Assignment Mechanism Research Articles

Causal inference for time-to-event data with a cured subpopulation.

When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.

Challenges and Adjustments in a Multisite School-Based Randomized Field Trial

Randomized experiments are a strong design for establishing impact evidence because the random assignment mechanism theoretically allows confidence in attributing group differences to the intervention. Growth of randomized experiments within educational studies has been widely documented. However, randomized experiments within education have received criticism for implementation challenges and for ignoring context. Additionally, limited guidance exists for programs that are tasked with both implementation and evaluation within the same funding period. This study draws on a research team's experiences and examines opportunities and challenges in conducting a multisite randomized evaluation of an internship program for teacher candidates. We discuss how problems were collaboratively addressed and adjusted to align with local realities and demonstrate how the research team, in consultation with local stakeholders, addressed methodological and program implementation problems in the field. Recommendations for future research are provided.

Dynamic scheduling of decentralized high-end equipment R&D projects via deep reinforcement learning

Distributed research and development (R&D) plays a pivotal role in high-end equipment manufacturing enterprises, significantly impacting interdisciplinary innovation and expediting development cycles. In the uncertain environment characterized by random arrivals of new projects, achieving dynamic scheduling and real-time coordination for decentralized R&D projects is a challenging problem. This paper studies a dynamic decentralized resource-constrained multi-project scheduling problem with product transfers (DDRCMPSP-PT). The objective is to minimize the development cycle after initial local schedules are generated to minimize the individual project makespan. To tackle the problem, we develop a decentralized multi-agent system using the dynamic coordination mechanism for resource assignment (DMAS/DCMRA). An up-to-date deep reinforcement learning (DRL) algorithm, dueling double deep Q-learning (D3QN) with prioritized replay, is employed to select the optimal strategy to resolve the global resource conflicts adaptively. Two priority rules are presented based on the structural properties and make up the action space of the DRL agent together with ten high-quality priority rules. Extensive simulation experiment results based on real enterprise cases reveal that the DMAS/DCMRA outperforms all traditional priority rule-based methods. The proposed priority rules also show competitive performance compared to other priority rules, which validates the applicability and superiority of the proposed method. This research offers a new agile project management technique for organizations running multiple R&D projects and managing crucial bottleneck resources.

Binary endogenous treatment in stochastic frontier models with an application to soil conservation in El Salvador

SummaryNumerous programs exist to promote productivity, alleviate poverty, and enhance food security in developing countries. Stochastic frontier analysis can be helpful to assess their effectiveness. However, challenges can arise when accounting for treatment endogeneity, often intrinsic to these interventions. We study maximum likelihood estimation of stochastic frontier models when both the frontier and inefficiency depend on a potentially endogenous binary treatment. We use instrumental variables to define an assignment mechanism and explicitly model the density of the first and second‐stage error terms. We provide empirical evidence using data from a soil conservation program in El Salvador.

Wanet: weight and attention network for video summarization

In this paper, we propose a deep learning-based model, called Weight and Attention Network (WANet), for video summarization. The network comprises a simple multi-head attention mechanism, followed by a feed-forward network to obtain the frame importance scores. Summary keyshots are obtained from the scores using a combination of kernel temporal segmentation and the knapsack algorithm. Contrary to past methods, we first enrich the input frames with similar information as opposed to letting the model learn all the features by itself. A novel weight assignment mechanism is introduced to assign weights to the input frames based on their similarity before passing the same to the model. Experimental results on the SumMe and TVSum datasets indicate the effectiveness of the present method when compared to state-of-the-art methods applied to the same datasets.

Credible School Value-Added with Undersubscribed School Lotteries

Abstract We introduce two empirical strategies harnessing the randomness in school assignment mechanisms to measure school value-added. The first estimator controls for the probability of school assignment, treating take-up as ignorable. We test this assumption using randomness in assignments. The second approach uses assignments as instrumental variables (IVs) for low-dimensional models of value-added and forms empirical Bayes posteriors from these IV estimates. Both strategies solve the underidentification challenge arising from school undersubscription. Models controlling for assignment risk and lagged achievement in Denver and New York City yield reliable value-added estimates. Estimates from models with lower-quality achievement controls are improved by IV.

Regret‐free truth‐telling in school choice with consent

Efficiency Adjusted Deferred Acceptance Rule (EDA) is a promising candidate mechanism for a public school assignment. A potential drawback of EDA is that it could encourage students to game the system since it is not strategy‐proof. However, to successfully strategize, students typically need information that is unlikely to be available to them in practice. We model school choice under incomplete information and show that EDA is regret‐free truth‐telling, which is a weaker incentive property than strategy‐proofness and was introduced by Fernandez (2020). We also show that there is no efficient matching rule that weakly Pareto dominates a stable matching rule and is regret‐free truth‐telling. Note that the original version of EDA by Kesten (2010) weakly Pareto dominates a stable matching rule, but it is not efficient.

