Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic template placement algorithm for gravitational wave data analysis

This paper presents an algorithm for constructing matched-filter template banks in an arbitrary parameter space. The method places templates at random, then removes those which are ‘‘too close’’ together. The properties and optimality of stochastic template banks generated in this manner are investigated for some simple models. The effectiveness of these template banks for gravitational wave searches for binary inspiral waveforms is also examined. The properties of a stochastic template bank are then compared to the deterministically placed template banks that are currently used in gravitational wave data analysis.
Minimization of energy storage requirements in wind farms by controlling the state of charge of a battery bank

This paper presents a methodology for reducing energy storage requirements through batteries (BESS) applied to smoothing the power variation of a wind farm (WF). This reduction is achieved by combining the operation of an energy management system (EMS) with an optimally sized BESS. The EMS deploys a state-of-charge feedback-based (SOC-FB) control system to reduce a WF's power variations by coordinating the BESS's charge and discharge cycles. At the same time, an evolutionary optimization algorithm determines the BESS's power capacity to keep the WF's power variation within specific limits defined in a cost function. This approach is tested using data from met towers used during the development phase of the WF. Simulation results show a significant reduction of the WF output power variation with an optimal-calculated capacity of the BESS in comparison with the operation of the WF without the approach proposed in this study.
Systematic Embedded Development and Implementation Techniques on Intel Myriad VPUs

The worldwide demand for speed in applications challenges the deployment of compute-intensive algorithms at the power-constrained edge. Novel embedded devices such as the heterogeneous Vision Processing Units (VPUs) emerge as a promising solution for low-power embedded imaging/vision applications, as they accelerate computer vision algorithms and convolutional neural networks with only 1-2W. In this brief, we propose a development methodology for exploiting the full potential of the VPU heterogeneity and providing sufficient acceleration within their restricted power envelope. Based on this methodology, we demonstrate the development paradigm on the Myriad VPUs and report experimental results from the implementation of demanding image processing kernels.
Numerical evaluation of shot noise using real-time simulations

We present a method to determine the shot noise in quantum systems from knowledge of their time evolution - the latter being obtained using numerical simulation techniques. While our ultimate goal is the study of interacting systems, the main issues for the numerical determination of the noise do not depend on the interactions. To discuss them, we concentrate on the single resonant level model, which consists in a single impurity attached to non-interacting leads, with spinless fermions. We use exact diagonalisations (ED) to obtain time evolution, and are able to use known analytic results as benchmarks. We obtain a complete characterization of finite size effects at zero frequency, where we find that the finite size corrections scale $\propto G^2$, $G$ the differential conductance. We also discuss finite frequency noise, as well as the effects of damping in the leads.
Simulation of quantum spin dynamics by phase space sampling of Bogoliubov-Born-Green-Kirkwood-Yvon trajectories

A numerical method, suitable for the simulation of the time evolution of quantum spin models of arbitrary lattice dimension, is presented. The method combines sampling of the Wigner function with evolution equations obtained from the Bogoliubov-Born-Green-Kirkwood-Yvon (BBGKY) hierarchy. Going to higher orders of the BBGKY hierarchy allows for a systematic refinement of the method. Quantum correlations are treated through both, the Wigner function sampling and the BBGKY evolution, bringing about highly accurate estimates of correlation functions. The method is particularly suitable for long-range interacting systems, and we demonstrate its power by comparing with exact results as well as other numerical methods. As an application we compute spin squeezing in a two-dimensional lattice with power-law interactions and a transverse field, which should be accessible in future ion trap experiments.
Study of a Proposed Spectral-based Approach for Facility Location in Tree Topologies

