Asymmetric Setting Research Articles

Chosen-Ciphertext Secure Unidirectional Proxy Re-Encryption Based on Asymmetric Pairings

Proxy re-encryption (PRE) is a cryptographic primitive that extends public key encryption by allowing ciphertexts to be re-encrypted from one user to another without revealing information about the underlying plaintext. This makes it an essential privacy-enhancing technology, as only the intended recipient is able to decrypt sensitive personal information. Previous PRE schemes were commonly based on symmetric bilinear pairings. However, these have been found to be slower and less secure than the more modern asymmetric pairings. To address this, we propose two new PRE scheme variants, based on the unidirectional symmetric pairing-based scheme by Weng et al. and adapted to utilize asymmetric pairings. We employ a known automated black-box reduction technique to transform the base scheme to the asymmetric setting, identify its shortcomings, and subsequently present an alternative manual transformation that fixes these flaws. The adapted schemes retain the properties of the base scheme and are therefore CCA-secure in the adaptive corruption model without the use of random oracles, while being faster, practical, and more secure overall than the base scheme.

Impact of beam asymmetries at the Future Circular Collider e+e−

In this paper, we present detailed simulations with asymmetric initial beam settings in the context of the proposed Future Circular Collider e+e− (FCC-ee) using the framework. We compare simulated equilibrium bunch sizes and luminosities against an already existing analytical model, which shows remarkably good agreement for realistic small perturbations. We investigate the longitudinal top-up injection, the currently preferred injection scheme for the FCC-ee, using self-consistent simulations featuring beam-beam collisions with beamstrahlung and the injection process for the first time. We present and assess the sensitivity and required precision of the nominal beam parameters in a potential real-life operation by providing first estimates of the tolerances in the initial asymmetry of several machine parameters, with respect to the 3D flip-flop mechanism, obtained from parameter scan simulations. Published by the American Physical Society 2024

Evaluating Layout Dimensionalities in PC+VR Asymmetric Collaborative Decision Making

With the commercialization of virtual/augmented reality (VR/AR) devices, there is an increasing interest in combining immersive and non-immersive devices (e.g., desktop computers) for asymmetric collaborations. While such asymmetric settings have been examined in social platforms, significant questions around layout dimensionality in data-driven decision-making remain underexplored. A crucial inquiry arises: although presenting a consistent 3D virtual world on both immersive and non-immersive platforms has been a common practice in social applications, does the same guideline apply to lay out data? Or should data placement be optimized locally according to each device's display capacity? This study aims to provide empirical insights into the user experience of asymmetric collaboration in data-driven decision-making. We tested practical dimensionality combinations between PC and VR, resulting in three conditions: PC2D+VR2D, PC2D+VR3D, and PC3D+VR3D. The results revealed a preference for PC2D+VR3D, and PC2D+VR2D led to the quickest task completion. Our investigation facilitates an in-depth discussion of the trade-offs associated with different layout dimensionalities in asymmetric collaborations.

О субоптимальном решении задачи быстродействия для линейной дискретной системы в случае несимметричных ограничений на управления

<p>The paper considers a linear discrete system with bounded control. The speed-in-action problem is solved for the system, that is, it is required to construct a control process that transfers the system from the initial state to the origin in a minimum number of steps. If the set of acceptable control values has a superellipse structure, then the problem of calculating optimal control can be reduced to solving a system of algebraic equations. A superellipsoidal approximation method has been developed for sets of arbitrary structure, and the case of asymmetric sets has been considered. Examples are given.</p>

Sustainable leadership and knowledge ingredients - the recipe for sustainable competitive advantage: a mixed-method approach

ABSTRACT Drawing on the principles of complexity theory, the research seeks to ascertain the causal configurations that can lead to enhanced Sustainable Competitive Advantage (SCA) within Higher Education Institutions (HEIs). The sample was drawn from HEIs in Pakistan. The research used asymmetrical fuzzy set qualitative comparative analysis (fsQCA), to identify the antecedent conditions that predict high SCA scores in HEIs. The results revealed several configurations that can help HEIs improve SCA, complementing the major tenets of complexity theory. Symmetric methods see SCA as the result of isolated antecedents, they have significant limitations when it comes to understanding SCA, which is a complex construct. This is evident from recent research. Symmetric approaches have fewer theoretical ramifications and are less informative. The study adds to the literature on leadership and knowledge management by highlighting the causal configurations that result in improved SCA. The research will assist management in understanding the complexity of SCA and the possibility that different recipes may be more effective in enhancing SCA than concentrating on individual factors and their effects on HEIs. Managers will find new paths to SCA with the aid of the study.

