Miracle-dz/UniFAR-FTUs huggingface dataset:
Hotel Service Quality: The Impact of Service Quality on Customer Satisfaction in Hospitality

Hospitality industry is a billion dollars industry, which includes many activities, from which main is hotel business, tourism services, event planning and transportation. This industry is a quick growing industry, where main factors are service quality and customer satisfaction. No any hospitality industry property will not survive if they are not oriented on their consumers, notably, to meet their needs, requirements and expectations, so that the image of the company will enhance. The hospitality industry faces with different difficulties than organizations which produce products due to the dissimilar nature of service in comparison with a product. In service industry there is a greater probability to fail, rather than in product sales. Service quality has been revealed as a key factor in search for sustainable competitive advantage. Satisfying and retaining customer has been recognized as an important factor in hospitality industry. Nowadays like never before, fulfilling consumers’ requests remains the greatest challenge. In the hospitality industry, the consumer is not only the part of the actual consumption process, but moreover often has preset service and quality perspectives. Today’s hospitality industry customer is increasing time poor, more sophisticated and more demanding. The main purpose of this study is to reveal the impact of service quality on customer satisfaction. The findings of the study will show influence of different service quality dimensions on satisfaction level in Hotels. A quantitative method used to analyze this study. A random sampling method used to distribute and gather data. 111 participants were involved in this study. This study proved that four of service quality dimensions (empathy, responsiveness, assurance and tangible) have positive relation with customer satisfaction, except reliability had negative relation with customer satisfaction.
Cultural Factors Influencing Psychological Contract (PC) Experiences among Survivor Managers

Most researches on psychological contract are conducted in the banking sector while limited local enquiries focused on the experiences of the survivor-managers in developing economy particularly in Nigeria. This study therefore, examines the cultural factors influencing PC experiences and responses among survivor-managers in Nigeria. The study adopted a qualitative approach and more specifically interviews. Data were analyzed using empirically driven thematic analysis. The findings revealed that; unemployment, values of dependency, corruption, family obligations, beliefs, spirituality among others shape attitudes and responses of the survivor-managers' with respect to downsizing exercise. Therefore, human resource managers who are part of the downsizing exercise should embrace open and transparent process as well as being sensitive to the wellbeing of the victims and survivors to reduce the negative psychological contract experiences and responses among Survivor-managers. In addition, the research created awareness on the need to diversify research in terms of methods adopted.
Cutting the Gordian Knot of Carry and Imbalances

Countries' external imbalances or interest rates produce similar currency sorts on average. Disentangling the two signals, we find that carry risk premiums come entirely from interest rates. An alternative carry formed on interest rates orthogonal to external imbalances increases the Sharpe ratio by 38% and is not spanned by traditional carry or imbalance factors. Strikingly, this new orthogonal carry subsumes traditional carry. Traditional carry has well-known exposure to stock market returns and performs badly when FX volatility is high. We find these stylized facts are entirely driven by its association with imbalances factors which have similar risk attributes while orthogonal carry does not.
Instance-Dependent Bounds for Zeroth-order Lipschitz Optimization with Error Certificates

We study the problem of zeroth-order (black-box) optimization of a Lipschitz function $f$ defined on a compact subset $\mathcal X$ of $\mathbb R^d$, with the additional constraint that algorithms must certify the accuracy of their recommendations. We characterize the optimal number of evaluations of any Lipschitz function $f$ to find and certify an approximate maximizer of $f$ at accuracy $\varepsilon$. Under a weak assumption on $\mathcal X$, this optimal sample complexity is shown to be nearly proportional to the integral $\int_{\mathcal X} \mathrm{d}\boldsymbol x/( \max(f) - f(\boldsymbol x) + \varepsilon )^d$. This result, which was only (and partially) known in dimension $d=1$, solves an open problem dating back to 1991. In terms of techniques, our upper bound relies on a packing bound by Bouttier al. (2020) for the Piyavskii-Shubert algorithm that we link to the above integral. We also show that a certified version of the computationally tractable DOO algorithm matches these packing and integral bounds. Our instance-dependent lower bound differs from traditional worst-case lower bounds in the Lipschitz setting and relies on a local worst-case analysis that could likely prove useful for other learning tasks.
Large deviation local limit theorems and limits of biconditioned planar maps