The wide proliferation of smart devices in the emerging Internet of Things (IoT) networking environment necessitates a revisit to the facility location problems, i.e., where to locate a certain service facility in order to minimize a corresponding cost function (e.g., minimize energy consumption in wireless sensor network by selecting a suitable location for the sink). To address this problem for a single facility, a spectral-based approach is presented in this paper for tree topologies considering the principal eigenvector of the distance matrix and the aggregate traffic loads. It was observed using simulation that even for those cases that the proposed approach does not produce the optimal location (i.e., the solution of the median problem), the corresponding cost is still close to the minimum.
A Deep Learning Approach to Identify Local Structures in Atomic‐Resolution Transmission Electron Microscopy Images

Recording atomic‐resolution transmission electron microscopy (TEM) images is becoming increasingly routine. A new bottleneck is then analyzing this information, which often involves time‐consuming manual structural identification. A deep learning‐based algorithm for recognition of the local structure in TEM images was developed, which is stable to microscope parameters and noise. The neural network is trained entirely from simulation but is capable of making reliable predictions on experimental images. The method is applied to single sheets of defected graphene, and to metallic nanoparticles on an oxide support.
Automated Classification of VMT Pathology from Optical Coherence Tomography B-scans

The research presented in this article aims to determine the possibility of identifying macular pathologies from optical coherence tomography (OCT) images. Early detection of a pathology in the human eye retina is beneficial for patient recovery. Disorders commonly evaluated in clinical practice and research are drusen in age-related macular degeneration (AMD), diabetic macular edema (DME), and choroidal neovascularization (CNV). Our work extends this set of pathologies to include the pathology associated with improper posterior vitreous detachment, namely vitreomacular traction (VMT). Experimental research was based on single line OCT B-scans of healthy eyes and eyes with the investigated pathologies. AMD, DME and CNV images were taken from a public OCT dataset for retina classification, while healthy subjects and VMT images were acquired using an Avanti Optovue device. The accuracy results of more than 97% show the possibility of automatic preliminary diagnosis and differentiation between macular pathologies.
Research on Environment Parameter Estimation Based on Regularization Method in RSSI Positioning

The wireless signal will have a lot of loss in the process of propagation, and there is a certain relationship between the degree of signal loss and the propagation distance. In the indoor positioning algorithm, the Received Signal Strength Indication(RSSI)-based ranging positioning algorithm uses this principle to locate the target. For the ranging algorithm based on RSSI, this paper first improves the model of the algorithm, and then proposes a regularized least squares approach to estimate the unknown parameters $A$ and $\gamma$ in the model. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments.
Medical Big Data Statistical Management System Based on Decision Tree Algorithm

In some hospitals, it is often seen that because there are more patients and fewer doctors, the attending doctor often rushes between the patient's wards to query the patient's physiological data, or the nurse is responsible for recording the patient's physiological data and reporting it to the doctor. The work is very labor-intensive, and the data seen by the doctor is not real-time data, so the judgment of the patient's condition is not real-time and effective. To this end, this paper develops a medical big data statistical management system, which collects, counts, and stores patients' medical information in the system's management database for centralized management and facilitates query. For the storage of medical big data(BD), this paper uses a distributed cache framework to store medical data information, and implements distributed data processing through the C4.5 algorithm of the decision tree(DT) algorithm. The experimental results of large-scale(LC) medical data processing based on the C4.5 algorithm are also The data processing efficiency of the algorithm is illustrated, which provides great convenience for medical data management.
A 1.2V Core Switched Geometry Schmitt Trigger Relaxation Oscillator

Relaxation oscillators can provide a large frequency range whilst maintaining low power consumption and reduced area. The main drawback of most of the Schmitt trigger-based oscillators is the supply voltage switching point variation, which has a profound influence on the output frequency spread. The present work analyses a low voltage core, switched geometry Schmitt trigger relaxation oscillator, with local, low voltage supply, provided by a low dropout internal regulator. The power consumption of the schematic is optimized by using the low voltage core, while the local regulator accommodated the schematic functionality for the supply voltage range between 1.6 and 5.5V.
Low-cost open-source flow velocity sensor for droplet generators

