Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoherent histories of quantum searching

The theory of decoherent histories is an attempt to derive classical physics from positing only quantum laws at the fundamental level without notions of a classical apparatus or collapse of the wave-function. Searching for a marked target in a list of N items requires \Omega(N) oracle queries when using a classical computer, while a quantum computer can accomplish the same task in O{\sqrt{N}} queries using Grover's quantum algorithm. We study a closed quantum system executing Grover algorithm in the framework of decoherent histories and find it to be an exactly solvable model, thus yielding an alternate derivation of Grover's famous result. We also subject the Grover-executing computer to a generic external influence without needing to know the specifics of the Hamiltonian insofar as the histories decohere. Depending on the amount of decoherence, which is captured in our model by a single parameter related to the amount of information obtained by the environment, the search time can range from quantum to classical. Thus, we identify a key effect induced by the environment that can adversely affect a quantum computer's performance and demonstrate exactly how classical computing can emerge from quantum laws.
Classification of DNA Sequences: Performance Evaluation of Multiple Machine Learning Methods

Polymerase chain reaction (PCR) has long been the mainstay in genetic sequencing and identification. Irrespective of whether short read or long read technologies are adopted, PCR methods are generally time consuming and expensive. Recently, an all-electronic approach, the so-called Single Molecule Break Junction (SMBJ) method, has been proposed as a possible alternative to PCR. In this article, we evaluate the performance of four different classifier models on the current signatures of ten short strand sequences, including a pair that differs by a single mismatch. We find that a gradient boosted tree classifier model achieves impressive accuracies, ranging from approximately 96% for molecules differing by a single mismatch to 99.5% otherwise.
Design and performance test of motor braking and friction braking integrated system

In terms of vehicle braking and energy saving, the traditional mechanical friction brake relies on frequent or long-term mechanical friction. In the process of friction braking, it will not only produce a large amount of toxic dust, but also cause the overheating of brake drum (disc) and friction plate (brake lining), resulting in the decline of braking efficiency and even braking failure. Therefore, an integrated system of motor braking and friction braking and its control method are proposed. When the vehicle is braked, the friction braking device works independently, while the motor braking device further realizes the braking of the vehicle according to the slip rate of the wheel; When the motor braking device works, it uses the kinetic energy of the vehicle to drive the brake disc and the permanent magnet firmly connected with it to rotate, so as to generate alternating magnetic field on the coil around the permanent magnet and generate electricity, and store its electric energy in the battery. The test shows that the motor braking and friction braking integrated system can effectively integrate the two braking devices, and combined with the wheel slip rate, can reliably ensure the vehicle braking effect and achieve the purpose of energy storage.
Artificial Neural Network for Predicting a Turbocharged Engine Performance: An Empirical Study

Experiments to determine the performance and characteristics of an internal combustion engine related to turbocharger is costly, time intense and complex. Besides, it is very difficult to develop an accurate mathematic regression model for engine performance because it involves many independent and dependent variables. Using artificial neural network (ANN), a machine learning model are used to predict the vehicle’s engine performance parameters. ANN model aims to minimize the cost and time required for an engine tuning experiment. However, the experimental data from engine tuning experiment is usually small because of its high cost and time intensive. Therefore, this work intent to reduce the optimization process for an ANN model with a small dataset. Optimization of an ANN is critical to prevent the model trapped within a local optimum solution. Systematic methods utilizing modified grid search and random search are suggested in tuning the model’s hyperparameters. Moreover, the importance of each optimization steps is ranked based on the analysis of the tuning result. The most optimized model has achieved an overall mean absolute error (MAE) of 4.92% for 7 output parameters. Finally, future works are suggested that can be applied such as building a model for each of the output parameter.
Effects of uncertainties and errors on a Lyapunov control

A Lyapunov (open-loop) control is often confronted with uncertainties and errors in practical applications. In this paper, we analyse the robustness of the Lyapunov control against the uncertainties and errors in quantum control systems. The analysis is carried out by examining the uncertainties and errors and calculating the control fidelity under the influence of the certainties and errors. Two examples, a closed control system and an open control system, are presented to illustrate the general formalism, discussions on the effect caused by the uncertainties and errors are given.
A comparative study of dynamic state estimation approaches for detecting loss of excitation failures

