Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a specific MATLAB code for processing of standard tensile test data for sheet metal forming simulations

In order to obtain proper and sufficiently precise results from metal forming simulations, accurate material data should be input to the simulation software. One of the most important input for the simulation software is the flow curve (yield curve) of the material. Although there are many tests which could be performed in order to construct the flow curve such as standard tensile test, hydraulic bulge test, frictionless dome test, etc., standard tensile test is the simplest and cheapest technique despite of its technical restrictions. In this article, the studies regarding to the creation of a specific MatLab code for processing of tensile test data are presented. This specific MatLab code is capable of determining the constants of Hollomon’s, Ludwig’s, Swift’s and Voce’s hardening equations with their regression results, as well. By means of this code, the proper flow curve of any material and also the Lankford parameters could be obtained and the hardening equations which fits best to the experimental data could be determined. Keywords: Tensile Test; Flow Curve; Hardening Equation; Sheet Metal Forming Simulation DOI: 10.17350/HJSE19030000019 Full Text:
Soft evolution after a hard scattering process

The dynamical cascade of momentum, spin, charge, and other quantum numbers from an ultra-violet process into the infra-red is a fundamental concern for asymptotically free or conformal gauge field theories. It is also a practical concern for any high energy scattering experiment with energies above tens of GeV. We present a formulation of the evolution equation that governs this cascade, the Banfi-Marchesini-Smye equation, from both an effective field theory point of view and a direct diagrammatic argument. The equation uses exact momentum conservation, and is applicable to both scattering with initial and final state hard partons. The direct diagrammatic formulation is organized by constructing a generating functional. This functional is also automatically realized with soft wilson lines and collinear field operators coupled to external currents. The two approaches are directly connected by reverse engineering the Lehman-Symanzik-Zimmermann reduction procedure to insert states within the soft and collinear matrix elements. At leading order, the cascade is completely controlled by the soft anomalous dimension. By decomposing the anomalous dimension into on-shell and off-shell regions as would be realized in the effective field theory approach with a Glauber mediating potential, we are forced to choose a transverse momentum ordering in order to trivialize the overlap between Glauber potential contributions and the pure soft region. The evolution equation then naturally incorporates factorization violating effects driven by off-shell exchanges for active partons. Finally, we examine the consequences of abandoning exact momentum conservation as well as terminating the evolution at the largest inclusive scale, procedures often used to simplify the analysis of the cascade.
The UTMOST pulsar timing programme I: Overview and first results

We present an overview and the first results from a large-scale pulsar timing programme that is part of the UTMOST project at the refurbished Molonglo Observatory Synthesis Radio Telescope (MOST) near Canberra, Australia. We currently observe more than 400 mainly bright southern radio pulsars with up to daily cadences. For 205 (8 in binaries, 4 millisecond pulsars) we publish updated timing models, together with their flux densities, flux density variability, and pulse widths at 843 MHz, derived from observations spanning between 1.4 and 3 yr. In comparison with the ATNF pulsar catalogue, we improve the precision of the rotational and astrometric parameters for 123 pulsars, for 47 by at least an order of magnitude. The time spans between our measurements and those in the literature are up to 48 yr, which allows us to investigate their long-term spin-down history and to estimate proper motions for 60 pulsars, of which 24 are newly determined and most are major improvements. The results are consistent with interferometric measurements from the literature. A model with two Gaussian components centred at 139 and $463~\text{km} \: \text{s}^{-1}$ fits the transverse velocity distribution best. The pulse duty cycle distributions at 50 and 10 per cent maximum are best described by log-normal distributions with medians of 2.3 and 4.4 per cent, respectively. We discuss two pulsars that exhibit spin-down rate changes and drifting subpulses. Finally, we describe the autonomous observing system and the dynamic scheduler that has increased the observing efficiency by a factor of 2-3 in comparison with static scheduling.
Kronoseismology. IV. Six Previously Unidentified Waves in Saturn’s Middle C Ring

Recent studies of stellar occultations observed by the Visual and Infrared Mapping Spectrometer on board the Cassini spacecraft have demonstrated that multiple spiral wave structures in Saturn’s rings are probably generated by normal-mode oscillations inside the planet. Wavelet-based analyses have been able to unambiguously determine both the number of spiral arms and the rotation rate of many of these patterns. However, there are many more planetary normal modes that should have resonances in the rings, implying that many normal modes do not have sufficiently large amplitudes to generate obvious ring waves. Fortunately, recent advances in wavelet analysis allow weaker wave signals to be uncovered by combining data from multiple occultations. These new analytical tools reveal that a pattern previously identified as a single spiral wave actually consists of two superimposed waves, one with five spiral arms rotating at 1593. °6/day and one with 11 spiral arms rotating at 1450. °5/day. Furthermore, a broad search for new waves revealed four previously unknown wave patterns with six, seven, eight, and nine spiral arms rotating around the planet at 1538. °2/day, 1492. °5/day, 1454. °2/day, and 1421. °8/day, respectively. These six patterns provide precise frequencies for another six fundamental normal modes inside Saturn, yielding what is now a complete sequence of fundamental sectoral normal modes with azimuthal wavenumbers from 2 to 10. These frequencies should place strong constraints on Saturn’s interior structure and rotation rate, while the relative amplitudes of these waves should help clarify how the corresponding normal modes are excited inside the planet.
Auto-tuning Flood Simulations on CPUs and GPUs

