Miracle-dz/UniFAR-FTUs huggingface dataset:
Exchange Rate Forecasting in Money Market

Changes in currency exchange rates can impact a country's economy, making it important to carefully consider and anticipate foreign currency exchange rates in the money market. Therefore, accurately predicting FOREX fluctuations is a significant concern. Convolutional neural networks (CNN) and random forest regression have been suggested as prediction algorithms for foreign exchange. The efficiency of the approach is assessed by forecasting the worth of the New Zealand dollar against the US dollar (NZU/USD)for 2, 4, 6, 8, 10, 12, and 14 weeks into the future. The best weight for the suggested Model is calculated utilizing the adaptive learning rate method (ADAM) optimization approach. The new approach provides an additional precise way to estimate foreign exchange rates (Forex) based on the estimation criteria. The CNN model with the layer for random forest regression performs better than the other three models.
Maxwell's inductions from Faraday's induction law

In article 598 of his Treatise on Electricity and Magnetism (Maxwell 1891 A Treatise on Electricity and Magnetism (Oxford: Clarendon)), Maxwell gives a seminal analysis of Faraday's law of electromagnetic induction. We present a detailed account of the analysis, attempting to reconstruct the missing steps, and discuss some related matters.
Interpretable Diagnosis of Glaucoma Based on Attention Mechanism and Embedded Class Activation Map

Glaucoma is a group of heterogeneous neurodegenerative diseases, which has become the main cause of irreversible blindness worldwide, so the early screening of glaucoma has become crucial. Although the methods of visualizing the feature maps of convolutional layers increase the transparency of deep learning models to a certain extent, these methods often embed high-level connectivity constraints, which leads to the fact that the interpretable diagnosis of glaucoma can only obtain interpretation results from high-dimensional information. The proposed GAMNet based on attention mechanism and embedded class activation map not only solve the problem that the feature information to be concerned under multi-scale cannot be accurately focused by attention, but also release the constraints of high convolutional layer features on the interpretation results of the model from the perspective of fusion features. Firstly, a dynamic receptive field module is introduced on the basis of the multi-scale feature pyramid structure, and the embedded coordinate attention mechanism is used to effectively capture long-distance dependencies, so as to construct a semantically rich dynamic multi-scale feature pyramid for accurate and efficient diagnosis of glaucoma fundus images. Secondly, the hierarchical gradient function of gradient-weighted class activation map is established, and the class activation map after feature fusion is used to provide a more detailed attention map, which provides a decision-making basis for glaucoma diagnosis. GAMNet provides the evidence activation map, compared with Garbor, Wavelet, GRI and Superpixel in the performance of optic disc segmentation, the results show that GAMNet classification is accurate, and provides the evidence activation map. It not only shows diagnostic results, but also has good interpretation performance without high-level connectivity constraints.
Convergent Waveform Relaxation Schemes for the Transient Analysis of Associative ReLU Arrays

In this circuit-theoretic paper, we establish a new result for the global convergence of the waveform relaxation (WR) algorithm in the specific context of analog associative arrays having the Rectified Linear Unit (ReLU) as an activation function. The traditional methods for proving WR convergence on generic analog circuits rely on the use of exponentially weighted norms to control the behavior of the transient waveforms for large simulation intervals. The main contribution of this paper is to show that in the particular case of analog associative ReLU arrays, WR convergence for large simulation intervals does not require exponentially weighted norms and can instead be ascertained using the common norm of uniform convergence. Using the connectivity matrix of the associativity array, a practical criterion for guaranteeing WR convergence is provided.
Optimizing Adhesion of Printed Conductive Lines on Additively Manufactured Ceramics