The Provision of Information and Incentives in School Assignment Mechanisms

The Provision of Information and Incentives in School Assignment Mechanisms

The Provision of Information and Incentives in School Assignment Mechanisms

The Provision of Information and Incentives in School Assignment Mechanisms

Integrated trajectory planning into automatic berthing control of underactuated ship based on fuzzy-backstepping method

The trajectory planning and automatic berthing control are traditionally studied separately; however, the planned trajectory is indeed required to be the reference trajectory for the automatic berthing control system. In this paper, a novel trajectory planning strategy is presented and integrated into the automatic berthing control of underactuated ship. First, a novel trajectory planning strategy, including the Bezier curve planning, the least square fitting, and the speed assignment mechanism, is presented for the first time herein to provide the differentiable reference trajectory for the automatic berthing control system based on backstepping method. Second, a novel coordinate transformation between the Earth-fixed frame and the berth-fixed frame is established for the first time herein for the arbitrary berth position. Third, the fuzzy logic system is incorporated into backstepping controller to alleviate the influence of the model uncertainty, which is probably introduced by the shallow water and bank effects. The closed-loop signals are proved to be stable based on Lyapunov theorem. Two simulations are conducted to illustrate the effectiveness and advantages of the presented fuzzy-backstepping-based automatic berthing control methodology.

Polarized Communities Search via Co-guided Random Walk in Attributed Signed Networks

Polarized communities search aims at locating query-dependent communities, in which mostly nodes within each community form intensive positive connections, while mostly nodes across two communities are connected by negative links. Current approaches towards polarized communities search typically model the network topology, while the key factor of node, i.e., the attributes, are largely ignored. Existing studies have shown that community formation is strongly influenced by node attributes and the formation of communities are determined by both network topology and node attributes simultaneously. However, it is nontrivial to incorporate node attributes for polarized communities search. Firstly, it is hard to handle the heterogeneous information from node attributes. Secondly, it is difficult to model the complex relations between network topology and node attributes in identifying polarized communities. To address the above challenges, we propose a novel method Co-guided Random Walk in Attributed signed networks (CoRWA) for polarized communities search by equipping with reasonable attribute setting. For the first challenge, we devise an attribute-based signed network to model the auxiliary relation between nodes and a weight assignment mechanism is designed to measure the reliability of the edges in the signed network. As to the second challenge, a co-guided random walk scheme in two signed networks is designed to explicitly model the relations between topology-based signed network and attribute-based signed network so as to enhance the search result of each other. Finally, we can identify polarized communities by a well-designed Rayleigh quotient in the signed network. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed CoRWA. Further analysis reveals the significance of node attributes for polarized communities search.

Experimental evaluation of a latency-aware routing and spectrum assignment mechanism based on deep reinforcement learning

The introduction of futuristic and challenging use cases of 5G and 6G communications will demand strict requirements in terms of high bandwidth and low latency. Optical backbone networks need to tackle these new network scenarios by offering highly efficient, flexible, and scalable technologies and solutions. In this context, elastic optical networks (EONs) have been recognized as a promising technological transport infrastructure for the future Internet since they can manage the optical spectrum with enhanced flexibility and efficiency. The service provisioning in EONs is a challenging issue to tackle since the routing and spectrum assignment (RSA) is characterized by a high degree of complexity. This work presents an approach for RSA in EONs leveraging the advantages of deep reinforcement learning (DRL) solutions. The devised approach jointly considers the constraints imposed by the optical technologies and the demanded connectivity service requirements (i.e., guaranteed bandwidth and maximum end-to-end latency) when computing and selecting the optical path and spectral resources. We first evaluate our approach through simulation experiments considering two reference network topologies, demonstrating its effectiveness in reducing the bandwidth blocking ratio, the path computation time, and the number of rejected connectivity services requiring lower latencies when compared to a baseline k-shortest path routing and first-fit spectrum allocation algorithm. Then, the trained DRL agent is integrated within a real proof of concept to attain an ML-assisted SDN control plane in the CTTC ADRENALINE testbed. The attained performance improvements highlight the potential benefits brought by using DRL mechanisms and its feasible integration within production EON transport infrastructures.

The Genetic Code Assembles via Division and Fusion, Basic Cellular Events.