In recent years, there has been a growing focus on how GPUs can be utilized for general purpose computations. However, this leads to less focus on the CPU as a computational resource. As a consequence, heterogeneous computers with both a CPU and a GPU may not utilize all its resources. To address this, I present a heterogeneous CPU and GPU implementation based on a high-resolution explicit scheme for the shallow-water equations on a single GPU (Brodtkorb et al. 2012) . I perform two levels of parallelism: First, a row domain decomposition method is used to decompose the computational domain to utilize both the CPU and the GPU in parallel. Secondly, the CPU code is multi-threaded to take advantage of all cores. Furthermore, systems of conservation laws often involve large computational domains where only parts of the domain has to be computed, e.g., water or other fluids. This can lead to imbalance in the workload if the computations between the GPU and the CPU are not balanced. To address this, I present dynamic auto-tuning methods that automatically tune the domain decomposition between the CPU and the GPU during runtime, as well as optimization techniques to skip computations for "dry" domain areas.
An Empirical Investigation of Student Acceptance of Synchronous E-Learning in an Online University

This study proposes an extended technology acceptance model to predict acceptance of synchronous e-learning by examining relationships among variables associated with factors influencing the technology acceptance of synchronous e-learning. Learners at an online university participated through an online survey; there were 251 respondents in all. Employing structural equation modeling, we tested a hypothesized model, and the resulting model was found to have a good fit. Self-efficacy, subjective norm, and system accessibility have an effect on learners’ technology acceptance of synchronous e-learning, whereas systematic lecture content does not affect it. In addition, the perceived ease of use also has an effect on behavioral intention, whereas perceived usefulness does not. This study examined the acceptance structure of synchronous e-learning. The findings are expected to enhance the understanding of synchronous e-learning for instructors and school administrators and also provide empirical evidence that can be used to establish effective strategies.
Performance Analysis of GA and PSO based Feature Selection Techniques for Improving Classification Accuracy in Remote Sensing Images

Background/Objectives: - Feature Selection is applicable to decrease the number of features in various applications wherein the data include hundreds and thousands of features. The objective of this study is to choose Genetic Algorithm for feature selection to obtain better fitness function. Methods/Statistical Analysis: Particle Swarm Optimization (PSO) approach is used for selecting the subset from the combination of texture based features and providing the better fitness values. In this paper PSO is used to obtain the feature sets and the performance is compared with genetic algorithm. Support vector machine classifier is used to improve the classification accuracy. Findings: The experimental results shows that PSO overall accuracy is improved to LISS IV 1.7%, 1.4% and 2.9% and the kappa coefficient is improved to 0.06%, 0.012% and 0.39% as compared to GA. Application/Improvements: The Fitness value obtained by GA is more complex and not accurate. To reduce the complexity and increase the accuracy Particle Swarm optimization is used. Hence PSO improved the quality of texture based images.
Band Engineering of Thermoelectric Materials

Lead chalcogenides have long been used for space‐based and thermoelectric remote power generation applications, but recent discoveries have revealed a much greater potential for these materials. This renaissance of interest combined with the need for increased energy efficiency has led to active consideration of thermoelectrics for practical waste heat recovery systems—such as the conversion of car exhaust heat into electricity. The simple high symmetry NaCl‐type cubic structure, leads to several properties desirable for thermoelectricity, such as high valley degeneracy for high electrical conductivity and phonon anharmonicity for low thermal conductivity. The rich capabilities for both band structure and microstructure engineering enable a variety of approaches for achieving high thermoelectric performance in lead chalcogenides. This Review focuses on manipulation of the electronic and atomic structural features which makes up the thermoelectric quality factor. While these strategies are well demonstrated in lead chalcogenides, the principles used are equally applicable to most good thermoelectric materials that could enable improvement of thermoelectric devices from niche applications into the mainstream of energy technologies.
Review of Impulse Noise Removal from DigitalVideo

