural and electrical properties of ultra-nano-crystalline diamond (UNCD) films grown in H$_\text{2}$/CH$_\text{4}$/N$_\text{2}$ plasma were systematically studied as a function of deposition temperature ($T_d$) and nitrogen content ($\%$ N$_2$) to thoroughly evaluate their effects on conductivity. $T_d$ was scanned from 1000 to 1300 K for N$_2$ fixed at 0, 5, 10 and 20 $\%$. It was found that even the films grown in the synthetic gas mixture with no nitrogen could be made as conductive as 1$-$10$^{-2}$ $\Omega$ cm with overall resistivity of all the films tuned over 4 orders of magnitude through varying growth parameters. On a set of 27 samples, Raman spectroscopy and scanning electron microscopy show a progressive and highly reproducible film material phase transformation, from ultra-nano-crystalline diamond to nano-crystalline graphite as deposition temperature increases. The rate of this transformation is heavily dependent on N$_2$ content. Addition of nitrogen greatly increases the amount of $sp^2$ bonded carbon in the films thus enhancing the physical connectivity in the GB network that have high electronic density of states. However, addition of nitrogen greatly slows down crystallization of $sp^2$ phase in the GBs. Therefore, proper balance between GB connectivity and crystallinity is the key in conductivity engineering of (N)UNCD.
Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning

Deep reinforcement learning has been successfully used in many dynamic decision making domains, especially those with very large state spaces. However, it is also well-known that deep reinforcement learning can be very slow and resource intensive. The resulting system is often brittle and difficult to explain. In this paper, we attempt to address some of these problems by proposing a framework of Rule-interposing Learning (RIL) that embeds high level rules into the deep reinforcement learning. With some good rules, this framework not only can accelerate the learning process, but also keep it away from catastrophic explorations, thus making the system relatively stable even during the very early stage of training. Moreover, given the rules are high level and easy to interpret, they can be easily maintained, updated and shared with other similar tasks.
Investigating the nature of the extended structure around the Herbig star RCrA using integral field and high-resolution spectroscopy

Context. We present a detailed analysis of the extended structure detected around the young and close-by Herbig Ae/Be star R CrA. This is a young triple system with an intermediate mass central binary whose separation is of the order of a few tens of the radii of the individual components, and an M-star companion at about 30 au. Aims. Our aim is to understand the nature of the extended structure by means of combining integral-field and high-resolution spectroscopy. Methods. We conducted the analysis based on FEROS archival optical spectroscopy data and adaptive optics images and integral-field spectra obtained with SINFONI and SPHERE at the VLT. Results. The observations reveal a complex extended structure that is composed of at least two components: a non-uniform wide cavity whose walls are detected in continuum emission up to 400 au, and a collimated wiggling-jet detected in the emission lines of helium and hydrogen. Moreover, the presence of [Fe II] emission projected close to the cavity walls suggests the presence of a slower moving wind, most likely a disk wind. The multiple components of the optical forbidden lines also indicate the presence of a high-velocity jet co-existing with a slow wind. We constructed a geometrical model of the collimated jet flowing within the cavity using intensity and velocity maps, finding that its wiggling is consistent with the orbital period of the central binary. The cavity and the jet do not share the same position angle, suggesting that the jet is itself experiencing a precession motion possibly due to the wide M-dwarf companion. Conclusions. We propose a scenario that closely agrees with the general expectation of a magneto-centrifugal-launched jet. These results build upon the extensive studies already conducted on R CrA.
Solitosynthesis and gravitational waves

We study the gravitational wave phenomenology in models of solitosynthesis. In such models, a first order phase transition is precipitated by a period in which non-topological solitons with a conserved global charge (Q-balls) accumulate charge. As such, the nucleation rate of critical bubbles differs significantly from thermal phase transitions. In general we find that the peak amplitude of the gravitational wave spectrum resulting from solitosynthesis is stronger than that of a thermal phase transition and the timescale of the onset of nonlinear plasma dynamics is comparable to Hubble. We demonstrate this explicitly in an asymmetric dark matter model, and discuss current and future constraints in this scenario.
Assessment of the Local Tchebichef Moments Method for Texture Classification by Fine Tuning Extraction Parameters