Exponential Erdős–Szekeres theorem for matrices

AbstractIn 1993, Fishburn and Graham established the following qualitative extension of the classical Erdős–Szekeres theorem. If is sufficiently large with respect to , then any real matrix contains an submatrix in which every row and every column is monotone. We prove that the smallest such is at most , greatly improving the previously best known double‐exponential upper bound of Bucić, Sudakov, and Tran, and matching the best known lower bound on an exponential scale. In particular, we prove the following surprisingly sharp transition in the asymmetric setting. On one hand, every matrix contains an submatrix, in which every row is monotone. On the other hand, there exist matrices containing no such submatrix.

Hybrid financing in a dual‐channel supply chain with asymmetric demand information

AbstractThis study delves into the interaction between hybrid financing and asymmetric demand information within a dual‐channel supply chain. In this setup, the supplier directly sells to customers and also through a capital‐constrained retailer. We investigate a unique financing approach involving a blend of bank loans and supplier equity investment to support the retailer's operational (procurement and marketing) activities. Analyzing the equilibrium strategies under both symmetric and asymmetric information settings yields intriguing insights. In the case of symmetric information, we find that the retailer's equilibrium order quantity decreases with the potential market size under hybrid financing, contrary to traditional notions. When asymmetric information is present, a higher acceptance of supplier equity investment by the retailer tends to lead to order quantity distortion downward, increasing signaling costs. Furthermore, a greater proportion of supplier equity investment prompts the retailer to order more products, ultimately boosting profits for both the retailer and supplier. This suggests that supplier equity investment can enhance supply chain efficiency and alleviate the double marginalization effect.

3D study of dyke-induced asymmetric graben: The 1971 Mt. Etna (Italy) case by structural data and numerical modelling

In this work, we have integrated field data and numerical models to characterise a unique dyke-induced graben system, exposed both in section and plan view, with an unexpected asymmetric fault geometry. This volcanotectonic feature is related to the 1971 eruption of Mt. Etna (southern Italy) and is located near the northern wall of the Valle del Bove, a huge depression carved into the eastern flank of the edifice. A new structural map and quantitative data were obtained from the analysis of aerial stereophotos collected before the onset of the 1971 eruption and after, high-resolution drone-derived models and field surveys carried out in the summer of 2022. In plan view, the graben is 2-km-long and its width ranges 27–143 m from the bottom to the upper part of the section view, with about 82 m of difference in elevation from top to bottom. Graben faults clearly show an asymmetric setting in terms of attitude, with one fault that dips 70° to the south, and the other one that dips 50° to the north. Vertical offset values are greater at higher elevations. We also ran a set of numerical models, aimed at investigating the distribution and orientation of stresses around the inferred dyke tip and in the host rock. The comparison between field data and numerical models suggests a key role of the inclined topography, as shown in section view, in determining the orientation of dyke-induced σ1 and σ3 in the host rock. This, in turn, controls the geometry of the graben faults, resulting in the observed asymmetric setting. Additionally, dyke-induced stress concentrations and vertical offset values support the hypothesis of a downward propagation of the graben faults, from the surface down to the dyke tip.

Flame transfer functions of asymmetric conical and V-shaped flames

Transfer functions of asymmetric flames subjected to incident flow perturbations are explored using the classical kinematic model, the G-equation, parameterized by flame tip angle α, flame-base incline angle β, and eccentricity e. With perturbations of various frequencies, we analyze perturbed asymmetric flames and derive analytical formulations of Flame Transfer Functions (FTF) for various flame conditions. It is observed that the gain of the asymmetric conical flame is increased compared to the symmetric case. Meanwhile, the amplification effect of the V-shaped flame is considerably suppressed as the flame becomes asymmetric. That means with a proper asymmetric geometry setting, the V-shaped flame no longer tends to amplify the perturbation and in other words becomes less susceptible to combustion instability. These findings have significant value for the future design and optimization of propulsion systems, where improving flame stability and reducing thermoacoustic instabilities are critical. Subsequently, a numerical study is conducted to verify the FTF results and examine the response of asymmetric flames under various perturbation levels. It has been demonstrated that the asymmetric conical flame is minimally affected by the amplitude of these perturbations, and the asymmetric V-shaped flame is highly sensitive to the convective amplitudes.