With the advancement in communication and information technology, legacy system are replaced by high speed computer as a result of this the world is shifting from analog to digital systems. Now, it is possible to transmit the digital videos over a communication channel and vice versa. Color video processing systems are used for a variety of purposes ranging from capturing scenes, processing of frames for feature extraction etc. Digital videos are generally contaminated by noise. Impulse noise is one such noise which may corrupt the frames of a digital video during acquisition, transmission over a noisy channel or in storage etc. This paper discussed in details about the color video processing and various schemes for removal of impulse noise for digital video.
Superconductivity in LiFeO2Fe2Se2 with anti-PbO-type spacer layers

Superconductivity has been found in the iron-arsenides with different structures by alternative stacking of the conducting Fe2As2 layers and spacer layers including alkali- or alkali-earth metal ions and PbO- or perovskite-type oxides blocks. So far, no spacer layer could be intercalated in between the neutral Fe2Se2 layers similar in structure to Fe2As2 layers except for alkali-metal ions in AxFe2-ySe2. The superconducting phase in AxFe2-ySe2 with transition temperature of 32 K is always inter-grown with the insulating phase which exhibits an antiferromagnetic order with extremely high Neel temperature of ~560 K and the order of Fe vacancies. Such inhomogeneity makes the investigation on the underlying physics in AxFe2-ySe2 complicated. Here we report the synthesis of a novel superconductor LiFeO2Fe2Se2 by hydrothermal method, exhibiting superconductivity at ~43 K, in which anti-PbO-type spacer layers of LiFeO2 have been successfully intercalated between anti-PbO-type Fe2Se2 layers. This finding demonstrates that superconductivity can be realized in the iron selenide with a novel spacer layer of anti-PbO-type, which is not found in the iron arsenides, and expands the category of the iron-based superconductors. Such a new synthetic method paves a new way to search for possible novel superconductors with different spacer layers, which is helpful to further study the underlying physics in iron-based high-Tc superconductors.
Understanding Mobile Users’ Privacy Expectations: A Recommendation-Based Method Through Crowdsourcing

Privacy is a pivotal issue of mobile apps because there is a plethora of personal and sensitive information in smartphones. Many mechanisms and tools are proposed to detect and mitigate privacy leaks. However, they rarely consider users’ preferences and expectations. Users hold various expectation towards different mobile apps. For example, users may allow a social app to access their photos rather than a game app because it goes beyond users’ expectation to access personal photos. Therefore, we believe it is practical and beneficial to understand users’ privacy expectations on various mobile apps and help them mitigate privacy risks introduced by smartphones. To achieve this objective, we propose and implement PriWe, a system based on crowdsourcing driven by users who contribute privacy permission settings of the apps installed on their smartphones. PriWe leverages the crowdsourced permission settings to understand users’ privacy expectations and provides app specific recommendations to mitigate information leakage. We deployed PriWe in the real world for evaluation. According to the feedback of 78 users who evaluated our system and 422 participants who completed our survey, PriWe is able to make proper recommendations which can match participants’ privacy expectations and are mostly accepted by users, thereby help them to mitigate privacy disclosure in smartphones.
A Convolutional Neural Network-based explainable classification method of SPECT myocardial perfusion images in nuclear cardiology

This study targets on the development of an explainable Convolutional Neural Network (CNN) pipeline in the form of a handcrafted CNN to identify patients’ coronary artery disease status (normal, ischemia or infarction). The proposed RGB-CNN model utilizes various pre- and post-processing tools and deploys a state-of-the-art explainability tool to produce more interpretable predictions in the task of decision making. The provided dataset includes 630 patients’ cases in stress and rest representations and comprises 257 normal, 241 ischemic and 127 infarction cases, previously classified by a doctor. The imaging dataset was split into 20% for testing and 80% for training, whose 15% was further used for validation purposes. Data augmentation was employed to increase generalization. Grad-CAM based color visualization approach was also utilized to provide predictions with interpretability in the detection of ischemia and infarction in SPECT-MPI images, counterbalancing any lack of rationale in the results extracted by CNNs. The proposed model achieved 94,06% accuracy and 0.9541% AUC, demonstrating efficient performance and stability.
A Comparison Between Convolutional and Transformer Architectures for Speech Emotion Recognition

Creating speech emotion recognition models com-parable to the capability of how humans recognise emotions is a long-standing challenge in the field of speech technology with many potential commercial applications. As transformer-based architectures have recently become the state-of-the-art for many natural language processing related applications, this paper investigates their suitability for acoustic emotion recognition and compares them to the well-known AlexNet convolutional approach. This comparison is made using several publicly available speech emotion corpora. Experimental results demonstrate the efficacy of the different architectural approaches for particular emotions. The results show that the transformer-based models outperform their convolutional counterparts yielding F1-scores in the range [70.33%, 75.76%]. This paper further provides insights via dimensionality reduction analysis of output layer activations in both architectures and reveals significantly improved clustering in transformer-based models whilst highlighting the nuances with regard to the separability of different emotion classes.
On the portability and tuning of supervised word sense disambiguation systems

