Miracle-dz/UniFAR-FTUs huggingface dataset:
First measurement of the ratio of branching fractions B(Λ0b→Λ+cμ−ν¯μ)/B(Λ0b→Λ+cπ−)

This article presents the first measurement of the ratio of branching fractions B(Λ 0 b → Λ + c μ - v μ )/B(Λ 0 b → Λ + c π - ). Measurements in two control samples using the same technique β(B 0 → D + μ - v μ )/B(B 0 → D + π - ) and B(B 0 → D*(2010) + μ-V μ )/B(B 0 → D*(2010) + π - ) are also reported. The analysis uses data from an integrated luminosity of approximately 172 pb -1 of pp collisions at √s = 1.96 TeV, collected with the CDF II detector at the Fermilab Tevatron. The relative branching fractions are measured to be B(Λ 0 b →Λ + cμ - v μ )-B(Λ 0 b Λ + c π - ) = 16.6 ± 3.0(stat) ± 1.0(syst) +2.6 -3.4 (PDG) ± 0.3(EBR), B(B 0 →D + μ - v μ )-B(B 0 →D + π - ) = 9.9 ± 1.0(stat) ± 0.6(syst) ± 0.4(PDG) ± 0.5(EBR), and B(B 0 →D*(2010)+μ - v μ )-B(d 0 →D*(2010) + π - ) =16.5 ± 2.3(stat) ± 0.6(syst) ± 0.5(PDG) ± 0.8(EBR). The uncertainties are from statistics (stat), internal systematics (syst), world averages of measurements published by the Particle Data Group or subsidiary measurements in this analysis (PDG), and unmeasured branching fractions estimated from theory (EBR), respectively. This article also presents measurements of the branching fractions of four new Λ 0 b semileptonic decays: Λ 0 b → Λ c (2595) + μ - v μ , Λ 0 b → Λ c (2625) + μ - v μ , Λ 0 b → Σ c (2455) 0 π + μ - v μ , and Λ 0 b → Σ c (2455) ++ π - μ - v μ , relative to the branching fraction of the Λ 0 b → Λ + c μ - v μ decay. Finally, the transverse-momentum distribution of Λ 0 b baryons produced in pp collisions is measured and found to be significantly different from that of B 0 mesons, which results in a modification in the production cross-section ratio σΛ 0 b /σB0with respect to the CDF I measurement.
The Stratopause Semiannual Oscillation in the Berlin Troposphere–Stratosphere–Mesosphere GCM

Abstract The tropical stratopause semiannual oscillation (SAO) in the Berlin troposphere–stratosphere–mesosphere general circulation model (TSM GCM) is investigated. The model is able to produce a semiannual oscillation that is properly located in time and space in comparison with observations; however, the westerly phase is 10–15 m s−1 too weak while the second easterly phase of the year is about the same amount too strong. A case study is performed to examine the westerly forcing of the SAO in detail. At 1 hPa in one particular March, when the strongest westerly acceleration of the year takes place, only 18% of the forcing is due to the dissipation of Kelvin waves, whereas 72% of the forcing is due to the dissipation of other slow eastward propagating planetary-scale waves and 10% is due to the dissipation of eastward propagating inertio–gravity waves. Throughout the year the Kelvin wave contribution to the total body force is below 50%. The modeled Kelvin waves are dissipated at higher altitudes than o.
On numerical solution of parameter-dependent convex differential inequalities

This paper is concerned with a numerical solution for a class of parameter-dependent convex differential inequalities (PDCDIs) formulated by including parameter-dependent LMIs, which have many applications in control system analysis and synthesis. In order to solve PDCDIs involving infinite inequalities corresponding to the values of the parameter, a procedure is presented to derive a finite set of inequalities from a given PDCDI.
Flow Features and Self-Noise of Airfoils Near Stall or in Stall

