Miracle-dz/UniFAR-FTUs huggingface dataset:
The Influence of Hotel Attributes on Brand Attachment and Post-Consumption Outcomes: The Mediating Effects of Brand Credibility

This research examine the mediating role of brand credibility between the relationship of hotel attributes and brand attachment that will to lead to post-consumption outcomes. We also examine how hotel attributes contributes to the brand attachment and post-consumption outcomes. Based on the mean-end chain theory, it explores the relationship among six constructs; hotel attributes, brand credibility, brand attachment and postconsumption outcomes (i.e., satisfaction, revisit intention and word-of-mouth). Partial Least Squares Structural Equation Modelling (PLS-SEM) approach was deployed to validate the research model. The result indicates brand credibility fully mediates the relationship between hotel attributes and brand attachment. Subsequently, brand credibility and brand attachment are found to be significantly linked with postconsumption outcomes. The result offers implications for hotelier and direction for future research.
Acetic Acid and Pentane Treatment by a Biofiltration System Based on Organic Beds Under Transient Conditions

Assessment of biofiltration systems under transient conditions is necessary when determining industrial operating conditions of biofiltration beds. The key system variables such as filter bed moisture content, the pollutant type and its concentration, must be evaluated in order to accurately calculate scaling of biofiltration beds. The removal efficiency of two organic volatile compounds (VOCs) was evaluated: acetic acid (hydrophilic) and pentane (hydrophobic). Two independent biofiltration systems were operated (A and B), in each system the bed packing material was the same. The filtration beds were made from compost, with the following mixtures: rice husk-chicken manure (CAS) and pruning waste-chicken manure (POD), each in a ratio of 1:1 (v:v). As part of this study, in system A, the effect of moisture on acetic acid removal efficiency was analyzed, finding that in a moisture range of 50-15% and of 50-25%, efficiencies above 90% are reached for CAS and for POD respectively. On the other hand, in system B, the performance of the biofilters was evaluated against different concentrations of pentane, resulting in removal efficiencies of 25% and 43% for POD and CAS, respectively. In general, these results indicate that there is a significant dependence between the performance of the system and the moisture content of the filter bed and the lower limit in which the bed would operate under industrial conditions is defined. The pollutant water solubility was also considered in order to define the maximum concentrations that the bed would support under transient conditions.
Deep Networks for Image-to-Image Prediction

beenverysuccessfulinavarietyofcomputervisiontasks,especiallyonthoselinkedtorecognition. Opticalﬂowesti-mationhasnotbeenamongthetasksCNNssucceededat. InthispaperweconstructCNNswhicharecapableofsolvingtheopticalﬂowestimationproblemasasupervisedlearningtask. Weproposeandcomparetwoarchitectures:agenericarchitectureandanotheroneincludingalayerthatcor-relatesfeaturevectorsatdifferentimagelocations. SinceexistinggroundtruthdatasetsarenotsufﬁcientlylargetotrainaCNN,wegeneratealargesyntheticFlyingChairsdataset.WeshowthatnetworkstrainedonthisunrealisticdatastillgeneralizeverywelltoexistingdatasetssuchasSintelandKITTI,achievingcompetitiveaccuracyatframeratesof5to10fps. Abstract Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especiallyonthoselinkedtorecognition. Opticalﬂowesti-mationhasnotbeenamongthetasksCNNssucceededat. In thispaperweconstructCNNswhicharecapableofsolvingtheopticalﬂowestimationproblemasasupervisedlearningtask. Weproposeandcomparetwoarchitectures:agenericarchitectureandanotheroneincludingalayerthatcor-relatesfeaturevectorsatdifferentimagelocations. Sinceexistinggroundtruthdatasetsarenotsufﬁcientlylargeto train a CNN, we generate a large synthetic Flying Chairs dataset. We show that networks trained on this unrealistic data still generalize very well to existing datasets such as Sintel and KITTI, achieving competitive accuracy at frame ratesof5to10fps. 1. Introduction Convolutionalneuralnetworkshavebecomethemethod ofchoiceinmanyﬁeldsofcomputervision. Theyareclas- sically applied to classiﬁcation sentedarchitecturesalsoallowforper-pixelpredictionslikesemanticsegmentation[28]ordepthestimationfromsingleimages[10]. Inthispaper,weproposetrainingCNNsend-to-endtolearnpredictingtheopticalﬂowﬁeldfromapairofimages. Whileopticalﬂowestimationneedspreciseper-pixello-calization,italsorequiresﬁndingcorrespondencesbetweentwoinputimages. Thisinvolvesnotonlylearningimagefeaturerepresentations,butalsolearningtomatchthematdifferentlocationsinthetwoimages.Inthisrespect,opticalﬂowestimationfundamentallydiffersfrompreviousappli-
Yeasts Isolated from Traditional Brem Bali Show Stress Tolerance Phenotype against Fermentation-Related Stresses