This report describes a set of experiments carried out to explore the portability of alternative supervised Word Sense Disambiguation algorithms. The aim of the work is threefold: firstly, studying the performance of these algorithms when tested on a different corpus from that they were trained on; secondly, exploring their ability to tune to new domains, and thirdly, demonstrating empirically that the LazyBoosting algorithm outperforms state-of-the-art supervised WSD algorithms in both previous situations.
PAMELA and FERMI limits on the neutralino-chargino mass degeneracy

Searches for Dark Matter (DM) particles with indirect detection techniques have reached important milestones with the precise measurements of the anti-proton () and gamma-ray spectra, notably by the PAMELA and FERMI-LAT experiments. While the γ-ray results have been used to test the thermal Dark Matter hypothesis and constrain the Dark Matter annihilation cross section into Standard Model (SM) particles, the anti-proton flux measured by the PAMELA experiment remains relatively unexploited. Here we show that the latter can be used to set a constraint on the neutralino-chargino mass difference. To illustrate our point we use a Supersymmetric model in which the gauginos are light, the sfermions are heavy and the Lightest Supersymmetric Particle (LSP) is the neutralino. In this framework the W+W− production is expected to be significant, thus leading to large and γ-ray fluxes. After determining a generic limit on the Dark Matter pair annihilation cross section into W+W− from the data only, we show that one can constrain scenarios in which the neutralino-chargino mass difference is as large as 20 GeV for a mixed neutralino (and intermediate choices of the propagation scheme). This result is consistent with the limit obtained by using the FERMI-LAT data. As a result, we can safely rule out the pure wino neutralino hypothesis if it is lighter than 450 GeV and constitutes all the Dark Matter.
Two dimensional inorganic electride-promoted electron transfer efficiency in transfer hydrogenation of alkyne and alkenes

Please note that technical editing may introduce minor changes to the text and/or graphics, which may alter content. The journal’s standard Terms & Conditions and the Ethical guidelines still apply. In no event shall the Royal Society of Chemistry be held responsible for any errors or omissions in this Accepted Manuscript or any consequences arising from the use of any information it contains. Accepted Manuscript Chemical Science
Application of BP neural network prediction mo del on crops pests level by MATLAB

In this paper,how to use the backpropagation training functions in the MAT LAB toolbox to train feedforward neural networks to solve specific problems is explained.And the result of the prediction by using the data of historical oc currence level of paddy pests in Lujiang,Anhui province verifies the feasibil ity of the predictive model.
Protein Secondary Structure Analysis Using Neural Network

From PDB database, we picked up three dimensional data of proteins which were used by Qian and Sejnowski [1] and Muskal and Kim [2]. The secondary structures were calculated using Kabsch and Sander method [3]. We made the data sets of 20 amino acids in helix, sheet and the others, which consist of nine residue segments including the anking residues. Using these data sets, we incorporated the following features in the neural network.
A new class of methods for functional connectivity estimation

Measuring functional connectivity from neural recordings is important in understandingprocessing in cortical networks. The covariance-based methods are the currentgolden standard for functional connectivity estimation. However, the link between thepair-wise correlations and the physiological connections inside the neural network is unclear.Therefore, the power of inferring physiological basis from functional connectivity estimationis limited. To build a stronger tie and better understand the relationship between functionalconnectivity and physiological neural network, we need (1) a realistic model to simulatedifferent types of neural recordings with known ground truth for benchmarking; (2) a newfunctional connectivity method that produce estimations closely reflecting the physiologicalbasis.In this thesis, (1) I tune a spiking neural network model to match with human sleepEEG data, (2) introduce a new class of methods for estimating connectivity from differentkinds of neural signals and provide theory proof for its superiority, (3) apply it to simulatedfMRI data as an application.
Robot learning by Single Shot Imitation for Manipulation Tasks

In this work, we present a Programming by imitation for a robotic manipulation system, which can be programmed for various tasks from only a single demonstration. The system is primarily based on the three components: i) scene parsing, ii) action classification, and iii) dynamic primitive shape fitting. All the above modules are developed by leveraging state-of-the-art techniques in 2D and 3D visual perception. The primary contribution of this system paper is an imitation-based robotic system that can replicate highly complex tasks by executing elementary task-specific program templates, thus avoiding extensive and exhaustive manual coding. In addition, we contribute by introducing a primitive shape fitting module by which it becomes easier to grasp objects of various shapes and sizes. To evaluate the system performance, the proposed robotic system has been tested on the task of multiple object sorting and reports 91.8% accuracy in human demonstrated action detection, 76.1% accuracy in action execution, and overall accuracy of 80%. We also examine the proposed system's component-wise performance to demonstrate the efficacy and deployability in industrial and household scenarios.