We first establish new local limit estimates for the probability that a nondecreasing integer-valued random walk lies at time $n$ at an arbitrary value, encompassing in particular large deviation regimes. This enables us to derive scaling limits of such random walks conditioned by their terminal value at time $n$ in various regimes. We believe both to be of independent interest. We then apply these results to obtain invariance principles for the Lukasiewicz path of Bienaym\'e-Galton-Watson trees conditioned on having a fixed number of leaves and of vertices at the same time, which constitutes a first step towards understanding their large scale geometry. We finally deduce from this scaling limit theorems for random bipartite planar maps under a new conditioning by fixing their number of vertices, edges, and faces at the same time. In the particular case of the uniform distribution, our results confirm a prediction of Fusy&Guitter on the growth of the typical distances and show furthermore that in all regimes, the scaling limit is the celebrated Brownian map.
Understanding Neural Networks and Individual Neuron Importance via Information-Ordered Cumulative Ablation

In this work, we investigate the use of three information-theoretic quantities—entropy, mutual information with the class variable, and a class selectivity measure based on Kullback–Leibler (KL) divergence—to understand and study the behavior of already trained fully connected feedforward neural networks (NNs). We analyze the connection between these information-theoretic quantities and classification performance on the test set by cumulatively ablating neurons in networks trained on MNIST, FashionMNIST, and CIFAR-10. Our results parallel those recently published by Morcos et al., indicating that class selectivity is not a good indicator for classification performance. However, looking at individual layers separately, both mutual information and class selectivity are positively correlated with classification performance, at least for networks with ReLU activation functions. We provide explanations for this phenomenon and conclude that it is ill-advised to compare the proposed information-theoretic quantities across layers. Furthermore, we show that cumulative ablation of neurons with ascending or descending information-theoretic quantities can be used to formulate hypotheses regarding the joint behavior of multiple neurons, such as redundancy and synergy, with comparably low computational cost. We also draw connections to the information bottleneck theory for NNs.
A Revised Description of the Cosmic Ray Induced Desorption of Interstellar Ices

Nonthermal desorption of ices on interstellar grains is required to explain observations of molecules that are not synthesized efficiently in the gas phase in cold dense clouds. Perhaps the most important nonthermal desorption mechanism is one induced by cosmic rays (CRs), which, when passing through a grain, heat it transiently to a high temperature—the grain cools back to its original equilibrium temperature via the (partial) sublimation of the ice. Current cosmic ray induced desorption (CRD) models assume a fixed grain cooling time. In this work, we present a revised description of CRD in which the desorption efficiency depends dynamically on the ice content. We apply the revised desorption scheme to two-phase and three-phase chemical models in physical conditions corresponding to starless and prestellar cores, and to molecular cloud envelopes. We find that, inside starless and prestellar cores, introducing dynamic CRD can decrease gas-phase abundances by up to an order of magnitude in two-phase chemical models. In three-phase chemical models, our model produces results very similar to those of the static cooling scheme—when only one monolayer of ice is considered active. Ice abundances are generally insensitive to variations in the grain cooling time. Further improved CRD models need to take into account additional effects in the transient heating of the grains—introduced, for example, by the adoption of a spectrum of CR energies.
On the Use of Minimum Penalties in Statistical Learning

Modern multivariate machine learning and statistical methodologies estimate parameters of interest while leveraging prior knowledge of the association between outcome variables. The methods that do allow for estimation of relationships do so typically through an error covariance matrix in multivariate regression which does not scale to other types of models. In this article we proposed the MinPEN framework to simultaneously estimate regression coefficients associated with the multivariate regression model and the relationships between outcome variables using mild assumptions. The MinPen framework utilizes a novel penalty based on the minimum function to exploit detected relationships between responses. An iterative algorithm that generalizes current state of the art methods is proposed as a solution to the non-convex optimization that is required to obtain estimates. Theoretical results such as high dimensional convergence rates, model selection consistency, and a framework for post selection inference are provided. We extend the proposed MinPen framework to other exponential family loss functions, with a specific focus on multiple binomial responses. Tuning parameter selection is also addressed. Finally, simulations and two data examples are presented to show the finite sample properties of this framework.
OODIn: An Optimised On-Device Inference Framework for Heterogeneous Mobile Devices