Droplet microfluidics enables the manipulation of small sample and reagent volumes in physically discrete droplets, each acting as a separate microreactor. This opens up new analytical routes in cell analysis and chemical synthesis. Flow regulation, and in particular, flow sensing, is vital to enable controlled and automated droplet generation. However, precision flow sensing technology is typically complex and expensive, or lacking in certain performance metrics. This work targets development and demonstration of an optical flow velocity/flow rate sensor, which is compact and low-cost, communicates and functions wirelessly, and has performance metrics comparable to currently available flow sensors. The demonstrated experimental flow sensor prototype was evaluated using commercially available pressure sensors in conjunction with an in-house developed dual-channel pressure pump at various flow rates. Based on the evaluation, the sensor had a measurement resolution of 0.6 µl/min, measurement accuracy of 0.76–13.03 % at a total cost of ~ € 100.
STELLAR VELOCITIES IN THE CARINA, FORNAX, SCULPTOR, AND SEXTANS dSph GALAXIES: DATA FROM THE MAGELLAN/MMFS SURVEY

We present spectroscopic data for individual stars observed from 2004 March through 2008 August as part of our Michigan/MIKE Fiber System (MMFS) survey of four dwarf spheroidal (dSph) galaxies: Carina, Fornax, Sculptor, and Sextans. Using MMFS at the Magellan/Clay Telescope at Las Campanas Observatory, we have acquired 8855 spectra from 7103 red-giant candidates in these Galactic satellites. We list measurements of each star's line-of-sight velocity (median error ±2.1 km s−1) and spectral line indices for iron and magnesium absorption features. We use globular cluster spectra to calibrate the indices onto standard [Fe/H] metallicity scales, but comparison of the resulting metallicities with published values suggests that the MMFS indices are best used as indicators of relative, not absolute, metallicity. The empirical distributions of velocity and spectral indices also allow us to quantify the amount of contamination by foreground stars. In a companion paper, we develop an algorithm that evaluates the membership probability for each star, showing that the present MMFS sample contains more than 5000 dSph members, including 774 Carina members, 2483 Fornax members, 1365 Sculptor members, and 441 Sextans members.
An Optimized Adaptive Learning Approach Based on Cuckoo Search Algorithm

The rapid expansion of MOOCs (massive open online courses) allows learners to benefit from these courses by removing the barriers that obstruct the right to an open high-quality education. The courses offered on MOOC platforms are often free which has revolutionized this mode of distance learning, especially with the restrictions imposed by the advent of the COVID-19 pandemic. However, even though the number of registrants to MOOCs is quite considerable, only 10% of the learners complete the MOOC and obtain a certification. This phenomenon leads us to dig deeper to wonder about the means to avoid the high dropout rate of learners in such platforms. For this purpose, we suggest in this paper two complementary systems: a preventive system coupled with a proactive system to personalize the learners' pathways according to their specific needs and prior knowledge. The optimization of the pathways will be handled using a metaheuristic optimization algorithm called: Cuckoo Search Algorithm.
Concealed Swarm of Micro-swimmers

Here we show that micro-swimmers can form a hydrodynamically concealed swarm through synergistic cooperation in suppressing one another's disturbing flows. We then demonstrate how such a concealed swarm can actively gather around a favorite spot, point toward a target, or track a desired trajectory in space, while minimally disturbing the ambient fluid. Quenching flow signatures and thus shrinking the associated detection region, by swarming in concealed modes, can potentially have a significant impact on trophic transfer rates among a broad range of aquatic organisms.
Decision-Making Approaches for Complex Applications using Deep Learning Models