Loss of excitation (LOE) is a characteristic failure of synchronous generators. In this article, we present a comparative study between different dynamic state estimation approaches to efficiently detect LOE. In this context, multiple modes of operation of the system are modeled in order to implement a faulty mode detection and diagnosis algorithm. This algorithm is continually monitoring the state variables of the generator to decide, in real-time, the most probable mode of operation. The main goal is to enhance the estimator robustness against mismatches between the model used and the real excitation system. To achieve this goal, we made a comparison between considering the field voltage as an unknown input and treating it as a constant with a high component of noise. Simulations using a two-area power system have shown that LOE detection times are not significantly degraded even if the the excitation system incorporates non-modeled effects.
FORECASTING A CORONAL MASS EJECTION'S ALTERED TRAJECTORY: ForeCAT

To predict whether a coronal mass ejection (CME) will impact Earth, the effects of the background on the CME's trajectory must be taken into account. We develop a model, ForeCAT (Forecasting a CME's Altered Trajectory), of CME deflection due to magnetic forces. ForeCAT includes CME expansion, a three-part propagation model, and the effects of drag on the CME's deflection. Given the background solar wind conditions, the launch site of the CME, and the properties of the CME (mass, final propagation speed, initial radius, and initial magnetic strength), ForeCAT predicts the deflection of the CME. Two different magnetic backgrounds are considered: a scaled background based on type II radio burst profiles and a potential field source surface (PFSS) background. For a scaled background where the CME is launched from an active region located between a coronal hole and streamer region, the strong magnetic gradients cause a deflection of 8. °1 in latitude and 26. °4 in longitude for a 1015 g CME propagating out to 1 AU. Using the PFSS background, which captures the variation of the streamer belt (SB) position with height, leads to a deflection of 1. °6 in latitude and 4. °1 in longitude for the control case. Varying the CME's input parameters within observed ranges leads to the majority of CMEs reaching the SB within the first few solar radii. For these specific backgrounds, the SB acts like a potential well that forces the CME into an equilibrium angular position.
Time Delay Estimation for Indoor Temperature Prediction by Weather Conditions

The problem of indoor temperature prediction for further application in model predictive control synthesis was considered. The introduction of time delay for weather conditions was used to develop a simplified model of the temperature's dynamics that simulates thermal inertness of the building shell. The procedure of numerical estimation of the time delay was proposed on the basis of correlation analysis. Dataset of continuous measurements of indoor temperature and weather conditions was used in the experiment. The data were collected by the wireless network of smart sensors and local meteostation. Splitting the data into training and test samples was used to validate the accuracy of the prediction.
NEW CLASS OF VERY HIGH ENERGY γ-RAY EMITTERS: RADIO-DARK MINI SHELLS SURROUNDING ACTIVE GALACTIC NUCLEUS JETS

We explore non-thermal emission from a shocked interstellar medium, which is identified as an expanding shell, driven by a relativistic jet in active galactic nuclei (AGNs). In this work, we particularly focus on parsec-scale size mini shells surrounding mini radio lobes. From the radio to X-ray band, the mini radio lobe emission dominates the faint emission from the mini shell. On the other hand, we find that inverse-Compton (IC) emission from the shell can overwhelm the associated lobe emission at the very high energy (VHE; E > 100 GeV) γ-ray range, because energy densities of synchrotron photons from the lobe and/or soft photons from the AGN nucleus are large and IC scattering works effectively. The predicted IC emission from nearby mini shells can be detected with the Cherenkov Telescope Array and they are potentially a new class of VHE γ-ray emitters.
Two-dimensional radiation-hydrodynamics simulations of super-luminous interacting supernovae of type IIn