The search for microbes, primarily yeasts with unique characters such as the tolerance against fermentation-relatedstresses, is gaining significant interest nowadays. Traditionally made alcoholic beverages can be used as sources for such yeasts, given that during fermentation and storage, microbes may develop stress tolerance responses leading to naturally stress-tolerant yeast strains. In this study, we used an alcoholic beverage, that is, Brem Bali, as the source of potential yeast isolates. We isolated nine yeast isolates from two traditional Brem Bali products. All isolates showed tolerance against high glucose stress (40–50%) and sensitivity against high-temperature stress (37–50 °C). Notably, isolate BT5 showed tolerance phenotype against ethanol stress (up to 12.5%). Notably, the ethanol stress tolerance phenotype shown by isolate BT5 was unlikely correlated to the ability of the isolates in combating other stresses. Based on the internal transcribed spacer sequence, isolates BT2, BT5, and BT6 shared high similarity with Wickerhamomyces anomalus (98%). Further approaches may be needed to clarify the identity of these isolates. Based on our study, isolate BT5 bears potential as a fermentation agent based on its ability to combat high glucose and ethanol stresses.
Texture Analysis using wavelet Transform

Content based Image retrieval (CBIR); Markov Random Field (MRF); Gray Level cooccurrence Matrix(GLCM); In this research application of wavelet based multiscale image analysis methods for texture analysis has been highlighted. These methods are based on multiresolution properties of the two-dimensional wavelet transform, which is used to extract the features needed to discriminate and differentiate various textures more accurately then existing methods, we also took into account the texture model, the noise distribution, and the inter-dependence of the texture features which further help in discriminating factor. Multiresolution approach is nothing but a modified wavelet transform called the tree-structured wavelet transform or wavelet packets for texture analysis and classification. This approach is motivated by the observation that a large class of natural textures can be modeled as quasi-periodic signals whose dominant frequencies are located in the middle frequency channels. With the transform, we are able to zoom into any desired frequency channels for further decomposition and thus we could extract more texture features as compared to other methods.
Food Ordering System based on Human Computer Interaction and Machine Learning Techniques

—There is a saying,”the best interface is no interface”. Many people consider voice interface as an excellent approach to communicate with computer system. In the following paper, we are trying to build up a system which is based on Human Computer Interaction to put in an order for food in restaurants. People tend to go to a restaurant where service and convenience of the customer are given top priority. A major problem faced by the customers in a restaurant is waiting for the staff to take the order, this is because during rush hours the availability of staff decreases. This mishap can be removed by implementing a Food ordering system, a device which is built using Raspberry Pi 3; along with voice controlled interface, as presented in this paper. Techniques like Natural Language Processing, Rule-based system, Sentiment Analysis are used in the proposed system.
RUL Estimation Enhancement using Hybrid Deep Learning Methods