Optimizing electronic assemblies for more powerful systems includes the usage of temperature-stable substrates with appropriate functionalization by e.g. printing technologies. Ceramic materials like alumina show very good thermal properties and can be printed using Fused Filament Fabrication. Thus, they can be utilized for 2D and 3D substrates in electronic assemblies. In printed electronics, adhesion of inks to substrates is a crucial issue and especially challenging on ceramic substrates. This work achieved a significant increase in adhesion, using a specifically modified silver-based ink with piezojet-printing, while maintaining good electrical resistance after furnace sintering. Adhesion is measured by pull-off testing, based on ISO 4624. By application of the methods described in this paper, ceramics could become a more viable option for use in functional electronic assemblies.
A compact patch coupler using cross-slots for 5G applications

A compact hybrid patch coupler operating at 3.5 GHz for fifth generation applications is presented. By introducing cross-slots with T-shaped heads on the edges, the proposed hybrid patch coupler’s overall size is reduced significantly. The proposed coupler offers a size reduction of 79% compared to the overall size occupied by a conventional reference design. Simulation results show a successful fit of the miniature coupler such that the reflection coefficient and the isolation values are −44.27 dB and −24.35 dB at 3.5 GHz. The insertion coefficient and coupling coefficient values are −3.18 dB and −3.88 dB for the same resonance frequency, with 30% fractional bandwidth (FBW) and 89° phase difference, showing that the cross-slot patch coupler is capable of diagonally coupling the input signal to its outputs at 3.5 GHz.
Acceptance sampling plans based on truncated life tests for Burr type X distribution

Abstract In this paper, we consider acceptance sampling plans for the Burr type X distribution under a truncated lifetime test. For various confidence levels, acceptance numbersand the ratio values of the experimental time to the given and specified median life, the smallest sample size required to insure a certain median lifetime are obtained. The operating characteristic function and the associated manufacturer’s risks are also proposed. We illustrate the acceptance sampling plans with a real numerical example.
MPSDYF: Design of an augmented Multimodal technique for Prediction of Soil quality to analyze crop Diseases, Yield and recommend Fertilization operations

Prediction of crop diseases, their yield, and recommendation of fertilization for improving the yield is a multidomain task that involves collective correlation of data samples, feature extraction & selection, classification of selected features into different yield types, post-processing for recommendations generation, and feedback-based continuous optimizations. Existing models that perform these tasks either have limited accuracy, or cannot be scaled for real-time heterogenous soil types. To overcome these issues, this text proposes design of an augmented multimodal technique for prediction of soil quality to analyze crop diseases, yield and recommend fertilization operations. The proposed model uses an augmented multimodal approach that combines multiple data sources, including soil properties, weather conditions, crop information, and satellite images. Analysis of the data samples is done via a combination of Vector Autoregressive Moving Average (VARMA) with Long-Short-Term Memory (LSTM) & Gated Recurrent Unit (GRU) to provide recommendations for fertilization operations based on soil quality and crop needs (or requirements). The proposed technique initially converts collected samples into 1D flattened vectors, and extracts colour, shape and texture feature sets. These feature sets are processed via the proposed fusion of VARMA LSTM & GRU processes. To validate performance of the model, it was tested on multiple datasets, and achieved 3.5% higher accuracy, 2.9% higher precision, 2.4% higher recall, and 8.3% lower delay in predicting soil quality and analysing its impact on crop diseases and yield levels. The results of this study can help farmers and agriculture practitioners make informed decisions about soil management and fertilization operations, leading to improved crop productivity and sustainability levels.
The modified inertial relaxed CQ algorithm for solving the split feasibility problems

In this work, we propose a new version of inertial relaxed CQ algorithms for solving the split feasibility problems in the frameworks of Hilbert spaces. We then prove its strong convergence by using a viscosity approximation method under some weakened assumptions. To be more precisely, the computation on the norm of operators and the metric projections is relaxed. Finally, we provide numerical experiments to illustrate the convergence behavior and to show the effectiveness of the sequences constructed by the inertial technique.
Low-Pressure Barrier Discharge as a Source of Radiation for Spectroscopic Study of Collisional-Radiative Recombination of Doubly Charged Ions

