Mutual Objectives Research Articles

Synergies of Success: Unveiling the Transformative Influence of Sponsorship on Event Excellence

This study delves into the intricate dynamics of sponsorship strategies, media exposures, and event organizer practices within the Malaysian events industry. Employing a quantitative research approach, the research sampled 110 corporate sponsor representatives, utilizing a structured questionnaire to collect data. Analysis, carried out using Statistical Packages for Social Science Version 26.0 (SPSS Version 26.0), incorporated both descriptive and inferential methods. The findings illuminate a significant positive relationship between sponsorship strategies and media exposure with event success, accompanied by a noteworthy negative relationship between event organizer practices and event success. These results underscore the pivotal role of sponsorship and media exposure in influencing successful events, providing valuable insights for corporate sponsors and event organizers alike. Importantly, the study emphasizes the need for organizations to reassess their sponsorship approaches, fostering successful events by fortifying policies and aligning mutual objectives. As the events landscape continues to evolve, this research contributes to the broader discourse, guiding industry practitioners in optimizing strategies for sustainable success in the dynamic Malaysian events industry.

The Future of U.S.-India Private Sector Space Collaboration

India and the United States have dramatically deepened their cooperation on space exploration and technology in recent years. The two countries have codified their collaboration in the space domain through bilateral and multilateral agreements, including the U.S.-India initiative on Critical and Emerging Technology (iCET) and the Artemis Accords. At the same time, private companies are playing an increasingly prominent role in each country’s civilian, military, and commercial space ambitions. While accounting for the differences in maturity and growth between each country’s private space sector, this paper examines the barriers and incentives to collaboration between the Indian and American private space sectors. This paper's recommendations aim to guide policymakers from both countries in bridging their respective private sectors to advance mutual objectives.

India-hina Trade and Partnership in Global Harmony

In China and India, the titans of Asia are emerging. Both nations are the most populous in the world, and their populations are increasing at the fastest rate. Additionally, their bilateral relationship has strengthened as a result of the significant expansion of China and India's global diplomatic and economic influence. For centuries, China and India have coexisted in harmony, as they are two of the earliest cultures in the world. Both countries have made efforts to establish fruitful political, diplomatic, and economic relationships. India's policy and security connections have been fortified by the two nations, and China has emerged as its largest trading partner. China has become India's largest trading partner, as the two nations strive to expand their economic and military connections. The economic relationship between the two countries is anticipated to be one of the strongest bilateral relations in the current global economic situation, and this development is expected to continue in the coming years. Both nations are currently in the process of developing a variety of issues, such as military, fiscal, cultural, and diplomatic cooperation. The two administrations maintain a substantial relationship; their respective roles in critical matters are considerably more well-defined, and their mutual objectives are considerably more apparent.

On-demand orbital angular momentum comb from a digital laser

Photonic orbital angular momentum (OAM) carried by phase-structured vortex light is an important and promising resource for the ever-increasing demand towards high-capacity data information due to its intrinsic unlimited dimensionality. Large superpositions of OAM are easy to be produced, but on-demand generation of arbitrary OAM spectra such as an OAM comb similar to a frequency comb is still a challenge; especially, the on-demand OAM comb and arbitrary multi-OAM modes have not yet been realized at the source. Here we report a versatile at-source strategy for developing a flexibly and dynamically switchable on-demand digital OAM comb laser for the first time, to our knowledge, by controlling the phase degree of freedom itself rather than any proxy. For this aim, we present a crucial design idea that a nested ring cavity configuration is composed of a degenerate cavity embedded into a stable ring cavity and a pair of conjugate two-fold symmetric multi-spiral-phase digital holographic mirrors loaded onto reflective phase-only spatial light modulators. In the nested ring cavity, the stable ring cavity and the degenerate cavity meet the requirements of high spatial coherence and supporting any transverse mode, respectively. The paired conjugate holographic mirrors located in mutual object and image planes circumvent the competing issue among different OAM modes and control the number and chirality of modes in OAM combs with ease. Our strategy has also universality as it has the ability of encoding OAM spectra with arbitrary distribution. The realization of a dynamic on-demand multi-OAM-mode laser is an important progress in the infancy of multi-OAM-mode sources. Our idea provides a promising solution for development of emerging high-dimensional technologies; in the future, there will be increasing opportunities in the fundamentals and applications of high-dimensional OAM modes, and beyond. Our strategy not only contributes to the development of new laser technology, but also provides a toolbox for both linear and nonlinear generation of the multiple OAM modes at the source.