The turbofan engine is one of the most critical aircraft components. Its failure may introduce unwanted downtime, expensive repair, and affect safety performance. Therefore, It is essential to accurately detect upcoming failures by predicting the future behavior health state of turbofan engines as well as its Remaining Useful Life. The use of deep learning techniques to estimate Remaining Useful Life has seen a growing interest over the last decade. However, hybrid deep learning methods have not been sufficiently explored yet by researchers.In this paper, we proposed two-hybrid methods combining Convolutional Auto-encoder (CAE), Bi-directional Gated Recurrent Unit (BDGRU), Bi-directional Long-Short Term Memory (BDLSTM), and Convolutional Neural Network (CNN) to enhance the RUL estimation. The results indicate that the hybrid methods exhibit the most reliable RUL prediction accuracy and significantly outperform the most robust predictions in the literature.
BRAIN MRI ANALYSIS AND SEGMENTATION USING 2D-UNET ARCHITECTURE

: Deep Neural Networks have demonstrated amazingly positive execution in the field of computer vision issues - object acknowledgment, discovery, and division. These techniques have been used in the clinical picture examination area. Convolutional neural systems (CNNs), a remarkable part of profound learning applications to visual purposes, have earned significant consideration in the most recent years because of its advanced exhibitions in computer vision applications. They have accomplished tremendous growth in the areas of object acknowledgment, recognition and division challenges. Our attention is on models being utilized, information pre-handling and readiness and fittingly preparing the subsequent information or picture. The U – Nets are a very powerful CNNs which has accuracy near to humans. We have created and exploited this CNN architecture, U-Net and have done image segmentation for the brain Magnetic Resonance Images (MRI). The aim of our work is to fundamentally concentrate on the pre-processing of the MRI images, perform Skull Stripping using Deep CNN architecture U-Net and to perform image segmentation.
EFFECT OF STRUCTURAL HETEROGENEITY OF 1Airy

α-Synuclein (α-Syn)is an intrinsically disordered protein that aggregates into intra-cellular Lewy bodies (LB) and Lewy neuritis which are considered to be the pathological hallmarks for the occurrence of Parkinson’s Disease(PD) (Spillantini al., 1997), a neuro-degenerative disorder. α bound protein with 140 amino acids conserved in the vertebrates and expressed in the pre-synaptic nerve terminals in several regions of the brain (Bisaglia, Mammi and Bubacco, 2009). The insights into the α-syn plasticity and misfolding from differential micelle binding have been studied very recently (Mazumder, Suk and Ulmer, 2013). This unstructured protein binds to curved synaptic vesicle membranes in h conformations but it misfolds into amyloid fibrils via β interactions (Lavedan, 1998). α-Syn basically comes from the synuclein family inherited from three highly expressed genes that are SNCA, SNCB and SNCG of α, β and γ synuclein respectively (Lavedan, 1998). The aggregation propensity of α syn is still not understood though various genetic data have supported the pathological role of this protein (Lashuel, Overk, Oueslati, Masliah, 2013). The cause for the familial Parkinsonism and the fibrillation propensity is due to the multiplication of the gene that encodes α-syn protein called the SNCA gene (Chartier-Harlin, et al., 2004) and also for the
Garden Educators ’ Talk during Guided School Visits

Botanic gardens are popular destinations for school visits to learn about plant-based science. However, little is known about teaching and learning in such settings, in particular about school visits guided by professional botanic garden educators (BGEs). The purpose of this study was to identify the pedagogical moves of the BGEs during guided school visits. More specifically, this study follows a qualitative research design, investigating six elementary school groups (7to 11-years old) guided by three BGEs. The guided visits were videotaped and the transcripts analyzed in order to find out the patterns of the BGE and student discourse. The findings from this study indicate that although BGE-student dialogic interactions occasionally occurred, a noninteractive/authoritative communicative approach was the more common practice of the BGEs when communicating science to students. However, the components of storytelling and the use of analogies identified have a great potential for enriching the explanatory talk of the BGEs. The results of this study suggest that the dynamics of discourse during guided school visits were shaped by both the BGEs and students through the shift of power during this discourse.
Cross-Lingual Abstractive Summarization with Limited Parallel Resources