In this paper we use machine learning to study the application of Local Tchebichef Moments (LTM) to the problem of texture classification. The original LTM method was proposed by Mukundan (2014). The LTM method can be used for texture analysis in many different ways, either using the moment values directly, or more simply creating a relationship between the moment values of different orders, producing a histogram similar to those of Local Binary Pattern (LBP) based methods. The original method was not fully tested with large datasets, and there are several parameters that should be characterised for performance. Among these parameters are the kernel size, the moment orders and the weights for each moment. We implemented the LTM method in a flexible way in order to allow for the modification of the parameters that can affect its performance. Using four subsets from the Outex dataset (a popular benchmark for texture analysis), we used Random Forests to create models and to classify texture images, recording the standard metrics for each classifier. We repeated the process using several variations of the LBP method for comparison. This allowed us to find the best combination of orders and weights for the LTM method for texture classification.
A SIMPLE CONTROL TECHNIQUE FOR UNIFIED POWER FLOW CONTROLLER (UPFC)

The paper suggested the better application of UPFC over STATCOM and SSSC in terms of reactive power compensation, voltage stability and control in a power system. The main advantage of the UPFC in three phase line compensation is integrated in to a Unified or Generalised power flow controller. This paper describes the simple technique of controlling real and reactive power flow & a comparative study between the conventional power flow controllers like STATCOM & SSSC with UPFC. This paper suggests a simulation study of all the controllers individually through MATLAB simulation tool.
Fetal cardiology : embryology, genetics, physiology, echocardiographic evaluation, diagnosis and perinatal management of cardiac diseases

Cardiovascular Development. Cardiac Morphogenesis. Cardiac Anatomy and Examination of Specimens. Placental Implantation and Development. Placental Circulations. The Physics of Ultrasound Imaging. Epidemiology of Congenital Heart Disease: Etiology,Pathogenesis and Incidence. Indication for Fetal Echocardiography: Screening in Low- and High-Risk Populations. Fetal Circulation. Development of Fetal Cardiac and Extracardiac Doppler Flows in Early Gestation. The Examination of the Normal Fetal HeartUsing Two-Dimensional Echocardiography. Early Human Fetal Cardiovascular Dynamics. First and Early Second Trimester Fetal Heart Screening. Three Dimensional Fetal Echocardiography. Cardiac Malpositions and Syndromes with Right or Left Atrial Isomerism.Anomalies of the Right Heart. Intracardiac Shunt Malformations. Left Heart Malformations. Ventricular Outflow Tract Anomalies. Truncus Arteriosus or Common Atrial Trunk. Fetal Detection of Arterial Malpositions. Abnormal Visceral and Atrial Situs andCongenital Heart Disease. Diseases of the Myocardium, Endocardium and Pericardium During Fetal Life. Cardiomyopathy in the Fetus. Examination of the Fetal Coronary Circulation. Fetal Cardiac Tumors. The Fetal Venous System: Normal Embryology, Anatomy andPhysiology and the Development and Appearance of Anomalies. Diagnosis of Cardiac Arrhythmias During Fetal Life. Fetal Bradydysrhythmia. Fetal Tachyarrhythmia. Cardiac Diseases in Association with Hydrops Fetalis. Genetics and Cardiac Abnormalities.Cardiac Defects in Chromosomally Abnormal Fetuses. Associated Anomalies in Congenital Heart Disease. From Fetal to Neonatal Circulation. The Neonate with Congenital Heart Disease - Medical and Interventional Management. Infants with Congenital HeartDisease in the First Year of Life. Prospects for Fetal Cardiac Surgery. Saving the Smallest Hearts - Cardiac Intervention for Structural Heart Disease and Arryhthymias in the Fetus. Fetal Cardiac Function in Normal and Growth-Restricted Fetuses. VenousFlow in Intrauterine Growth Restriction and Cardiac Decompensation. Fetal Distress and Intrapartum Monitoring. Cardiac Disease in Pregnancy. Maternal Diseases and Therapies Affecting the Fetal Cardiovascular System.
Control Method of Parallel Inverters with Self-Synchronizing Characteristics in Distributed Microgrid