MMFG: Multimodal-based Mutual Feature Gating 3D Object Detection

To address the problem that image and point cloud features are fused in a coarse fusion way and cannot achieve deep fusion, this paper proposes a multimodal 3D object detection architecture based on a mutual feature gating mechanism. First, since the feature aggregation approach based on the set abstraction layer cannot obtain fine-grained features, a point-based self-attention mechanism module is designed. This module is added to the extraction branch of point cloud features to achieve fine-grained feature aggregation while maintaining accurate location information. Second, a new gating mechanism is designed for the deep fusion of image and point cloud. Deep fusion is achieved by mutual feature weighting between the image and the point cloud. The newly fused features are then fed into a feature refinement network to extract classification confidence and 3D target bounding boxes. Finally, a multi-scale detection architecture is proposed to obtain a more complete object shape. The location-based encoding feature algorithm is also designed to focus the interest points in the region of interest adaptively. The whole architecture shows outstanding performance on the KITTI3D and nuSenece datasets, especially at the difficult level. It shows that the framework solves the problem of low detection rates in LiDAR mode due to the low number of surface points obtained from distant objects.

The Evolution and Review of Regulatory Changes on Emerging Asian Mutual Fund Industry Post-Global Financial Crisis

The proposed research paper aims to explore the significant transformations in the mutual fund industry following the global financial crisis of 2007-2009, with a particular focus on regulatory changes and their effects on mutual fund performance and investor confidence. The study will examine how new liquidity related regulations, such as enhanced liquidity-related disclosures, have contributed to the industry's resilience and growth. Additionally, the paper will analyze the disparity in mutual fund objectives and compositions across different regions, specifically comparing the dominant markets of the United States, and Emerging Asian, markets. By investigating the evolution of open-ended and exchange-traded funds as the fastest-growing investment vehicles post-crisis, this research aims to provide a comprehensive understanding of the current state and future prospects of the global mutual fund industry.

Boundary-aware information maximization for self-supervised medical image segmentation

Self-supervised representation learning can boost the performance of a pre-trained network on downstream tasks for which labeled data is limited. A popular method based on this paradigm, known as contrastive learning, works by constructing sets of positive and negative pairs from the data, and then pulling closer the representations of positive pairs while pushing apart those of negative pairs. Although contrastive learning has been shown to improve performance in various classification tasks, its application to image segmentation has been more limited. This stems in part from the difficulty of defining positive and negative pairs for dense feature maps without having access to pixel-wise annotations.In this work, we propose a novel self-supervised pre-training method that overcomes the challenges of contrastive learning in image segmentation. Our method leverages Information Invariant Clustering (IIC) as an unsupervised task to learn a local representation of images in the decoder of a segmentation network, but addresses three important drawbacks of this approach: (i) the difficulty of optimizing the loss based on mutual information maximization; (ii) the lack of clustering consistency for different random transformations of the same image; (iii) the poor correspondence of clusters obtained by IIC with region boundaries in the image. Toward this goal, we first introduce a regularized mutual information maximization objective that encourages the learned clusters to be balanced and consistent across different image transformations. We also propose a boundary-aware loss based on cross-correlation, which helps the learned clusters to be more representative of important regions in the image. Compared to contrastive learning applied in dense features, our method does not require computing positive and negative pairs and also enhances interpretability through the visualization of learned clusters.Comprehensive experiments involving four different medical image segmentation tasks reveal the high effectiveness of our self-supervised representation learning method. Our results show the proposed method to outperform by a large margin several state-of-the-art self-supervised and semi-supervised approaches for segmentation, reaching a performance close to full supervision with only a few labeled examples.

A Twist for Graph Classification: Optimizing Causal Information Flow in Graph Neural Networks

Graph neural networks (GNNs) have achieved state-of-the-art results on many graph representation learning tasks by exploiting statistical correlations. However, numerous observations have shown that such correlations may not reflect the true causal mechanisms underlying the data and thus may hamper the ability of the model to generalize beyond the observed distribution. To address this problem, we propose an Information-based Causal Learning (ICL) framework that combines information theory and causality to analyze and improve graph representation learning to transform information relevance to causal dependence. Specifically, we first introduce a multi-objective mutual information optimization objective derived from information-theoretic analysis and causal learning principles to simultaneously extract invariant and interpretable causal information and reduce reliance on non-causal information in correlations. To optimize this multi-objective objective, we enable a causal disentanglement layer that effectively decouples the causal and non-causal information in the graph representations. Moreover, due to the intractability of mutual information estimation, we derive variational bounds that enable us to transform the above objective into a tractable loss function. To balance the multiple information objectives and avoid optimization conflicts, we leverage multi-objective gradient descent to achieve a stable and efficient transformation from informational correlation to causal dependency. Our approach provides important insights into modulating the information flow in GNNs to enhance their reliability and generalization. Extensive experiments demonstrate that our approach significantly improves the robustness and interpretability of GNNs across different distribution shifts. Visual analysis demonstrates how our method converts informative dependencies in representations into causal dependencies.