Some interacting supernovae (SNe) of type IIn show a sizeable continuum polarisation suggestive of a large scale asymmetry in the circumstellar medium (CSM) and/or the SN ejecta. Here, we extend the recent work of Dessart et al. on super-luminous SNe IIn and perform axially-symmetric (i.e., 2D) multi-group radiation hydrodynamics simulations to explore the impact of an imposed large scale density asymmetry. When the CSM is asymmetric, the latitudinal variation of the radial optical depth $\tau$ introduces a strong flux redistribution from the higher-density CSM regions, where the shock luminosity is larger, towards the lower-density CSM regions where photons escape more freely --- this redistribution ceases when $\tau $1. When the CSM is symmetric and the ejecta asymmetric, the flux redistribution within the CSM now tends to damp the latitudinal variation of the luminosity at the shock. It then requires a larger ejecta asymmetry to produce a sizeable latitudinal variation in the emergent flux. When the interaction is between a SN ejecta and a relic disk, the luminosity boost at early times scales with the disk opening angle -- forming a super-luminous SN IIn this way requires an unrealistically thick disk. In contrast, interaction with a disk of modest thickness/mass can yield a power that rivals radioactive decay of a standard SN II at nebular times.
Quantization of the Hall conductivity in the Harper-Hofstadter model

We study the robustness of the quantization of the Hall conductivity in the Harper-Hofstadter model towards the details of the protocol with which a longitudinal uniform driving force $F_x(t)$ is turned on. In the vector potential gauge, through Peierls substitution, this involves the switching-on of complex time-dependent hopping amplitudes $\mathrm{e}^{-\frac{i}{\hbar}\mathcal{A}_x(t)}$ in the $\hat{\mathbf{x}}$-direction such that $\partial_t \mathcal{A}_x(t)=F_x(t)$. The switching-on can be sudden, $F_x(t)=\theta(t) F$, where $F$ is the steady driving force, or more generally smooth $F_x(t)=f(t/t_{0}) F$, where $f(t/t_{0})$ is such that $f(0)=0$ and $f(1)=1$. We investigate how the time-averaged (steady-state) particle current density $j_y$ in the $\hat{\mathbf{y}}$-direction deviates from the quantized value $j_y \, h/F = n$ due to the finite value of $F$ and the details of the switching-on protocol. Exploiting the time-periodicity of the Hamiltonian $\hat{H}(t)$, we use Floquet techniques to study this problem. In this picture the (Kubo) linear response $F\to 0$ regime corresponds to the adiabatic limit for $\hat{H}(t)$. In the case of a sudden quench $j_y \, h/F$ shows $F^2$ corrections to the perfectly quantized limit. When the switching-on is smooth, the result depends on the switch-on time $t_{0}$: for a fixed $t_{0}$ we observe a crossover force $F^*$ between a quadratic regime for $F F^*$. The crossover $F^*$ decreases as $t_{0}$ increases, eventually recovering the topological robustness. These effects are in principle amenable to experimental tests in optical lattice cold atomic systems with synthetic gauge fields.
Experimental Results on Direct Position Determination by Exploiting the Multipath Environment

Estimating the source position directly from the array data by parameterizing the array steering vector by the source position is the fundamental principle of the direct position determination (DPD). This paper focuses on the passive DPD of a narrowband emitter in an urban environment using two antenna arrays. By considering the environment as known, the first-order reflections in an urban environment are modeled using the image-source method (ISM). A detailed analysis of the DPD and the Multipath-DPD methods was performed and shown in [1]. Here, we focus on the application of the Multipath-DPD approach on the experimental data and the feasibility of direct localization by exploiting the urban environment. The Multipath-DPD enables a direct source localization using a single antenna array and it eliminates the ambiguous position estimates.
Mapping Kitaev's quantum double lattice models to Levin and Wen's string-net models

We exhibit a mapping identifying Kitaev's quantum double lattice models explicitly as a subclass of Levin and Wen's string net models via a completion of the local Hilbert spaces with auxiliary degrees of freedom. This identification allows to carry over to these string net models the representation-theoretic classification of the excitations in quantum double models, as well as define them in arbitrary lattices, and provides an illustration of the abstract notion of Morita equivalence. The possibility of generalising the map to broader classes of string nets is considered.
Volatile carbon locking and release in protoplanetary disks. A study of TW Hya and HD 100546