In this paper, we describe the first results of the experiment, which made it possible to compare directly the rates of collisional-radiative recombination of Ne+ and Ne++ ions with electrons in plasma. As the main result of the work, we present the ratio of the coefficients of collisional-radiative recombination of doubly and singly charged neon ions, which is several times higher than the value predicted by the theory of the process in a purely collisional model.
Simulation of turbulent flows with the entropic multirelaxation time lattice Boltzmann method on body-fitted meshes

We propose a body-fitted mesh approach based on a semi-Lagrangian streaming step combined with an entropy-based collision model. After determining the order of convergence of the method, we analyse the flow past a circular cylinder in the lower subcritical regime, at a Reynolds number $Re=3900$ , in order to assess the numerical performances for wall-bounded turbulence. The results are compared to experimental and numerical data available in the literature. Overall, the agreement is satisfactory. By adopting an efficient local refinement strategy together with the enhanced stability features of the entropic model, this method extends the range of applicability of the lattice Boltzmann approach to the solution of realistic fluid dynamics problems, at high Reynolds numbers, involving complex geometries.
Implicit Predictors in Regularized Data-Driven Predictive Control

We introduce the notion of implicit predictors, which characterize the input-(state)-output prediction behavior underlying a predictive control scheme, even if it is not explicitly enforced as an equality constraint (as in traditional model or subspace predictive control). To demonstrate this concept, we derive and analyze implicit predictors for some basic data-driven predictive control (DPC) schemes, which offers a new perspective on this popular approach that may form the basis for modified DPC schemes and further theoretical insights.
Integrated System-on-Module for Design-Space Exploration of Spiking Neural Networks

We present an integrated system-on-module for design-space exploration of neurosynaptic behavior of complex, non-volatile memory enhanced, spiking neural networks. The system operates in a locally-analog, globally-digital manner, which enables both exploration and validation of individual computational components and characteristic spike-based features of neurosynaptic arrays. The system components are reconfigurable, adaptable and interchangeable enabling reproducible, precise firing patterns. The platform is coupled with an embedded resistive-RAM which acts as a weight retention mechanism. Experimental results obtained in 28 nm CMOS technology illustrate the feasibility of the methodology.
A MATRIX-FREE METHOD FOR SPATIAL-TEMPORAL GAUSSIAN STATE-SPACE MODELS

This study develops a scalable matrix-free h-likelihood method for spatial-temporal Gaussian state-space models. The state vectors are constructed in such a way that they follow spatial-temporal Gaussian autoregressions that are consistent with the conditional formulation of auto-normal spatial fields. The proposed h-likelihood method provides the same inferences as those obtained from the Kalman filter and residual maximum likelihood analyses. However, for data from a large number of spatial sites, our method has significant computational advantages. Furthermore, we describe inferences in small time steps and indicate how the proposed method can be adapted to other complex spatialtemporal dynamical models based on stochastic partial differential equations. The proposed method applies to data with regularly or irregularly sampled spatial locations. Lastly, we illustrate our method by means of a simulation study and a data example on atmospheric concentrations of total nitrate across eastern
Query-Specific Embedding Co-Adaptation Improve Few-Shot Image Classification

Few-Shot Image Classification (FSIC) aims to identify unseen categories by a limited number of instances. Recently, some metric-based methods have attempted to generate more discriminative task-specific embeddings by embedding adaptation strategies. However, the generated embeddings are either query-agnostic or ignore local relations between instances in each category, resulting in limited performance improvement. To address the above issues, in this letter, we propose QS-CAN, a Query-Specific embedding Co-Adaptation Network, which generates task- and query-specific embeddings by fusing inter-class and intra-class information. The core modules of QS-CAN are Inter-Class Adapter(Inter-A) and Intra-Class Adapter(Intra-A). The Inter-Class Adapter encodes the global relationship within the task, pushing the different categories away from each other. At the same time, the Intra-Class Adapter focuses on modeling the local relationship within each category, pulling the instances within a category closer. Moreover, an Adaptive Fusion Module (AFM) is proposed to integrate two co-adapted embeddings to get a more discriminative space. Experiments show that our method performs comparably to other advanced methods on three widely used datasets.
On a New Class of Vector Variational Control Problems