Decision-making (DM) is the domain that can develop any type of application. Applications such as health care systems, popular product prediction on e-commerce applications, filtering of spam emails, the behavior of the user based on online purchases, etc. Machine learning (ML) algorithms are mainly used to find complex patterns from several domains. Disease prediction is one of the tasks that can work on various diseases to predict accurate diseases according to the given dataset. This task can work efficiently on text data and image data. Many image datasets such as CT scans and MRI images are used to detect the tumors from the brain scan images. Tumors are many types and all the tumors are not cancerous and some tumors are noncancerous. Deep Learning (DL) is the domain that can work better on complex datasets such as decision making. This research is mainly focused on developing the decision-making in several domains such as disease prediction, E-commerce sites, predicting autonomous vehicles (AVs), etc. The performances of several algorithms are also shown in this work.
Quantum interference and light polarization effects in unresolvable atomic lines: Application to a precise measurement of the6,7LiD2lines

We characterize the effect of quantum interference on the line shapes and measured line positions in atomic spectra. These effects, which occur when the excited state splittings are of order the excited state line widths, represent an overlooked but significant systematic effect. We show that excited state interference gives rise to non-Lorenztian line shapes that depend on excitation polarization, and we present expressions for the corrected line shapes. We present spectra of 6,7 Li D lines taken at multiple excitation laser polarizations and show that failure to account for interference changes the inferred line strengths and shifts the line centers by as much as 1 MHz. Using the correct lineshape, we determine absolute optical transition frequencies with an uncertainty of <= 25kHz and provide an improved determination of the difference in mean square nuclear charge radii between 6 Li and 7 Li. This analysis should be important for a number of high resolution spectral measurements that include partially resolvable atomic lines.
Generalised constraints on the curvature perturbation from primordial black holes

Primordial black holes (PBHs) can form in the early Universe via the collapse of large density perturbations. There are tight constraints on the abundance of PBHs formed due to their gravitational effects and the consequences of their evaporation. These abundance constraints can be used to constrain the primordial power spectrum, and hence models of inflation, on scales far smaller than those probed by cosmological observations. We compile, and where relevant update, the constraints on the abundance of PBHs before calculating the constraints on the curvature perturbation, taking into account the growth of density perturbations prior to horizon entry. We consider two simple parameterizations of the curvature perturbation spectrum on the scale of interest: constant and power-law. The constraints from PBHs on the amplitude of the power spectrum are typically in the range 10 −2 10 −1 with some scale dependence.
THE VELA PULSAR AND ITS LIKELY COUNTER-JET IN THE Ks BAND

We report the first high spatial resolution near-infrared (near-IR) imaging of the Vela pulsar in the Ks band obtained with the new adaptive optics system recently mounted on the Gemini-South telescope. For the first time, we have firmly detected the pulsar in this band with Ks ≈ 21.8 mag, and have resolved in detail an extended feature barely detected previously in the immediate vicinity of the pulsar in the JsH bands. The pulsar Ks flux is fully consistent with the extension of the flat optical spectrum of the pulsar toward the IR and does not confirm the strong IR flux excess in the pulsar emission suggested earlier by the low spatial resolution data. The extended feature is about two times brighter than the pulsar and is likely associated with its X-ray counter-jet. It extends ∼2″ southward of the pulsar along the X-ray counter-jet and shows knot-like structures and a red spectrum.
A Real-Time Approach to Classify the Water Quality of the River Ganga at Mehandi Ghat, Kannuaj

Only 0.3 percent of the total water on Earth is available in rivers and ponds, and the majority of it is polluted to the point where drinking it directly can cause disease. In this paper, we will identify the quality of the river Ganges and check if it is portable and healthy. We aim to classify the water on some parameters using different classification algorithms, such as Random Forest, which is a supervised machine learning algorithm. This model's accuracy is around 99 percent, which is far superior to other approaches taken for water quality prediction.
Thermal Casimir-Polder interaction of different atoms with graphene