Surface-Level Analysis of International Economic Agreements and Their Strangulatory Impact on Pakistan Economy

The international economic system that evolved after World War II, which was conceived by and rolled out on behalf of Global North states, essentially stood on a tripod of international economic agreements, that is, International Investment Agreements, Double Taxation Agreements, and International Trade Agreements. The ostensible purpose of these agreements was to (a) facilitate cross-border deployment of surplus investible capital, (b) export of finished and capital goods, and (c) allocate favorably taxing rights between states on various incomes earned by multinational enterprises in overseas jurisdictions. Intuitively, when these agreements are signed by and between states at par level of economic development, their resultant economic outcomes would be equal or near-equal. But when such agreements are signed in asymmetrical bilateral economic settings, that is, between a developed and a developing country, the resultant economic outcomes would most likely be lopsided and inimical to the economic interests of weaker partner in the equation. From the global south perspective, these agreements expose developing countries to international arbitration on account of suits filed by investors, erode their tax base by allowing multinational corporations to enter aggressive tax planning ploys, and undermine their export competitiveness vis-à-vis stronger economies. The paper premises that developing countries that signed more of these agreements, would find themselves in a deeper trouble than those which signed less of them, and that countries that signed the agreements in a quicker span of time, got into trouble quicker, too. In this connection, Pakistan is brought in to undertake a surface-level time-series analysis of its signing agreements and the corresponding trajectory of select economic indicators over the past seven decades.

Unveiling asymmetry impacts of economic policy uncertainty on climate change: fresh insights into African Countries.

This study investigates the influence of economic policy uncertainty on climate change in selected African countries within asymmetric settings. Although previous research has examined the impact of various economic factors on climate change, the asymmetric effects of economic policy uncertainty have not been thoroughly explored, particularly in African countries. We analyze annual data spanning from 1980 to 2017 by utilizing three models: Panel Pooled Mean Group-Autoregressive distributed lag model (ARDL-PMG), Panel Pooled Mean Group-non-linear autoregressive distributed lag model (NARDL-PMG), and Dumitrescu-Hurlin asymmetric causality tests. According to the results of ARDL-PMG estimation, economic policy uncertainty has a detrimental impact on climate change in the long run. However, the NARDL-PMG estimation suggests that a positive shock in economic policy uncertainty negatively affects long-term climate change mitigation. However, a negative shock has a beneficial effect on climate change in the long term. In African nations, positive and negative changes in economic policy uncertainty failed to generate any significant climate change effects in the short run. The results also reveal that both positive and negative shocks in economic policy may cause climate change in a one-way direction. Based on the findings of our study, we recommend that African policymakers implement programs aimed at reducing economic policy uncertainties to help mitigate the effects of climate change.

Learning-based adaption of robotic friction models

In the Fourth Industrial Revolution, wherein artificial intelligence and the automation of machines occupy a central role, the deployment of robots is indispensable. However, the manufacturing process using robots, especially in collaboration with humans, is highly intricate. In particular, modeling the friction torque in robotic joints is a longstanding problem due to the lack of a good mathematical description. This motivates the usage of data-driven methods in recent works. However, model-based and data-driven models often exhibit limitations in their ability to generalize beyond the specific dynamics they were trained on, as we demonstrate in this paper. To address this challenge, we introduce a novel approach based on residual learning, which aims to adapt an existing friction model to new dynamics using as little data as possible. We validate our approach by training a base neural network on a symmetric friction data set to learn an accurate relation between the velocity and the friction torque. Subsequently, to adapt to more complex asymmetric settings, we train a second network on a small dataset, focusing on predicting the residual of the initial network’s output. By combining the output of both networks in a suitable manner, our proposed estimator outperforms the conventional model-based approach, an extended LuGre model, and the base neural network significantly. Furthermore, we evaluate our method on trajectories involving external loads and still observe a substantial improvement, approximately 60%–70%, over the conventional approach. Our method does not rely on data with external load during training, eliminating the need for external torque sensors. This demonstrates the generalization capability of our approach, even with a small amount of data – less than a minute – enabling adaptation to diverse scenarios based on prior knowledge about friction in different settings.