Security screening metrics for information-sharing partnerships.

Recent history has shown both the benefits and risks of information sharing among firms. Information is shared to facilitate mutual business objectives. However, information sharing can also introduce security-related concerns that could expose the firm to a breach of privacy, with significant economic, reputational, and safety implications. It is imperative for organizations to leverage available information to evaluate security related to information sharing when evaluating current and potential information-sharing partnerships. The "fine print" or privacy policies of firms can provide a signal of security across a wide variety of firms being considered for new and continued information-sharing partnerships. In this article, we develop a methodology to gauge and benchmark information security policies in the partner-selection process that can help direct risk-based investments in information sharing security. We develop a methodology to collect and interpret firm privacy policies, evaluate characteristics of those policies by leveraging natural language processing metrics and developing benchmarking metrics, and understand how those characteristics relate to one another in information-sharing partnership situations. We demonstrate the methodology on 500 high-revenue firms. The methodology and managerial insights will be of interest to risk managers, information security professionals, and individuals forming information sharing agreements across industries.

Controllable augmentations for video representation learning

This paper focuses on self-supervised video representation learning. Most existing approaches follow the contrastive learning pipeline to construct positive and negative pairs by sampling different clips. However, this formulation tends to bias the static background and has difficulty establishing global temporal structures. The major reason is that the positive pairs, i.e., different clips sampled from the same video, have limited temporal receptive fields, and usually share similar backgrounds but differ in motions. To address these problems, we propose a framework to jointly utilize local clips and global videos to learn from detailed region-level correspondence as well as general long-term temporal relations. Based on a set of designed controllable augmentations, we implement accurate appearance and motion pattern alignment through soft spatio-temporal region contrast. Our formulation avoids the low-level redundancy shortcut with an adversarial mutual information minimization objective to improve the generalization ability. Moreover, we introduce local-global temporal order dependency to further bridge the gap between clip-level and video-level representations for robust temporal modeling. Extensive experiments demonstrate that our framework is superior on three video benchmarks in action recognition and video retrieval, and captures more accurate temporal dynamics.

CCGIB: A Cross-Channel Graph Information Bottleneck Principle.

The empirical studies of most existing graph neural networks (GNNs) broadly take the original node feature and adjacency relationship as single-channel input, ignoring the rich information of multiple graph channels. To circumvent this issue, the multichannel graph analysis framework has been developed to fuse graph information across channels. How to model and integrate shared (i.e., consistency) and channel-specific (i.e., complementarity) information is a key issue in multichannel graph analysis. In this article, we propose a cross-channel graph information bottleneck (CCGIB) principle to maximize the agreement for common representations and the disagreement for channel-specific representations. Under this principle, we formulate the consistency and complementarity information bottleneck (IB) objectives. To enable optimization, a viable approach involves deriving variational lower bound and variational upper bound (VarUB) of mutual information terms, subsequently focusing on optimizing these variational bounds to find the approximate solutions. However, obtaining the lower bounds of cross-channel mutual information objectives proves challenging through direct utilization of variational approximation, primarily due to the independence of the distributions. To address this challenge, we leverage the inherent property of joint distributions and subsequently derive variational bounds to effectively optimize these information objectives. Extensive experiments on graph benchmark datasets demonstrate the superior effectiveness of the proposed method.

Building strategic value with your medical group

Despite massive investments by health systems in employed physician networks, these networks are often poorly aligned with the health system. At their worst, they operate as a loose conglomeration of practices, with suboptimal quality, financial and operational performance. This paper discusses Mon Health, a growing five-hospital system in West Virginia, and the development of its transformation plan. That effort first addressed the development of a vision of how the physician network would evolve to meet the mutual objectives of the stakeholders and the demands of the market, defined jointly by physicians and executives. The resulting roadmap further addressed issues like quality, provider well-being, strategic growth and physician leadership. The organisation ensured employed physicians would be integrated into the organisation’s leadership and operations, through vehicles like the physician leadership council and dyad leadership of services. Tactics include engaging physicians in service line leadership dyads and advisory board roles, building a shared vision for evolution, defining behavioural expectations for a common culture, and building the management infrastructure to drive these initiatives. The paper also addresses early performance improvements facilitated by this initiative. Ultimately, we will provide a road map for developing a transformation plan to build an accountable, multi-specialty group.