A detailed experimental and numerical investigation of the ﬂow features and the self-noise of airfoils near stall and in stall has been achieved. Several airfoils of diﬀerent shapes have been tested at diﬀerent speeds and diﬀerent angles-of-attack. The stall noise is shown to possibly have two diﬀerent regimes, ﬁrst a light stall and then a deep stall, with distinct noise features. Both are seen in the low frequency range and are of dipolar nature. Yet, the deep stall has extra narrow peaks which could be associated with shear layer instabilities and large scale vortex shedding. Experiments with a vertical nozzle (large aspect ratio) and a horizontal one (small aspect ratio) have also stressed the strong installation eﬀects on the stall noise features. The two setups yield diﬀerent low frequency narrow peaks for characteristic Strouhal numbers based either on the chord length or the separation bubble thickness. Possible interaction with the jet have been identiﬁed in the vertical setup. In parallel, both RANS and LES results have been obtained on the long-span NACA0012 airfoil in the large aspect ratio setup. The strong interaction with the jet has been conﬁrmed and both the experimental wall pressure coeﬃcient and the characteristic Strouhal numbers have been matched. The spanwise boundary conditions are also found to strongly modify the ﬂow ﬁeld and to mimic both types of experiments with small and large aspect ratios. Finally an analytical model has been developped for the low frequency range characteristics of the stall noise. It is based on Curle’s analogy for a dipole acoustically compact in the chordwise direction. It is shown to correctly capture the humps seen in the experimental spectrum provided the experimental wall pressure spectra and spanwise coherence lengths are used.
Extracting the Extreme Mode Operation of Coal Mills Using the Gaussian Mixture Model Method

Coal mills have been the heart of running coal-fired power stations. Coal mills must operate safely and normally to avoid unnecessary unit load losses and complete shutdowns. It has been desirable to have a model that can detect anomalous behavior of a system. The objective of this paper is to be able to isolate unsafe operations or clusters from an unlabeled coal mill dataset. In this paper, Gaussian Mixture Model (GMM) algorithm was implemented on a two-year dataset from an MPS-type vertical spindle coal mill of a power plant located in South Africa to label and classify them into different clusters. To implement GMM, one must assume the number of clusters in advance. Cluster performance evaluation criteria were selected to confirm and evaluate the assumed number of clusters. Furthermore, the operating behavior of coal mills was considered. GMM algorithm was found to fit the system dataset well, and it is good at isolating safe and unsafe modes of operation. This depends on finding the right balance in choosing the appropriate number of clusters and features.
Swarm algorithms with chaotic jumps for optimization of multimodal functions

In this article, the use of some well-known versions of particle swarm optimization (PSO) namely the canonical PSO, the bare bones PSO (BBPSO) and the fully informed particle swarm (FIPS) is investigated on multimodal optimization problems. A hybrid approach which consists of swarm algorithms combined with a jump strategy in order to escape from local optima is developed and tested. The jump strategy is based on the chaotic logistic map. The hybrid algorithm was tested for all three versions of PSO and simulation results show that the addition of the jump strategy improves the performance of swarm algorithms for most of the investigated optimization problems. Comparison with the off-the-shelf PSO with local topology (l best model) has also been performed and indicates the superior performance of the standard PSO with chaotic jump over the standard both using local topology (l best model).
Constructing the Evaluation Index System of Chinese-Portuguese Machine Translation using the Delphi and Analytic Hierarchy Process Methods

Quality assessment is an important part of ensuring the quality and long-term development of machine translation. There is still much room for research into the evaluation of machine translation from Chinese to Portuguese, and no specific evaluation metrics have yet been developed. This study uses the Delphi method and Analytic Hierarchy Process (AHP) to construct an index system of Chinese-Portuguese Machine Translation and analyzes the relative index weight. The system includes four first-level indicators and nine second-level indicators. The weight of primary indicators, from high to low, is "Accuracy, fidelity" (52.5%), "Readability" (22.9%), "Fluency" (16.8%), and "Acceptability & Usability" (7.8%). The secondary indicators with the highest weight of each primary indicator are "Correction", "Lexicon", "Readability", and "Utility". AHP was used to transform the qualitative problems in evaluating Chinese-Portuguese Machine Translation into the quantitative problem in order to provide a theoretical basis and practical guidance for the future manual evaluation of Chinese-Portuguese Machine Translation, and for the eventual modification of automatic machine translation evaluation methods.
DDF-GAN: A Generative Adversarial Network with Dual-Discriminator for Multi-Focus Image Fusion