Abstract In this paper, we introduce geodesic (strongly) b-V-KT-pseudoinvex multidimensional control problems. This new class of multiobjective variational control problems, involving multiple integral cost functionals, is described such that every geodesic Kuhn–Tucker point is geodesic efficient solution. In addition, to illustrate the effectiveness of our main result, the paper is completed with an application.
LungHeart-AtMe: Adventitious Cardiopulmonary Sounds Classification Using MMoE with STFT and MFCCs Spectrograms

Adventitious cardiopulmonary (lung and heart) sound detection and classification through a digital stethoscope plays a vital role in early diagnosis and telehealth services. However, automatically detecting the adventitious sounds is challenging since they are easily susceptible to each other’s influence and noises. In this paper, for the first time, we simultaneously classify adventitious lung and heart sounds using our proposed LungHeart-AtMe model based on a mixed dataset of the ICBHI 2017 lung sounds dataset and the PhysioNet 2016 heart sounds dataset. Based on characteristics of lung and heart sounds, Wavelet Decomposition is applied first to perform noise reduction, then two time-frequency feature extraction techniques, which are Short Time Fourier Transform (STFT) and Mel Frequency Cepstral Coefficients (MFCCs), are chosen to extract preliminary features of sounds and transform sounds data to spectrograms that are easy to analyze. Our LungHeart-AtMe model is improved by introducing MMoE structure and by using the attention mechanism-based CNN model to extend its global feature extraction capability. From our experimental result, LungHeart-AtMe has achieved a Sensitivity of 71.55% and a Specificity of 28.06% for cardiopulmonary sounds classification.
Noise-Robust Radar HRRP Target Sequential Recognition Based on Correlative Scattering Centers

In order to enhance radar target recognition performance of high-resolution range profile (HRRP) under low signal-to-noise ratio (SNR), a novel HRRP sequential recognition method utilizing the correlation among scattering centers is proposed in this letter. In this method, the correlative information among the prominent scattering centers is considered in the covariance matrix and the sequential recognition is carried out through the long short-term memory (LSTM) network to fully excavate the information of HRRPs. Moreover, we introduce a noise-robust recognition algorithm to renew the Gaussian-trained model by the estimated variance of the noise. Experimental results indicate that the proposed method can acquire higher recognition rates and better robustness by introducing correlative information and sequential processing.
Computers and Fluids

The turbulent wake generated by a horizontal circular cylinder in free-surface flows of increasing shallowness with submergence-to-diameter ratios between 0.5 and 2.1 are investigated using large-eddy simulation. At Froude number ( 𝐹𝑟 ) = 0.26, the free-surface deformation is small with little influence on the wake, whereas at 𝐹𝑟 = 0.53 there is a drop in the free-surface downstream of the cylinder that impacts the coherence of the vortex shedding. Irrespective to the relative submergence, the close location of the cylinder to the bottom wall generates an asymmetric von-Kármán vortex street. Proper Orthogonal Decomposition (POD) is used to analyse the spatio-temporal coherence of the turbulent structures shed in the cylinder wake. The spatial patterns of the first two POD modes, those containing the most energy, depict the von-Kármán vortices. As 𝐹𝑟 increases, the energy content of the first pair of POD modes decreases from 56% at 𝐹𝑟 = 0.26 to 26.8% at 𝐹𝑟 = 0.53, as large-scale vortices lose coherence more rapidly with shallower conditions. This energy redistribution leads to the smaller flow structures to contain a relatively higher energy when 𝐹𝑟 is larger. The frequency of the dominating vortex shedding determined from the spectra of the POD temporal coefficients unveils that the first two coefficients feature a dominant peak at the von-Kármán vortex shedding frequency. At 𝐹𝑟 < 0.45, the reconstructed flow field using the first 20 POD modes agrees well with the instantaneous velocities from LES, whereas free-surface effects on the wake dynamics at increasing 𝐹𝑟 requires more POD modes to reconstruct the flow field with reduced error.
Nanoconfined heliconical structure of twist-bend nematic liquid crystal phase