Radical progress in the field of deep learning (DL) has led to unprecedented accuracy in diverse inference tasks. As such, deploying DL models across mobile platforms is vital to enable the development and broad availability of the next-generation intelligent apps. Nevertheless, the wide and optimised deployment of DL models is currently hindered by the vast system heterogeneity of mobile devices, the varying computational cost of different DL models and the variability of performance needs across DL applications. This paper proposes OODIn, a framework for the optimised deployment of DL apps across heterogeneous mobile devices. OODIn comprises a novel DL-specific software architecture together with an analytical framework for modelling DL applications that: (1) counteract the variability in device resources and DL models by means of a highly parametrised multi-layer design; and (2) perform a principled optimisation of both model- and system-level parameters through a multi-objective formulation, designed for DL inference apps, in order to adapt the deployment to the user-specified performance requirements and device capabilities. Quantitative evaluation shows that the proposed framework consistently outperforms status-quo designs across heterogeneous devices and delivers up to 4.3× and 3.5× performance gain over highly optimised platform- and model-aware designs respectively, while effectively adapting execution to dynamic changes in resource availability.
Single-Server Private Linear Transformation: The Joint Privacy Case

This paper introduces the problem of Private Linear Transformation (PLT) which generalizes the problems of private information retrieval and private linear computation. The PLT problem includes one or more remote server(s) storing (identical copies of) <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> messages and a user who wants to compute <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> independent linear combinations of a <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula>-subset of messages. The objective of the user is to perform the computation by downloading minimum possible amount of information from the server(s), while protecting the identities of the <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> messages required for the computation. In this work, we focus on the single-server setting of the PLT problem when the identities of the <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> messages required for the computation must be protected jointly. We consider two different models, depending on whether the coefficient matrix of the required <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> linear combinations generates a Maximum Distance Separable (MDS) code. We prove that the capacity for both models is given by <inline-formula> <tex-math notation="LaTeX">$L/(K-D+L)$ </tex-math></inline-formula>, where the capacity is defined as the supremum of all achievable download rates. Our converse proofs are based on linear-algebraic and information-theoretic arguments. For each model, we also present an achievability scheme that relies on MDS codes.
Satellite- and Cache-Assisted UAV: A Joint Cache Placement, Resource Allocation, and Trajectory Optimization for 6G Aerial Networks

This paper considers Low Earth Orbit (LEO) satellite- and cache-assisted unmanned aerial vehicle (UAV) communications for content delivery in terrestrial networks, which shows great potential for next-generation systems to provide ubiquitous connectivity and high capacity. Specifically, caching is provided by the UAV to reduce backhaul congestion, and the LEO satellite supports the UAV’s backhaul link. In this context, we aim to maximize the minimum achievable throughput per ground user (GU) by jointly optimizing cache placement, the UAV’s resource allocation, and trajectory while cache capacity and flight time are limited. The formulated problem is challenging to solve directly due to its non-convexity and combinatorial nature. To find a solution, the problem is decomposed into three sub-problems: (1) cache placement optimization with fixed UAV resources and trajectory, followed by (2) the UAV resources optimization with fixed cache placement vector and trajectory, and finally, (3) we optimize the UAV trajectory with fixed cache placement and UAV resources. Based on the solutions of sub-problems, an efficient alternating algorithm is proposed utilizing the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Simulation results show that the max-min throughput and total achievable throughput enhancement can be achieved by applying our proposed algorithm instead of other benchmark schemes.
Grounding inductive biases in natural images: invariance stems from variations in data

To perform well on unseen and potentially out-of-distribution samples, it is desirable for machine learning models to have a predictable response with respect to transformations affecting the factors of variation of the input. Here, we study the relative importance of several types of inductive biases towards such predictable behavior: the choice of data, their augmentations, and model architectures. Invariance is commonly achieved through hand-engineered data augmentation, but do standard data augmentations address transformations that explain variations in real data? While prior work has focused on synthetic data, we attempt here to characterize the factors of variation in a real dataset, ImageNet, and study the invariance of both standard residual networks and the recently proposed vision transformer with respect to changes in these factors. We show standard augmentation relies on a precise combination of translation and scale, with translation recapturing most of the performance improvement -- despite the (approximate) translation invariance built in to convolutional architectures, such as residual networks. In fact, we found that scale and translation invariance was similar across residual networks and vision transformer models despite their markedly different architectural inductive biases. We show the training data itself is the main source of invariance, and that data augmentation only further increases the learned invariances. Notably, the invariances learned during training align with the ImageNet factors of variation we found. Finally, we find that the main factors of variation in ImageNet mostly relate to appearance and are specific to each class.
Self-supervision of Feature Transformation for Further Improving Supervised Learning