The centralized control mode is no longer applicable for microgrid operation due to the high penetration rate of distributed energy, which is responsible for the widespread interest in the use of the distributed microgrid. Focusing on the issues of power coupling and uncontrollable droop coefficient at the terminal of the connecting line between the micro-source and AC bus, which is rarely considered, this paper proposes an improved virtual synchronous generator (VSG) control strategy based on local data considering precise control of the droop coefficient and realizing the power decoupling and the expected droop characteristics. Then, combined with the virtual rotor characteristic matching method, the reasonable active and reactive power sharing of the parallel microgrid inverters are realized in terms of static and dynamic performance without additional improvement of reactive power control. Finally, the effectiveness and feasibility of the proposed method are verified based on the MATLAB/Simulink simulation platform. The combination of the proved strategy and matching principle endows inverters with self-synchronization characteristics, forming the self-synchronizing voltage sources, which gives the distributed microgrid a higher self-stability, autonomy and robustness to ensure the stable operation of the microgrid.
The effect of heavy metals to be found in the environment on the human body

The heavy metals mean serious risk in environmenta l pollution. Some of them are essential for many organisms in a low concentration, but the others sh ould be toxic at low concentrations, too. The heavy metals are contacted through the food chain with living organi sms. Industrial, transport and municipal waste orig in effects of contaminants appear more and more in environmental pollution, and many of which earlier and nowadays g ot a large amount of heavy metals into the environment . Therefore, over the last few decades the trace el ement analytical examination of the various biological an d human samples has become increasingly important. The human biological samples for trace element analysis of reliably detected many cases of occupational di sease, poisoning, environmental hazards, and using the res ults of analyzes of health status can be characteri zed. This paper presents an example for the correlation of th e concentration in human hair and in the environmen t of different heavy metals. The valley “Parád-Recsk, Hu ngary” has special geological capabilities. In the ar a of “Recsk” according to the deep drilling of mine comp any large amounts of copper, lead, zinc minerals ha ve been identified. In “Parádsasvár, Hungary” worked glassw orks from the early 1900s until 1995 and lead cryst al glass was produced, which increased the air and soil lead content. In this area were monitored the quantitie s of heavy metals in soil and the human hair analysis by Flame Atomic Adsorption Spectrometry (FAAS).
Comprehensive Survey on Automatic Embedded Attendance System

Keeping up the participation is vital in every one of the organizations for checking the execution of workers. Each foundation has its own particular strategy in and collecting attendance specific manner. Some are taking participation physically utilizing the old paper or record based methodology and some have embraced strategies for programmed participation utilizing some biometric systems. A participation recording framework's essential capacity is to keep a record of the time at which the representatives of a specific firm answer to work and leave from work. It is a standout amongst the most broadly utilized occasion recording application as a part of the business today. A track of representative participation is an unquestionable requirement for finance era. The customary technique for keeping up a participation register makes the employment extremely dull and inclined to human mistakes. The gathering and handling of information of representative participation may incorporate leaves, half working days, extra time and so on and utilizing it to create the finance physically devours critical worker hours, which thus could be utilized to build profitability and thus beneficially of the firm. If there should arise an occurrence of a blunder, much additional the reality of the situation will become obvious eventually used to identify and redress it, if the procedure is manual. The entire procedure can be improved to awesome degree. This paper describes the various research work recently proposed related with the above specified issues.
Overview and comparison of modern NoSQL databases

With every new day, data is getting bigger and more complex. The challenge had stepped out years ago and the industry has started exploring different ideas. At the beginning, the ideas were in the field of relational databases, handing out adjustments, new features, plays and tricks, which all in all did their job. For a while. With the expansion of web it became clear it was insufficient. Big data has stepped on the stage. And so did NoSQL databases. This paper presents different types of databases and shows differences between relational and NoSQL databases.