Handling Overlapping Asymmetric Data Sets—A Twice Penalized P-Spline Approach

Aims: Overlapping asymmetric data sets are where a large cohort of observations have a small amount of information recorded, and within this group there exists a smaller cohort which have extensive further information available. Missing imputation is unwise if cohort size differs substantially; therefore, we aim to develop a way of modelling the smaller cohort whilst considering the larger. Methods: Through considering traditionally once penalized P-Spline approximations, we create a second penalty term through observing discrepancies in the marginal value of covariates that exist in both cohorts. Our now twice penalized P-Spline is designed to firstly prevent over/under-fitting of the smaller cohort and secondly to consider the larger cohort. Results: Through a series of data simulations, penalty parameter tunings, and model adaptations, our twice penalized model offers up to a 58% and 46% improvement in model fit upon a continuous and binary response, respectively, against existing B-Spline and once penalized P-Spline methods. Applying our model to an individual’s risk of developing steatohepatitis, we report an over 65% improvement over existing methods. Conclusions: We propose a twice penalized P-Spline method which can vastly improve the model fit of overlapping asymmetric data sets upon a common predictive endpoint, without the need for missing data imputation.

Determinants of Users' Intentions to Use AI-Enabled Technological Innovations in Hotels: A hybrid approach using PLS-SEM and fsQCA

This study investigates the factors influencing hotel guests' intentions to adopt next-generation technologies enabled by artificial intelligence (AI). Both affective and cognitive processes, which led to guests' intentions to adopt these new technologies, were considered to have antecedents in the form of intrinsic and extrinsic motives, respectively. The data collected from 331 respondents were analyzed using a combination of methods, including the asymmetrical fuzzy set qualitative comparative analysis (fsQCA) and the symmetrical partial least square-structural equation modeling (PLS-SEM). The results of the symmetrical study indicated that novelty and compatibility have a good impact on both enjoyment and usefulness, which ultimately lead to behavioral intentions. In contrast, asymmetrical studies have shown that all the criteria are necessary conditions to produce users' intention to embrace AI-based technology. By integrating IDT and TAM, this study extends the comprehension of factors driving customers to use AI-enabled technologies during their hotel stays. This study also adds to the existing literature by exploring configurational modeling with fsQCA, as opposed to prior studies that have relied on net impact modeling via SEM.

Statistical inference for a novel distribution using ranked set sampling with applications

Working on symmetrical or asymmetrical data is complicated since each requires a different probability density function. Many statistical distributions can be used for these data types, where choosing one should be satisfied with the correct data type. So, we apply the ranked set sampling technique, which is essential in gaining data when dealing with units in a population is expensive. However, their classification is simple according to the variable of interest. The Unit Generalized Rayleigh distribution has recently played a crucial role in analyzing symmetrical or asymmetrical complex data sets specifically in modeling claim and risk data used in actuarial and financial studies, and its density can take different symmetric and asymmetric possible shapes. It is proposed in various areas, such as reliability, survival, economics, actuarial science, and insurance. We applied the ranked set sampling design in this article for gaining the model parameter estimations of the unit generalized Rayleigh model. Different estimation procedures and risk measures are computed. Moreover, the performance of these measures is illustrated via numerical simulation experiments. Under different proposed estimators, we conduct the validation of the suggested ranked set sampling design via numerous Monte Carlo simulation experiments by computing average bias and mean squared errors. At the end, we illustrated two real applications of the financial area for demonstrating the potential and the supremacy of the proposed ranked set sampling estimators.

Masticatory Muscles Activation and TMJ Space During Asymmetrically Loaded Jaw Closing

Masticatory muscle activation and temporomandibular joint (TMJ) load generated during asymmetrically loaded jaw closing are largely unknown. Two different strategies were developed to explain how the central nervous system (CNS) generates muscle activation patterns during motion: minimization of joint load (MJL) vs. minimization of muscle effort (MME). The aim of the present study was to investigate, experimentally, the neuromuscular strategy selected by the CNS to coordinate jaw closing in reaction to the application of an external asymmetric load. Masticatory muscle activation was measured with electromyography (EMG) and the minimum intra-articular distance (MID) was assessed by dynamic stereometry to infer joint loading. Ten healthy subjects performed jaw-closing movements against an asymmetric mandibular load set from 0.0 to 2.0 kg in 0.5-kg steps. Recordings were analyzed by exploratory and graphical statistical tools. Moreover, the observed differences in MID and EMG among the various mandibular loads were tested using non-parametric tests for repeated measures data. The ipsilateral-contralateral differences in MID and EMG of the anterior temporalis showed a significant increase (p < 0.001, p = 0.01) with increasing asymmetrical load with both joints being most heavily loaded at 1 kg. EMG signals of the masseter did not change significantly with increasing load. This study is the first to have analyzed the changes in the TMJ intra-articular space during asymmetrically loaded jaw-closing movements, not only three dimensionally and dynamically, but also combined with EMG. Asymmetrical load affected the TMJ space and masticatory muscle activation patterns, primarily resulting in an increased activation of the anterior temporalis muscle. This might suggest the involvement of a control mechanism to protect the joints from overloading. However, the results do not fully support the hypothesis of MJL nor the MME strategy.