Parallel cross-lingual summarization data is scarce, requiring models to better use the limited available cross-lingual resources. Existing methods to do so often adopt sequence-to-sequence networks with multi-task frameworks. Such approaches apply multiple decoders, each of which is utilized for a specific task. However, these independent decoders share no parameters, hence fail to capture the relationships between the discrete phrases of summaries in different languages, breaking the connections in order to transfer the knowledge of the high-resource languages to low-resource languages. To bridge these connections, we propose a novel Multi-Task framework for Cross-Lingual Abstractive Summarization (MCLAS) in a low-resource setting. Employing one unified decoder to generate the sequential concatenation of monolingual and cross-lingual summaries, MCLAS makes the monolingual summarization task a prerequisite of the CLS task. In this way, the shared decoder learns interactions involving alignments and summary patterns across languages, which encourages attaining knowledge transfer. Experiments on two CLS datasets demonstrate that our model significantly outperforms three baseline models in both low-resource and full-dataset scenarios. Moreover, in-depth analysis on the generated summaries and attention heads verifies that interactions are learned well using MCLAS, which benefits the CLS task under limited parallel resources.
Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering

ASCENT is a fully automated methodology for extracting and consolidating commonsense assertions from web contents (Nguyen et al., 2021). It advances traditional triple-based commonsense knowledge representation by capturing semantic facets like locations and purposes, and composite concepts, i.e., subgroups and related aspects of subjects. In this demo, we present a web portal that allows users to understand its construction process, explore its content, and observe its impact in the use case of question answering. The demo website (https://ascent.mpi-inf.mpg.de) and an introductory video (https://youtu.be/qMkJXqu_Yd4) are both available online.
Exceptional sign changes of the nonlocal spin Seebeck effect in antiferromagnetic hematite

Low power spintronic devices based on the propagation of pure magnonic spin currents in antiferromagnetic insulator materials offer several distinct advantages over ferromagnetic components including higher frequency magnons and a stability against disturbing external magnetic fields. In this work, we make use of the insulating antiferromagnetic phase of iron oxide, the mineral hematite α-Fe2O3 to investigate the long distance transport of thermally generated magnonic spin currents. We report on the excitation of magnons generated by the spin Seebeck effect, transported both parallel and perpendicular to the antiferromagnetic easy-axis under an applied magnetic field. Making use of an atomistic hematite toy model, we calculate the transport characteristics from the deviation of the antiferromagnetic ordering from equilibrium under an applied field. We resolve the role of the magnetic order parameters in the transport, and experimentally we find significant thermal spin transport without the need for a net magnetization.
Environmental radiation impact on lifetimes and quasiparticle tunneling rates of fixed-frequency transmon qubits

Quantum computing relies on the operation of qubits in an environment as free of noise as possible. This work reports on measuring the impact of environmental radiation on lifetimes of fixed frequency transmon qubits with various capacitor pad geometries by varying the amount of shielding used in the measurement space. It was found that the qubit lifetimes are robust against these shielding changes until the most extreme limit was tested without a mixing chamber shield in the refrigerator. In contrast, the quasiparticle tunneling rates were found to be extremely sensitive to all configurations tested, indicating these devices are not yet limited by losses related to superconducting quasiparticles.
ECG Heart-Beat Classification Using Multimodal Image Fusion

In this paper, we present a novel Image Fusion Model (IFM) for ECG heart-beat classification to overcome the weaknesses of existing machine learning techniques that rely either on manual feature extraction or direct utilization of 1D raw ECG signal. At the input of IFM, we first convert the heart-beats of ECG into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF) and then fuse these images to create a single imaging modality. We use AlexNet for feature ex-traction and classification and thus employ end-to-end deep learning. We perform experiments on PhysioNet’s MIT-BIH dataset for five different arrhythmias in accordance with the AAMI EC57 standard and on PTB diagnostics dataset for myocardial infarction (MI) classification. We achieved an state-of-an-art results in terms of prediction accuracy, precision and recall.
End-to-End Deep Fault-Tolerant Control