Factors Influencing Engagement in Hybrid Virtual and Augmented Reality

Hybridity in immersive technologies has not been studied for factors that are likely to influence engagement. A noticeable factor is the spatial enclosure that defines where users meet. This involves a mutual object of interest, contents that the users may generate around the object, and the proximity between users. This study examines these factors, namely how object interactivity, user-generated contents (UGC) and avatar proximity influence engagement. We designed a Hybrid Virtual and Augmented Reality (HVAR) environment that supports paired users to experience cultural heritage in both Virtual Reality (VR) and Augmented Reality (AR) . A user study was conducted with 60 participants, providing assessments of engagement and presence via questionnaires, together with mobile electroencephalogram (mEEG) and user activity data that measures VR user engagement in real-time. Our findings provide insights into how engagement between users can occur in HVAR environments for the future hybrid reality with multi-device connectivity.

ACEH AND IRAN BUSINESS COMMUNICATION BUILD

The natural resources in Aceh is flooded, nonetheless, the local government of Aceh management, utilization and effort still not yet maximized. The agriculture, farming and husbandry, fishery, services and air transportations in Aceh could be handled and managed maximally. The land area of Aceh which is reached 2.3 hectares can be utilized for multipurpose. Additionally, the Islamic Laws products in Aceh are doubtful as well, particularly in cosmetics and food industry sectors. And the other hand, Islamic Republic of Iran which is considered as one of most strategic country in the world could be a solution for Aceh because of; Aceh needs to some of petrochemical and oil derivative products to support the constructions and infrastructure sectors and the other side Iran could be as a west Asia’s gate to distribute some of strategic commodities from Aceh such as CPO and paper and certainly in education sector Iran seemly could be beneficial for Acehnese students. The theory that used in this article was based on business communication and Para diplomacy concept. The result shown that are a lot of sectors that are potentially recognized as the strips of cooperation between Aceh and Islamic Republic of Iran. The collaborations between Iranian outstanding technology and Aceh natural resources will be tremendous way to reach the mutual objectives of both sides.

Fair Representation Learning for Recommendation: A Mutual Information Perspective

Recommender systems have been widely used in recent years. By exploiting historical user-item interactions, recommender systems can model personalized potential interests of users and have been widely applied to a wide range of scenarios. Despite their impressive performance, most of them may be subject to unwanted biases related to sensitive attributes (e.g., race and gender), leading to unfairness. An intuitive idea to alleviate this problem is to ensure that there is no mutual information between recommendation results and sensitive attributes. However, keeping independence conditions solely achieves fairness improvement while causing an obvious degradation of recommendation accuracy, which is not a desired result. To this end, in this paper, we re-define recommendation fairness with a novel two-fold mutual information objective. In concerned details, we define fairness as mutual information minimization between embeddings and sensitive information, and mutual information maximization between embeddings and non-sensitive information. Then, a flexible Fair Mutual Information (FairMI) framework is designed to achieve this goal. FairMI first employs a sensitive attribute encoder to capture sensitive information in the data. Then, based on results from the sensitive attribute encoder, an interest encoder is developed to generate sensitive-free embeddings, which are expected to contain rich non-sensitive information of input data. Moreover, we propose novel mutual information (upper/lower) bounds with contrastive information estimation for model optimization. Extensive experiments over two real-world datasets demonstrate the effectiveness of our proposed FairMI in reducing unfairness and improving recommendation accuracy simultaneously.

Mutual physical state-aware object handover in full-contact collaborative human-robot construction work

Full-contact physical interactions inherent in typical construction workflow, such as material handovers, have yet to be adequately resolved or adopted in Human–Robot Collaboration (HRC) due to safety concerns. Replicating protective behavior norms for robots can help achieve safe robot material handovers to human workers. To build such a human-adaptive model, firstly, we present a comprehensive receiver grip state indicator that encompasses both gripping strength and gestures with whole-hand tactile sensors. Secondly, a Learning from Demonstration (LfD) model built to replicate the human grip state-reactive behavior norms for robots is described. The proposed method outperforms other robot-to-human object handover methods using only one-shot demonstrations of natural handovers. Additionally, the LfD-based programming interface is accessible to construction workers without programming expertise and can continuously collect data for a future large-scale LfD model covering a wide range of handover materials, users, and gestures to further enhance worker safety during close-proximity material handovers.