A New Cosine-Originated Probability Distribution with Symmetrical and Asymmetrical Behaviors: Repetitive Acceptance Sampling with Reliability Application

Several new acceptance sampling plans using various probability distribution methods have been developed in the literature. However, there is no published work on the design of new sampling plans using trigonometric-based probability distributions. In order to cover this amazing and fascinating research gap, we first introduce a novel probabilistic method called a new modified cosine-G method. A special member of the new modified cosine-G method, namely, a new modified cosine-Weibull distribution, is examined and implemented. The density function of the new model possesses symmetrical as well as asymmetrical behaviors. The usefulness and superior fitting power of the new modified cosine-Weibull distribution are demonstrated by analyzing an asymmetrical data set. Furthermore, based on the new modified cosine-Weibull distribution, we develop a new repetitive acceptance sampling strategy for attributes with specified shape parameters. Finally, a real-world application is presented to illustrate the proposed repetitive acceptance sampling strategy.

National or third-party manufacturer? Sourcing strategy of a dominant platform: Signaling game's perspective

The dominant online platforms (e.g., Amazon and JD) in practice have adopted a strategy of introducing their own store brands as a means to engage in competition with national brands inside consumer markets. However, whether to source store brands from national or third-party manufacturers is still a dilemma for platforms. This paper investigates the online platform's optimal sourcing decision for the store brand when the platform possesses private information about the degree of consumer acceptance of the store brand. This paper develops a signaling game in which the better-informed dominant-platform may reveal its private information through profit margins and highlight the signaling effect of price on the sourcing strategy. Due to the signaling cost, we find that asymmetric information has the potential to inhibit the platform from cooperating with the national manufacturer to produce the store brand. Furthermore, the existence of information asymmetry reduces the profit of the platform with superior information, while simultaneously boosting the profit of the less-informed national manufacturer. Interestingly, our findings reveal that under an asymmetric information setting, a higher level of information asymmetry might negatively impact the platform, which contrasts with the situation when information is symmetric. Consequently, the platform gains more advantages by cooperating with a third-party manufacturer with a moderate level of information asymmetry.

On the Power of Gradual Network Alignment Using Dual-Perception Similarities.

Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes. Our study is motivated by the fact that, since most of existing NA methods have attempted to discover all node pairs at once, they do not harness information enriched through interim discovery of node correspondences to more accurately find the next correspondences during the node matching. To tackle this challenge, we propose Grad-Align, a new NA method that gradually discovers node pairs by making full use of node pairs exhibiting strong consistency, which are easy to be discovered in the early stage of gradual matching. Specifically, Grad-Align first generates node embeddings of the two networks based on graph neural networks along with our layer-wise reconstruction loss, a loss built upon capturing the first-order and higher-order neighborhood structures. Then, nodes are gradually aligned by computing dual-perception similarity measures including the multi-layer embedding similarity as well as the Tversky similarity, an asymmetric set similarity using the Tversky index applicable to networks with different scales. Additionally, we incorporate an edge augmentation module into Grad-Align to reinforce the structural consistency. Through comprehensive experiments using real-world and synthetic datasets, we empirically demonstrate that Grad-Align consistently outperforms state-of-the-art NA methods.

Strategic investment under uncertainty: why multi-option firms lose the preemption run

We consider a dynamic duopoly game where firms choose both the timing and size of their investments. The existing real options literature predominantly consists of contributions where firms have a single option to invest. This paper relaxes this assumption by giving Firm A multiple options to undertake further investments with the purpose to expand whereas Firm B only holds the option to enter the market. In this asymmetric setting we get the surprising result that, in equilibrium, Firm B invests first. If Firm A invests first, Firm A and Firm B keep on being involved in preemption games for subsequent investments until Firm B enters the market, which leads to inefficiently early investments of Firm A. When Firm B invests first, then only one preemption game is played, which leads to Firm A being free to choose its unrestricted optimal investment moments.