Multi-focus image fusion can overcome the issues that optical lens imaging cannot focus multiple targets simultaneously due to the depth of field limitation. In this paper, we propose a generative adversarial network (DDF-GAN) which consists of a generator and two discriminators to directly generate fused images without decision maps and post-processing. In training process, the source all-in-focus image and the fused image generated by generator are used as input to one of the dual discriminators. Meanwhile, the gradient map of the fused image and the source gradient map of the source all-in-focus image are used as input to another discriminator. An adversarial relationship is established to enhance texture details of fused image. In addition, we create a data set and use it as the main training set for the proposed model. Abundant experiments were carried out to verify the availability of our method. Experimental results prove that our method has advantages in subjective visual perception of human and quantitative measurement.
Traffic Flow Prediction Using Deep Learning Models

For the effective implementation of Intelligent Transportation Systems (ITS), accurate and timely traffic flow information is critical. Traffic data has exploded in recent years, ushering in the era of big data. The main issue of a traffic flow prediction system is determining how to build an adaptive model based on past data. Existing traffic flow forecast approaches rely on shallow learning models, which are unsatisfactory for many real-world applications. This situation prompts us to reconsider traffic flow prediction using deep learning models and large amounts of traffic data. In this paper, we present the Long Short-Term Memory (LSTM) model, Gated Recurrent Unit (GRU) model, Stacked Auto Encoder (SAE), and an Auto-Regressive Integrated Moving Average (ARIMA) model, all of which are deep learning methods. These techniques are applied to real-world traffic big data collected by performance measurement systems. Compared to other deep learning and shallow machine learning prediction networks, experimental results demonstrate that the gated recurrent unit model is more applicable and performs better.
Authentication Scheme Based on Non-Interactive Zero-Knowledge Proof for Mobile Health

Digital technologies, such as wireless body area networks (WBANs) for mobile health (mHealth) applications, are expected to enhance the quality of the public health care system. Although mHealth can improve patients' quality of health by offering outpatient real-time health monitoring systems instead of being stuck in the hospital all the time to monitor chronic diseases. The major challenge in adopting mHealth is data security and privacy. The health data routed on the internet from the patient monitoring device to the health center for remote monitoring is vulnerable to confidentiality attacks. To handle this issue, we present an authentication scheme based on non-interactive zero-knowledge proof (NIZKP), which issues certificates and authenticates monitoring devices each time performing transactions without revealing sensitive information. Our authentication scheme provides a high level of security with a low computational cost, which is lightweight for WBANs.
IMRG: Impedance Matching Oriented Receiver Grouping for MIMO WPT System

In recent years, multiple-input multiple-output (MIMO) technology has been imported into magnetic resonance coupled (MRC) enabled wireless power transfer (WPT) systems for concurrent charging of multiple devices. Besides the traditional performance optimization methods (e.g., TX current scheduling, system frequency adjustment, etc.), receiver (RX) grouping will also severely influence the achieved power- delivered-to-load (PDL). In this paper, we investigate the optimal RX grouping issue to maximize the proportional fairness of RX achieved PDL, which is a joint optimization problem involving RX grouping and time-slice allocation among groups. By decoupling the problem, we solve the group generation sub-problem with a impedance-matching based greedy algorithm to generate potential RX group candidates, and we further solve the time slice allocation sub-problem with a genetic algorithm to distribute resources among group candidates. We prototype the proposed system, denoted as IMRG, and conduct extensive experiments to evaluate the performance. The experimental results validate the effectiveness of the proposed algorithm, e.g., IMRG achieves average 59.6% PDL improvement through RX grouping compared to the simultaneous charging scheme.
Unconstrained Face Recognition System using Deep Neural Network: A Review