The composition of planetary solids and gases is largely rooted in the processing of volatile elements in protoplanetary disks. To shed light on the key processes, we carry out a comparative analysis of the gas-phase carbon abundance in two systems with a similar age and disk mass, but different central stars: HD 100546 and TW Hya. We combine our recent detections of C$^{0}$ in these disks with observations of other carbon reservoirs (CO, C$^{+}$, C$_{2}$H) and gas mass and warm gas tracers (HD, O$^{0}$), as well as spatially resolved ALMA observations and the spectral energy distribution. The disks are modelled with the DALI 2D physical-chemical code. Stellar abundances for HD 100546 are derived from archival spectra. Upper limits on HD emission from HD 100546 place an upper limit on the total disk mass of $\leq0.1\,M_{\odot}$. The gas-phase carbon abundance in the atmosphere of this warm Herbig disk is at most moderately depleted compared to the interstellar medium, with [C]/[H]$_{\rm gas}=(0.1-1.5)\times 10^{-4}$. HD 100546 itself is a $\lambda\,$Bootis star, with solar abundances of C and O but a strong depletion of rock-forming elements. In the gas of the T Tauri disk TW Hya, both C and O are strongly underabundant, with [C]/[H]$_{\rm gas}=(0.2-5.0)\times 10^{-6}$ and C/O${>}1$. We discuss evidence that the gas-phase C and O abundances are high in the warm inner regions of both disks. Our analytical model, including vertical mixing and a grain size distribution, reproduces the observed [C]/[H]$_{\rm gas}$ in the outer disk of TW Hya and allows to make predictions for other systems.
No) New physics in B_s mixing and decay

The status of possible new-physics signals in B_s,d-meson mixing and decay is reviewed. In particular, it is emphasized that the recent LHCb results, that find no evidence for a non-standard phase in B_s-barB_s mixing, make a consistent explanation of the D0 data on the like-sign dimuon charge asymmetry notoriously difficult. In order to clarify the inconclusive experimental situation, independent measurements of the semileptonic asymmetries are needed.
A machine learning approach to address 1-week-ahead peak demand forecasting using the XGBoost algorithm

Load forecasting represents a crucial topic for the power grid industry. Several activities such as grid expansion planning, generation dispatch and maintenance scheduling depend to a great extent on load behaviour prediction.1-week-ahead load forecasting is typically required for unit commitment and outage scheduling. For this time interval, temperature, and day type (business day / weekend / holiday) are identified as the key explanatory variables.This forecasting activity is typically conducted with a "similar day" approach: weather conditions and day type for the date to be forecasted are identified and then compared to the most similar recent historical observations in order to estimate the peak demand with these historical values.Once the similar days have been identified, the forecasting task is not completely straightforward as several difficulties arise regarding differences in the feature values. Simple and complex approaches could be tried to address these shortcomings.A machine learning approach to address this forecasting task may have the advantage of making the most out of the data when it is tuned to allow for a fair degree of complexity.In this paper a model is developed using the XGBoost algorithm. A set of variables is proposed as features and the model is trained for data covering a period of 12 years. 10-fold cross validation is conducted for parameter tuning.The performance achieved by the prediction model is then compared to that of staff members making their predictions with a traditional similar day approach method. The model achieved a root mean squared error of 289 on the test against 429 corresponding to the average of the predictions carried out by the staff members.
Drowsiness Detection using Deep Learning Algorithms