Self-supervised learning, which benefits from automatically constructing labels through pre-designed pretext task, has recently been applied for strengthen supervised learning. Since previous self-supervised pretext tasks are based on input, they may incur huge additional training overhead. In this paper we find that features in CNNs can be also used for self-supervision. Thus we creatively design the \emph{feature-based pretext task} which requires only a small amount of additional training overhead. In our task we discard different particular regions of features, and then train the model to distinguish these different features. In order to fully apply our feature-based pretext task in supervised learning, we also propose a novel learning framework containing multi-classifiers for further improvement. Original labels will be expanded to joint labels via self-supervision of feature transformations. With more semantic information provided by our self-supervised tasks, this approach can train CNNs more effectively. Extensive experiments on various supervised learning tasks demonstrate the accuracy improvement and wide applicability of our method.
Exceptional Points in Flat Optics: A Non-Hermitian Line-Wave Scenario

Line waves are recently discovered wave entities that are localized along two directions and therefore can be viewed as the one-dimensional counterpart of surface waves. These waves can be supported at discontinuities of the surface reactance and/or resistance of low-dimensional materials such as metasurfaces or graphene. Here, a broader class of non-Hermitian surface-impedance junctions is studied that can support coupled line waves and that allows the investigation of different one-dimensional waveguiding mechanisms in a unified framework. It is theoretically demonstrated that, under parity–time-symmetry conditions, exceptional points can occur in a truly flat-optics scenario, hence endowing these waveguiding systems with the attractive features of both line-wave and exceptional-point physics and shedding further light on the phase transitions existing in these systems. It is also shown that the required surface-impedance parameters are compatible with those attainable with typical models of photoexcited graphene metasurfaces at terahertz frequencies. Besides providing additional understanding in the physics of line waves, which is still in its infancy, these results pave the way toward intriguing developments in the largely unexplored field of non-Hermitian flat optics, with possible applications ranging from sensing to lasing and on-chip optical signal processing.
Dark exciton-exciton annihilation in monolayer transition-metal dichalcogenides

Daniel Erkensten1,∗ , Samuel Brem, Koloman Wagner, Roland Gillen, Raül Perea-Cauśın, Jonas D. Ziegler, Takashi Taniguchi, Kenji Watanabe, Janina Maultzsch, Alexey Chernikov, and Ermin Malic 1 Department of Physics, Chalmers University of Technology, Gothenburg, Sweden Department of Physics, Philipps-Universität, 35037 Marburg, Germany 3 Department of Physics, University of Regensburg, D-93040 Regensburg, Germany 4 Department of Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen-Nürnberg, Germany 5 International Center for Materials Nanoarchitectonics, National Institute for Materials Science, Tsukuba, Ibaraki 305-004, Japan 6 Research Center for Functional Materials, National Institute for Materials Science, Tsukuba, Ibaraki 305-004, Japan 7 Institute for Applied Physics, Dresden University of Technology, Dresden, 01187, Germany
A Space-Time Trefftz Discontinuous Galerkin Method for the Linear Schrödinger Equation

A space-time Trefftz discontinuous Galerkin method for the Schr\"odinger equation with piecewise-constant potential is proposed and analyzed. Following the spirit of Trefftz methods, trial and test spaces are spanned by non-polynomial complex wave functions that satisfy the Schro\"odinger equation locally on each element of the space-time mesh. This allows for a significant reduction in the number of degrees of freedom in comparison with full polynomial spaces. We prove well-posedness and stability of the method, and, for the one- and two- dimensional cases, optimal, high-order, h-convergence error estimates in a skeleton norm. Some numerical experiments validate the theoretical results presented.
Intrinsic local symmetry-breaking in nominally cubic paraelectric BaTiO3

Whereas low-temperature ferroelectrics have a well understood ordered spatial dipole arrangement, the fate of these dipoles in paraelectric phases remains poorly understood. Using density functional theory (DFT), we find that unlike the case in conventional non-polar ABO$_3$ compounds illustrated here for cubic BaZrO$_3$, the origin of the distribution of the B site off-centering in cubic paraelectric such as BaTiO$_3$ is an intrinsic, energy stabilizing symmetry breaking. Minimizing the internal energy E of a constrained cubic phase already reveals the formation of a distribution of intrinsic local displacements that (i) mimic the symmetries of the low temperature phases, while (ii) being the precursors of what finite temperature DFT Molecular Dynamics finds as thermal motifs. The implications of such symmetry breaking on the microscopic structures and anomalous properties in these kinds of PE materials are discussed.