The human face is one of the easiest bio-metric modality that does not require any special imaging techniques or subject cooperation. This benefits a wide range of applications, from automatic image tagging in social media structures to critical applications such as border control and surveillance forensics. Different other modalities of bio-metrics are also accurate and sophisticated, but they can hardly replace the need for facial bio-metrics. The success of face recognition technology is limited to unconstrained facial image acquisition scenarios. In the unconstrained scenarios acquiring of face images is not well controlled and subjects may be uncooperative, hence, degrading the performance of face recognition systems. Such confounding factors include variations in facial pose and expression, nonuniform illumination, occlusion and low image resolution. This paper details the state-of-the-art facial recognition techniques in uncontrolled environment and compare on the basis of their accuracy.
The H I Distribution and Dynamics in Two Late-Type Barred Spiral Galaxies: NGC 925 and NGC 1744

We present H I observations of NGC 925 and NGC 1744, two late-type barred spiral galaxies that are quite comparable in mass and optical morphology. NGC 925 is a very asymmetric galaxy with a strong spiral arm in the south and flocculent arms in the north. The rotation curves of the east and west sides of the galaxy are consistent with this asymmetry as well. In addition, NGC 925's dynamical center may be slightly offset (21.″5, or ∼1 kpc) from its optical center. The ratio of the H I to optical diameter of NGC 925 is small compared with other galaxies. We detected a ∼107 M⊙ H I cloud and streamer, with little or no stellar component, apparently interacting with NGC 925. While the interaction between NGC 925 and the cloud may be responsible for the observed asymmetries, given the weakness of the interaction we postulate that NGC 925 has suffered other gravitational encounters over the past few gigayears. NGC 1744, on the other hand, is an unperturbed galaxy having a textbook example of a velocity field for a disk galaxy. It has a symmetric, albeit weak, spiral structure. Both NGC 925 and NGC 1744 have small streaming motions (∼10 km s-1) associated with the bar and spiral arms.
Comparative Analysis of Recommendation Systems based on Medical Data Sets

In today’s world, a number of most common diseases such as DM (Diabetes Mellitus) for which not assured medicine has searched yet, the capable method for these types of patients for the survival is to take care and proper diet. Fasting Blood Sugar is one of the vital parameters of diabetes mellitus. In the health care system, numerous data of the patients have analyzed that help to obtain insights and assist the estimate of the diseases. Hence, there is a need for an intelligent scheme for estimation of the health situation by analyzing the standard of living, health check reports, and communal activities. However, the recommendation system is becoming the main framework for health care applications. Moreover, fitness intelligent schemes have become a necessary tool in the decision-making approach in health areas. Conversely, people use an online system for understanding the physical condition. Therefore, health recommendation schemes have estimated better results in diagnosis, medical insurance, medical-based management techniques, and other medicine based on the health profile of the patient. This paper provides comparative analysis of various recommendation methods such as MLP, RNN, LSTM, SVM, LR, and NB. These recommendation methods are used for the recommended diseases with Medical Data Sets.
What Does It Take to Work in the Translation Profession in Canada in the 21st Century? Exploring a Database of Job Advertisements

This article contains an investigation into the translation profession in Canada in the 21st century. The aim of this study is to investigate translation from the perspective of those who employ translators by analyzing a database of job advertisements for a variety of translation-related positions that were collected between January 2000 and December 2002. Spurred on by the effects of globalization, the language industry is in a period of change. Based on empirical evidence collected from the database, this article attempts to evaluate the current state of the profession in Canada and to determine what employers are seeking.
WP-CamemBERT for next location Prediction