Drowsiness is the major cause of accidents for the drivers in the world. Drowsiness can occur while driving due to lack of sleep or tiredness. The best way to avoid accidents caused ed by drivers is to detect drowsiness of the driver and warn him before he/she fall into sleep. The main idea behind this project is to develop a trained model which can detect fatigue/state of any human and can issue a timely warning. There are many drivers who do not take regular breaks when driving long distances. They face a high risk of becoming drowsy which they often fail to cognize early enough. The drivers ' eyes are monitored using a camera and by developing an algorithm by which symptoms of driver fatigue can be detected early enough to avoid the person from sleeping. Machine Learning algorithm (Neural Networks) has been used to build a model that detects the state of eyes whether they are open or closed. This trained model can be used in any vehicle to identify the drowsiness state of the drivers and warn them by generating an alarm. This technique can also be used for monitoring employees.
Spontaneous CP Violation in $A_4$ Flavor Symmetry and Leptogenesis

We propose a simple renormalizable model for the spontaneous CP violation based on $SU(2)_{L}\times U(1)_{Y}\times A_{4}$ symmetry in a radiative seesaw mechanism, which can be guaranteed by an extra $Z_{2}$ symmetry. In our model CP is spontaneously broken at high energies, after breaking of flavor symmetry, by a complex vacuum expectation value of $A_{4}$-triplet and gauge singlet scalar field. We show that the spontaneously generated CP phase could become a natural source of leptogenesis, %CP violation for neutrino oscillation and leptogenesis, respectively. Then, and also investigate CP violation at low energies in the lepton sector and show how the CP phases in PMNS could be arisen through spontaneous symmetry breaking mechanism. As a numerical study, interestingly, we show that the normal mass hierarchy favors relatively large values of $\theta_{13}$, large deviations from maximality of $\theta_{23} \pi/4$ and discrete values of $\delta_{CP}$ around $100^{\circ},135^{\circ},255^{\circ}$ and $300^{\circ}$. Finally, with a successful leptogenesis our numerical results give more predictive values on the Dirac CP phase: for the normal mass hierarchy $1^{\circ}\lesssim\delta_{CP}\lesssim10^{\circ}$ and for inverted one $\delta_{CP}\sim100^{\circ},135^{\circ},300^{\circ}$.
Automatic Estimation of Neonatal Sleep/Wake States in the NICU Using 3D CNN

In the neonatal intensive care unit (NICU), the preterm infant located in incubator takes various medical care day and night. Unusual environment in NICU may affect neurodevelopment of newborn subject, some researches evaluate the sleep/wake state of subject by visual or using the Actigraph attached on the leg. This paper proposes a sleep/wake status estimation method using video images and convolutional neural network (CNN) to reduce assessment time and improve the reliability. The Brazelton’s criteria evaluates the newborn’s sleep/wake states in six stages, the proposed method performs six-class classification using 3D CNN. In the experiment, we conducted 4 experiments by using original data, two different frame shifting, and using the frame differential. Experimental results using 16 video of 8 subjects showed that the training using original dataset achieved the highest macro-F1 value (0.766) which improves the macro-F1 value (0.765) of our previous result using support vector machine (SVM) and optical flow. Results also suggested that the 3D CNN improves the classification accuracy but the data augmentation using frame shift is not suitable to our dataset.
Line profile studies of hydrodynamical models of cometary compact H II regions

We simulate the evolution of cometary H II regions based on several champagne flow models and bow shock models, and calculate the profiles of the [Ne II] fine-structure line at 12.81 μm, the H30α recombination line and the [Ne III] fine-structure line at 15.55 μm for these models at different inclinations of 0°, 30° and 60°. We find that the profiles in the bow shock models are generally different from those in the champagne flow models, but the profiles in the bow shock models with lower stellar velocity (≤ 5 km s−1) are similar to those in the champagne flow models. In champagne flow models, both the velocity of peak flux and the flux weighted central velocities of all three lines point outward from molecular clouds. In bow shock models, the directions of these velocities depend on the speed of stars. The central velocities of these lines are consistent with the stellar motion in the high stellar speed cases, but they are opposite directions from the stellar motion in the low speed cases. We notice that the line profiles from the slit along the symmetrical axis of the projected 2D image of these models are useful for distinguishing bow shock models from champagne flow models. It is also confirmed by the calculation that the flux weighted central velocity and the line luminosity of the [Ne III] line can be estimated from the [Ne II] line and the H30α line.