Ideally, accurate sensor measurements are needed to achieve a good performance in the closed-loop control of mechatronic systems. As a consequence, sensor faults will prevent the system from working correctly, unless a fault-tolerant control (FTC) architecture is adopted. As model-based FTC algorithms for nonlinear systems are often challenging to design, this article focuses on a new method for FTC in the presence of sensor faults, based on deep learning. The considered approach replaces the phases of fault detection and isolation and controller design with a single recurrent neural network, which has the value of past sensor measurements in a given time window as input and the current values of the control variables as output. This end-to-end deep FTC method is applied to a mechatronic system composed of a spherical inverted pendulum, whose configuration is changed via reaction wheels, in turn actuated by electric motors. The simulation and experimental results show that the proposed method can handle abrupt faults occurring in link position/velocity sensors.
ν Gem: A Hierarchical Triple System with an Outer Be Star

Time series of spectroscopic, speckle-interferometric, and optical long-baseline-interferometric observations confirm that ν Gem is a hierarchical triple system. It consists of an inner binary composed of two B-type stars and an outer classical Be star. Several photospheric spectral lines of the inner components were disentangled, revealing two stars with very different rotational broadening (∼260 and ∼140 km s−1, respectively), while the photospheric lines of the Be star remain undetected. From the combined spectroscopic and astrometric orbital solution it is not possible to unambiguously cross identify the inner astrometric components with the spectroscopic components. In the preferred solution based on modeling of the disentangled line profiles, the inner binary is composed of two stars with nearly identical masses of 3.3 M ⊙ and the more rapidly rotating star is the fainter one. These two stars are in a marginally elliptical orbit (e = 0.06) about each other with a period of 53.8 days. The third star also has a mass of 3.3 M ⊙ and follows a more eccentric (e = 0.24) orbit with a period of 19.1 yr. The two orbits are codirectional, and at inclinations of 79° and 76° of the inner and the outer orbit, respectively, about coplanar. No astrometric or spectroscopic evidence could be found that the Be star itself is double. The system appears dynamically stable and not subject to eccentric Lidov–Kozai oscillations. After disentangling, the spectra of the components of the inner binary do not exhibit peculiarities that would be indicative of past interactions. Motivations for a wide range of follow-up studies are suggested.
Global attractor for the periodic generalized Korteweg-De Vries equation through smoothing

<p style='text-indent:20px;'>We establish a smoothing result for the generalized KdV (gKdV) on the torus with polynomial non-linearity, damping, and forcing that matches the smoothing level for the gKdV at <inline-formula><tex-math id="M1">\begin{document}$ H^1 $\end{document}</tex-math></inline-formula>. As a consequence, we establish the existence of a global attractor for this equation as well as its compactness in <inline-formula><tex-math id="M2">\begin{document}$ H^s(\mathbb{T}) $\end{document}</tex-math></inline-formula>, <inline-formula><tex-math id="M3">\begin{document}$ s\in (1, 2). $\end{document}</tex-math></inline-formula></p>
Novel generic models for differentiating stem cells reveal oscillatory mechanisms

Understanding cell fate selection remains a central challenge in developmental biology. We present a class of simple yet biologically-motivated mathematical models for cell differentiation that generically generate oscillations and hence suggest alternatives to the standard framework based on Waddington’s epigenetic landscape. The models allow us to suggest two generic dynamical scenarios that describe the differentiation process. In the first scenario gradual variation of a single control parameter is responsible for both entering and exiting the oscillatory regime. In the second scenario two control parameters vary: one responsible for entering, and the other for exiting the oscillatory regime. We analyse the standard repressilator and four variants of it and show the dynamical behaviours associated with each scenario. We present a thorough analysis of the associated bifurcations and argue that gene regulatory networks with these repressilator-like characteristics are promising candidates to describe cell fate selection through an oscillatory process.