Standard Genetic Code (SGC) evolution is quantitatively modeled in up to 2000 independent coding 'environments'. Environments host multiple codes that may fuse or divide, with division yielding identical descendants. Code division may be selected-sophisticated gene products could be required for an orderly separation that preserves the coding. Several unforeseen results emerge: more rapid evolution requires unselective code division rather than its selective form. Combining selective and unselective code division, with/without code fusion, with/without independent environmental coding tables, and with/without wobble defines 25 = 32 possible pathways for SGC evolution. These 32 possible histories are compared, specifically, for evolutionary speed and code accuracy. Pathways differ greatly, for example, by ≈300-fold in time to evolve SGC-like codes. Eight of thirty-two pathways employing code division evolve quickly. Four of these eight that combine fusion and division also unite speed and accuracy. The two most precise, swiftest paths; thus the most likely routes to the SGC are similar, differing only in fusion with independent environmental codes. Code division instead of fusion with unrelated codes implies that exterior codes can be dispensable. Instead, a single ancestral code that divides and fuses can initiate fully encoded peptide biosynthesis. Division and fusion create a 'crescendo of competent coding', facilitating the search for the SGC and also assisting the advent of otherwise uniformly disfavored wobble coding. Code fusion can unite multiple codon assignment mechanisms. However, via code division and fusion, an SGC can emerge from a single primary origin via familiar cellular events.

Relation Between Objective Space Normalization and Weight Vector Scaling in Decomposition-Based Multiobjective Evolutionary Algorithms

Real-world multiobjective optimization problems (MOPs) usually have conflicting and differently-scaled objectives. To deal with such problems, objective space normalization is widely used in multiobjective evolutionary algorithm (MOEA) design, especially, in the design of decomposition-based MOEAs. It has been demonstrated that uniformly-distributed solutions can be obtained for badly-scaled MOPs by decomposition-based MOEAs with objective space normalization. Recently, weight vector scaling has also been used for badly-scaled MOPs. In some studies, it was argued that weight vector scaling and objective space normalization are essentially the same when applied to decomposition-based MOEAs. In this paper, we theoretically and empirically show the relation between objective space normalization and weight vector scaling. Our results demonstrate that similarities and differences between the two methods depend on the choice of a scalarizing function. How the choice between normalization and weight vector scaling affects decomposition-based MOEAs with solution assignment mechanisms is also analyzed.

A Multiplatform-Cooperation-Based Task Assignment Mechanism for Mobile Crowdsensing

Mobile crowdsensing (MCS) has been an effective sensing paradigm by utilizing the smart devices carried by mobile users to complete sensing tasks at different locations. An important problem in MCS is how to achieve effective task assignment in the context of opportunistic sensing, where mobile users are selectively recruited to perform tasks in an opportunistic way. However, most existing work in this aspect suppose there are only one service platform and further the sensing qualities of users are known apriori. In this paper, we study the task assignment when there are multiple service platforms and further the sensing qualities of users are unknown apriori. The design objective is to maximize the overall sensing qualities of finished tasks at all platforms. For this purpose, we build a multi-platform cooperation framework and formulate the task quality maximization problem in this case as a 0-1 integer linear programming (ILP) problem. We propose a Multi-platform Cooperation based Task Assignment mechanism (MCTA). MCTA includes two phases. The first phase establishes stable cooperation relationship among platforms while respecting their respective cooperation willingness, and for this phase, we propose a cross-platform cooperation relationship construction algorithm. The second phase performs effective online task assignment, and for this phase, we propose two online Multi-Armed Bandit (MAB) with sleeping arms based user selection algorithms using local and global learning, respectively, based on whether cross-platform user-sensing-quality learning is allowed. We derive the regrets of the proposed algorithms and prove that MCTA has the properties of cooperation stability and computation efficiency. Extensive simulation results show the high performance of our proposed MCTA mechanism as compared with existing work.

Probabilistic Coverage Constraint Task Assignment with Privacy Protection in Vehicular Crowdsensing.

The increasing popularity of portable smart devices has led to the emergence of vehicular crowdsensing as a novel approach for real-time sensing and environmental data collection, garnering significant attention across various domains. Within vehicular crowdsensing, task assignment stands as a fundamental research challenge. As the number of vehicle users and perceived tasks grows, the design of efficient task assignment schemes becomes crucial. However, existing research solely focuses on task deadlines, neglecting the importance of task duration. Additionally, the majority of privacy protection mechanisms in the current task assignment process emphasize safeguarding user location information but overlook the protection of user-perceived duration. This lack of protection exposes users to potential time-aware inference attacks, enabling attackers to deduce user schedules and device information. To address these issues in opportunistic task assignment for vehicular crowdsensing, this paper presents the minimum number of participants required under the constraint of probability coverage and proposes the User-Based Task Assignment (UBTA) mechanism, which selects the smallest set of participants to minimize the payment cost while measuring the probability of accomplishing perceived tasks by user combinations. To ensure privacy protection during opportunistic task assignment, a privacy protection method based on differential privacy is introduced. This method fuzzifies the sensing duration of vehicle users and calculates the probability of vehicle users completing sensing tasks, thus avoiding the exposure of users' sensitive data while effectively assigning tasks. The efficacy of the proposed algorithm is demonstrated through theoretical analysis and a comprehensive set of simulation experiments.