ABSTRACT We produced controlled heliconical structures of a twist-bend nematic (NTB) liquid-crystal (LC) phase in nanoconfinement in a porous anodic aluminium oxide (AAO) film. The structural parameters of the NTB phase such as conical angle and helical pitch can be modulated by varying the surface energy of the inner surface of the porous AAO film, done by using different self-assembled monolayers (SAMs). The LC molecules tend to be more freely packed, thus forming a larger conical angle, when placed on the tri-deca-fluoro-1,1,2,2-tetrahydrooctyl-trichlorosilane (FOTS)-treated substrate, which has a relatively low surface energy. In contrast, the molecules form a more tightly packed structure, and thus a smaller conical angle, when placed on the 2-(methoxy(polyethyleneoxy)-propyl)trimethoxysilane (PEG 6/9)-treated substrate, which has higher surface energy. This work improves our collective understanding of self-assembled heliconical structures in the NTB phase. Graphical Abstract
Assimilating All-Sky Infrared Radiances from GOES-16 ABI Using an Ensemble Kalman Filter for Convection-Allowing Severe Thunderstorms Prediction

This article presents the first practice of assimilating real-world all-sky GOES-16 ABI infrared brightness temperature (BT) observations using an ensemble-based data assimilation system coupled with the Weather Research and Forecasting (WRF) Model at a convection-allowing (1 km) horizontal resolution, focusing on the tornadic thunderstorm event across Wyoming and Nebraska on 12 June 2017. It is found that spurious clouds created before observed convection initiation are rapidly removed, and the analysis and forecasts of thunderstorms are significantly improved, when all-sky BT observations are assimilated with the adaptive observation error inflation (AOEI) and adaptive background error inflation (ABEI) techniques. Better forecasts of the timing and location of convection initiation can be achieved after only 30 min of assimilating BT observations; both deterministic and probabilistic WRF forecasts of midlevel mesocyclones and low-level vortices, started from the final analysis with 100 min of BT assimilation, closely coincide with the tornado reports. These improvements result not only from the effective suppression of spurious clouds, but also from the better estimations of hydrometeors owing to the frequent assimilation of all-sky BT observations that yield a more accurate analysis of the storm. Results show that BT observations generally have a greater impact on ice particles than liquid water species, which might provide guidance on how to better constrain modeled clouds using these spaceborne observations.
The Use of Partial Cloudiness in a Bulk Cloud Microphysics Scheme: Concept and 2D Results

The source and sink terms of microphysical processes vary nonlinearly with cloud condensate amount. Therefore, partial cloudiness is one of the important factors to be considered in a cloud microphysics scheme given that in-cloud condensate amount depends on the cloud fraction of the grid box. An alternative concept to represent the partial cloudiness effect on the microphysical processes of a bulk microphysics scheme is proposed. Based on the statistical relationship between cloud condensate and cloudiness, all hydrometeors in the microphysical processes are treated after converting them to in-cloud values by dividing the amount by estimated cloudiness and multiplying it after the computation of all microphysics terms. The underlying assumption is that all the microphysical processes occur in a cloudy part of the grid box. In a 2D idealized storm case, the Weather Research and Forecasting (WRF) single-moment 5-class (WSM5) microphysics scheme with the proposed approach increases the amount of snow and rain through enhanced autoconversion/accretion and increases precipitation reaching the surface.