The thermal correction to the energy of Casimir-Polder interaction of atoms with a suspended graphene membrane described by the Dirac model is investigated. We show that a major impact on the thermal correction is made by the size of the gap in the energy spectrum of graphene quasiparticles. Specifically, if the temperature is much smaller than the gap parameter (alternatively, larger or of the order of the gap parameter), the thermal correction is shown to be relatively small (alternatively, large). We have calculated the free energy of the thermal Casimir-Polder interaction of atoms of He, Na, Rb, and Cs with graphene described by both the hydrodynamic and Dirac models. It is shown that in exact computations using the Dirac model, one should use the polarization operator at nonzero temperature. The computational results for the Casimir-Polder free energy obtained in the framework of hydrodynamic model of graphene are several times larger than in the Dirac model within the separation region below 2$\mu$m. We conclude that the theoretical predictions following from the two models can be reliably discriminated in experiments on quantum reflection of different atoms on graphene.
Analysis strategies for general spin-independent WIMP-nucleus scattering

We propose a formalism for the analysis of direct-detection dark-matter searches that covers all coherent responses for scalar and vector interactions and incorporates QCD constraints imposed by chiral symmetry, including all one- and two-body WIMP-nucleon interactions up to third order in chiral effective field theory. One of the free parameters in the WIMP-nucleus cross section corresponds to standard spin-independent searches, but in general different combinations of new-physics couplings are probed. We identify the interference with the isovector counterpart of the standard spin-independent response and two-body currents as the dominant corrections to the leading spin-independent structure factor, and discuss the general consequences for the interpretation of direct-detection experiments, including minimal extensions of the standard spin-independent analysis. Fits for all structure factors required for the scattering off xenon targets are provided based on state-of-the-art nuclear shell-model calculations.
Modeling and Simulation of Speed Regulating System for Paute Molino Hydroelectric Power Plant and Commissioning Tests Contrasting

The main objective of this project is to create a model for the new speed regulator for the Phase C units of the Paute Molino Power Plant. This regulator model is used to conduct stability studies of the Ecuadorian electric system. Development of the turbine transfer functions is presented as well as their modification to have an adequate response of a Pelton turbine and its generator, including load effect, PID controllers, injectors and deflectors positioning provided by the manufacturer. A fundamental part of the new speed regulator is the parameters identification since it ensures a proper model response. The particle swarm optimization (PSO) is used to consider a nonlinear turbine model to configure PID controller gains within given limits. Finally, a comparison between simulation and commissioning tests results are presented to confirm the simulated Pelton turbine model based on field test responses.
An Energy-Efficient Three-Independent-Gate FET Cell Library for Low-Power Edge Computing

With the increasing demand for compute-intensive applications for IoT devices, new technologies that enable a power reduction at the device-level are needed to improve energy savings at the system-level. Unfortunately, the scaling of standard CMOS technologies is not as fast as the scaling of computing performance, which leads to the so-called "power wall". The Three-Independent-Gate Field-Effect Transistor (TIGFET) is a promising technology that enhances the device functionality to create more compact logic gates and provide silicon-nanowire structures that meet the requirements of low leakage power systems. However, the evaluation of complex designs is currently limited to the intrinsic model of the transistor and does not consider the parasitic effects of cell layouts. In this paper, we propose a standard cell library for 10-nm silicon-nanowire TIGFET devices, including combinational and sequential gates, to evaluate a production RISC-V core targeting low energy consumption budget. After synthesis, the core achieves 4 × lower energy consumption up to a frequency of 340 MHz compared to an equivalent low-power 12-nm FinFET technology node.
Minimal Model for an Unbalanced Holographic Superconductor

We describe the simplest holographic model for an s-wave unbalanced superconductor in 2+1 dimensions. We study its phase diagram and linear response features with particular attention to the possibility of spatially modulated phases (LOFF) and mixed spin-electric properties. The normal phase of the model at hand allows us to analyze a strong-coupling generalization of Mott two-current model for spintronic systems; the superconducting phase features an interesting DC spin-superconductivity without spin-symmetry breaking.