Imbalance Reduction of P2P Energy Market by Closed-Loop Clustering and Forecasting

Energy forecasting at the individual level remains a technical barrier for the peer-to-peer (P2P) energy market. Forecasting errors will cause energy imbalances between real-time delivery and contracts. To reduce the economic loss, this paper proposes an imbalance reduction method based on hierarchical energy forecasting. Existing hierarchical forecasting usually cluster prosumers based on the similarities of their energy profiles. However, minimising the variance cannot guarantee reductions in energy imbalances. This paper explores a novel closed-loop clustering (CLC) algorithm, which aligns the mutual objectives of clustering and imbalance reduction through the introduction of a feedback mechanism. The proposed method is applied in a simulated P2P energy market to cluster photovoltaics (PVs) into virtual groups (VG). The output of each VG is predicted and traded with flexible demands as an entity. The results show that CLC could reduce up to 25.70% energy imbalance compared with individual trading. Network constraints have shown minor impact on the trading results.

APD: Learning Diverse Behaviors for Reinforcement Learning Through Unsupervised Active Pre-Training

Unsupervised pre-training in reinforcement learning enables the agent to gain prior environmental knowledge, which is then fine-tuned in the supervised stage to quickly adapt to various downstream tasks. In the absence of task-related rewards, pre-training aims to acquire policies (i.e., behaviors) that generate different trajectories to explore and master the environment. Previous research categorizes states into their associated behaviors by learning a supervised discriminator. However, an underlying problem persists: such discriminator is trained in lack of relevant data, leading to an underestimation of reward for new states and inadequate exploration. To this end, we introduce an unsupervised active pre-training algorithm for diverse behavior induction (APD). We explicitly characterize the behavior variables with a state-dependent sampling method, and the agent can decompose the entire state space into parts for fine-grained and diverse behavior learning. Specifically, a particle-based entropy estimator is applied to optimize a combination of behavioral entropy and mutual information objective. Moreover, we develop behavior-based representation learning to compress states into the latent space. Experiments show that our method can improve exploration efficiency and outperforms most state-of-the-art unsupervised algorithms on a number of continuous control tasks in the DeepMind Control Suite.

Optimal decentralized coordinated operation of electric vehicle aggregators enabling vehicle to grid option using distributed algorithm

Electric vehicle (EV) aggregators play the role of intermediate entities between electric vehicles and the grid who can work independently or as the grid operator's agents within the electric network. For all the benefits they can provide through collaboration with other entities like the grid operator and distributed generator (DG), the uncoordinated action of EV aggregators can bring about undesired consequences such as high losses, voltage deviation, peak demand escalation and hence a huge inefficiency of the network operation. In this paper, a unique decentralized approach for the coordination of independent privately-owned EV aggregators and distributed generators is proposed. In this framework, the objective functions representing the goals of the aggregators and generators seek higher profits or lower costs. The objectives also take account of the distribution system operator (DSO) requirements encompassing technical indices such as losses and bus voltages. Several EV-related uncertainties including the plug-in/ plug-out times, the initial state of charge as well as the variations in the number of aggregators and distributed generators, techno-economical parameters of the grid such as the price and load variations and the conflicts of interest among the three parties contribute to the model complexity. For this purpose, a decentralized method based on the Fast Alternating Direction Method of Multipliers (FADMM) is employed to solve the model. The proposed approach is tested on two 33-bus and 69-bus distribution test networks comprised of aggregators and DGs through different scenarios. The results demonstrate that, while retaining the privacy of different agents, the proposed algorithm guarantees the objectives of both aggregators and DGs through higher profits and in the meantime ensures the technical fulfillment for the grid operator. Finally, the effect of varying parameters on the personal and mutual objectives of the mentioned players is evaluated.

Disaster preparedness: An interprofessional student incident command system simulation

Disasters are complex and require teams to navigate uncertain situations, communicate effectively across teams, craft unified messages, and address inputs from multiple stakeholders in real time. It is vital that students learn to work in interprofessional teams while they are in school so that when they become practicing professionals they have the teamwork and communication skills necessary to collaborate during a disaster. The aim of this interprofessional tabletop simulation was to engage first semester graduate health administration students and upper level undergraduate public health students in an incident command center response to an F5 tornado. Best practices in simulation design, including creating mutual objectives, promoting a safe learning environment, and utilizing a structured debriefing, were used to craft the incident command simulation. Evaluation of the simulation was conducted through direct observation, as well as surveys assessing knowledge gain and satisfaction. Evaluation of quantitative data indicated knowledge gain in incident command system sections and roles, job action sheets to perform necessary actions, and coordination of services to respond to a disaster. Students also reported high levels of satisfaction with the experience and improvements in teamwork and communication skills. Qualitative themes described characteristics of interprofessional education (role clarity, communication, collaboration, teamwork) and simulation (value of practice, complexity of response). Applying evidence-based practices to the design and implementation of this interprofessional simulation helped ensure students gained knowledge with incident command system structure and function and improved their ability to communicate.