Strategy-proof and envy-free mechanisms for house allocation

We consider the problem of allocating indivisible objects to agents when agents have strict preferences over objects. There are inherent trade-offs between competing notions of efficiency, fairness and incentives in such assignment mechanisms. It is, therefore, natural to consider mechanisms that satisfy two of these three properties in their strongest notions, while trying to improve on the third dimension. In this paper, we are motivated by the following question: Is there a strategy-proof and envy-free random assignment mechanism more efficient than equal division?Our contributions in this paper are twofold. First, we further explore the incompatibility between efficiency, fairness, and strategy-proofness within random assignment mechanisms. We define a new notion of efficiency that is weaker than ex-post efficiency and prove that any strategy-proof and envy-free mechanism must sacrifice efficiency even in this very weak sense. Next, we introduce a new family of mechanisms called Rank Exchange mechanisms that are strategy-proof and envy-free and stochastically dominate equal division. Further, we show that Rank Exchange mechanisms characterize the set of neutral, strategy-proof, and envy-free mechanisms that also satisfy a natural separability axiom that may be of independent interest.

Federated causal inference in heterogeneous observational data.

We are interested in estimating the effect of a treatment applied to individuals at multiple sites, where data is stored locally for each site. Due to privacy constraints, individual-level data cannot be shared across sites; the sites may also have heterogeneous populations and treatment assignment mechanisms. Motivated by these considerations, we develop federated methods to draw inferences on the average treatment effects of combined data across sites. Our methods first compute summary statistics locally using propensity scores and then aggregate these statistics across sites to obtain point and variance estimators of average treatment effects. We show that these estimators are consistent and asymptotically normal. To achieve these asymptotic properties, we find that the aggregation schemes need to account for the heterogeneity in treatment assignments and in outcomes across sites. We demonstrate the validity of our federated methods through a comparative study of two large medical claims databases.

Simulation-based joint user assignment and edge resource allocation optimization for hybrid tasks in vehicular edge computing

With the swift advancement of 5G communication technology and the widespread prevalence of intelligent connected vehicles, a multitude of applications for the Internet of Vehicles (IoV) have been steadily burgeoning. Nevertheless, owing to vehicular mobility and the variety of tasks generated by IoV applications competing for restricted edge resources, the task of assigning users to edge nodes, in addition to appropriating edge node resources to hybrid tasks within edge environments, persists as a formidable challenge. In addressing this predicament, we establish a model delineating the execution duration of hybrid tasks engendered by IoV applications, and introduce a joint mechanism for user assignment and edge resource allocation. Within this mechanism, we architect an optimal strategy for edge resource allocation, predicated upon given user assignment outcomes. In order to ascertain the user assignment strategy, we employ an enhanced genetic algorithm in scenarios of a large user base, and utilize the branch-and-bound algorithm when dealing with a small user base. Finally, we design and implement an experimental setup for joint user assignment and edge resource allocation. The comprehensive simulation results provide robust verification of the effectiveness of our proposed mechanism.

Edge Trusted Sharing: Task-Driven Decentralized Resources Collaborate in IoT

6G is committed to providing a fully connected world. The deployment and application of 6G technology in the Internet of Things (IoT) can efficiently collaborate IoT resources and realize resource sharing, which mainly encourages IoT development. However, due to the lack of trust between IoT resources, security and privacy become the main challenges. As an emerging technology, blockchain can solve the trust-absence issues and provide more benefits, but the introduction of blockchain also brings problems for IoT resource collaboration and sharing. This article first proposes a blockchain-enabled edge resource sharing (BEERS) architecture by combining blockchain and edge computing technology. Then, based on a typical resource sharing and collaboration scenario, the joint optimization problem of resource scheduling and task assignment (JRSTA) is constructed. We decompose JRSTA into a primal problem and a master problem and design a greedy-based task assignment (GBTA) algorithm to solve the primal problem. Based on the GBTA algorithm, the resource scheduling and task assignment (RSTA) algorithm is developed. Next, we design a layered parallel edge resource scheduling and task assignment (LPRSTA) mechanism to improve practicability. Finally, we analyze the security and the performance of our proposed architecture and algorithms. The results show that the proposed architecture can support the secure collaboration of IoT resources, and the proposed algorithm can achieve effective resource scheduling and task assignment.