CamemBERT is a Natural Language Processing (NLP) model. The current paper introduced a combination of Bertwordpiece tokenizer and CamemBERT that we named WP-CamemBERT, to solve the next location prediction problem. Substantially, we sought to investigate the performance of our proposed model WP-CamemBERT compared to famous models in the domain that are: Markov, Long-Short Term Memory (LSTMs), Convolutional Neural network (CNN2D), and Support Vector Machine (SVM) models. In order to build the next location prediction model, we used a dataset of mobile users made available through the CRAWDAD project. The results showed that WP-Camembert gave significant improvement in the prediction accuracy and surpassed the state of the art models that are: Markov, LSTMs, CNN2D, and SVM. Particularly, WP-CamemBERT enhanced the next location prediction accuracy by at least 3.2%.
Relationship between Potential Vorticity Tendency and Tropical Cyclone Motion

Abstract This paper proposes a consistent conceptual framework to explain tropical cyclone (TC) motion based on the concept of potential vorticity tendency (PVT) and to verify this framework based on analyses of different observational datasets. The framework suggests that a TC is likely to move toward an area of maximum wavenumber-1 (WN1) PVT, which is mainly contributed by the corresponding WN1 components of potential vorticity (PV) advection and diabatic heating (DH). The PV advection process consists of advection of symmetric PV by the asymmetric flow [AASPV, which includes, but is not limited to, the environmental “steering flow” and the beta-induced circulation (the so-called ventilation flow)] and the advection of asymmetric PV by the symmetric flow (SAAPV). The asymmetric PV includes any asymmetry in the TC circulation, the beta gyres and contributions from asymmetric convective heating. The modification of PVT by the DH process depends on the vertical gradient of convective heating and the coupli.
Tracking Risks from Multi-path TDoA-based Localization in Wireless Communications

New antenna technologies like phased array antennas are crucial to enhance high-speed data transmission in 5G and 6G high-frequency communications (mmWave/Terahertz). By exploiting spatial-temporal beamforming space (e.g., angle of arrival, angle of departure, time of arrival), directional antenna technologies can enable precise radio positioning. The feature has tremendous potential to be used in many futuristic applications, such as behavioral monitoring or assisting rescue teams moving inside a dark building. However, besides that positive side, precise radio positioning potentially poses significant tracking risks to mobile users. In this work, we demonstrate a case study of tracking a user in a restricted access building based on passively received signals. We then build an efficient mechanism to regenerate the trajectory of a target device by exploiting the characteristics of directional signals. We also exploit the capability of simultaneous localization and mapping using multipath channel information. Through theoretical analysis and simulation, we found that tracking risks of radio positioning techniques are real and challenging to resolve.
Simultaneous measurement of free-surface velocity and surfactant concentration via a common laser probe

A new technique was developed to simultaneously measure the velocity and surfactant surface concentration at an air/water interface via a common laser probe. The surfactant concentration is measured using the nonlinear optical technique of reflected second-harmonic generation (SHG), utilizing an Nd:YAG laser. The transmitted portion of the same laser beam provides illumination for velocity measurements at the interface using the technique of boundary-fitted digital particle image velocimetry (DPIV). The combined SHG and DPIV system was utilized to scan along the free surface of a uniform water flow with a surface barrier trapping hemicyanine, an insoluble monolayer. Measurements were made downstream of the Reynolds ridge, which marks the contamination front and the leading edge of a free-surface boundary layer.
SCN-SAM: A Modified Self-Cure Network for Facial Expression Recognition Under Face Masks

Due to COVID-19, wearing masks has become more common. However, it is challenging to recognize expressions in the images of people wearing masks. In general facial recognition problems, blurred images and incorrect annotations of images in large-scale image datasets can make the model’s training difficult, which can lead to degraded recognition performance. To address this problem, the Self-Cure Network (SCN) effectively suppresses the over-fitting of the network to images with uncertain labeling in large-scale facial expression datasets. However, it is not clear how well the SCN suppresses the uncertainty of facial expression images with masks. This paper verifies the recognition ability of SCN on images of people wearing masks and proposes a self-adjustment module to further improve SCN (called SCN-SAM). First, we experimentally demonstrate the effectiveness of SCN on the masked facial expression dataset. We then add a self-adjustment module without extensive modifications to SCN and demonstrate that SCN-SAM outperforms state-of-the-art methods in synthetic noise-added FER datasets.