Stochastic Optimization Theory Research Articles

Modelling the developmental path in the acquisition of /l/-final irregular plurals by Portuguese children

ABSTRACT Previous empirical research has shown that Portuguese children aged 4;0 to 6;0 are sensitive to the quality of stem-final vowels when acquiring the irregular plural forms of /l/-final words (acquisition order: plurals of /al, ɛl, ɔl, ul/ > plurals of /il/). This study presents a formal account of this emergent pattern. We first construct an initial-state grammar that arguably instantiates Portuguese children’s grammatical knowledge at the onset of morpho-phonological acquisition. We then simulate morpho-phonological learning using two constraint-based models (Stochastic Optimality Theory and Noisy Harmonic Grammar) and their associated Gradual Learning Algorithm. The results of our learning simulations corroborate the experimental data, showing that the plural form of /il/-final words takes longer to master than that of other /l/-final words. In addition to replicating the empirical results, the learning simulation reveals two important implications. First, the raw frequency of individual forms cannot account for the attested patterns, suggesting that the input frequency is mediated by some principles of grammatical learning. Second, Harmonic Grammar outperforms Optimality Theory in simulating real acquisition data. Through the additive constraint interaction, Harmonic-Grammar learning is more restrictive, avoiding the generation of unobserved data in the course of morpho-phonological acquisition.

Feature-driven new sound category formation: computational implementation with the L2LP model and beyond

One of the primary questions of second language (L2) acquisition research is how a new sound category is formed to allow for an L2 contrast that does not exist in the learner's first language (L1). Most models rely crucially on perceived (dis)similarities between L1 and L2 sounds, but a precise definition of what constitutes “similarity” has long proven elusive. The current study proposes that perceived cross-linguistic similarities are based on feature-level representations, not segmental categories. We investigate how L1 Japanese listeners learn to establish a new category for L2 American English /æ/ through a perception experiment and computational, phonological modeling. Our experimental results reveal that intermediate-level Japanese learners of English perceive /æ/ as an unusually fronted deviant of Japanese /a/. We implemented two versions of the Second Language Linguistic Perception (L2LP) model with Stochastic Optimality Theory—one mapping acoustic cues to segmental categories and another to features—and compared their simulated learning results to the experimental results. The segmental model was theoretically inadequate as it was unable explain how L1 Japanese listeners notice the deviance of /æ/ from /a/ in the first place, and was also practically implausible because the predicted overall perception patterns were too native English-like compared to real learners' perception. The featural model, however, showed that the deviance of /æ/ could be perceived due to an ill-formed combination of height and backness features, namely */low, front/. The featural model, therefore, reflected the experimental results more closely, where a new category was formed for /æ/ but not for other L2 vowels /ɛ/, /ʌ/, and /ɑ/, which although acoustically deviate from L1 /e/, /a/, and /o/, are nonetheless featurally well-formed in L1 Japanese, namely /mid, front/, /low, central/, and /mid, back/. The benefits of a feature-based approach for L2LP and other L2 models, as well as future directions for extending the approach, are discussed.

Nominal plurals in Sign Language of the Netherlands: Accounting for allomorphy and variation

In both signed and spoken languages, reduplication is a common process in the formation of morphologically complex structures, expressing, e.g., plurality and certain aspectual meanings. A framework in which spoken language reduplication has been formalized frequently is Optimality Theory (OT). While an important attribute of OT-constraints is their universality, to date, the question to what extent such constraints are modality-independent, and thus work for sign language reduplication as well, remains largely unanswered. In the present study, we offer the first OT-formalization of plural reduplication in Sign Language of the Netherlands (NGT). The NGT-data reveal that this language features different plural allomorphs, the choice of which depends on phonological properties of the base noun. However, we also identify variation, e.g., all noun types allow for zero marking.In our formalization, we aim to introduce constraints that are maximally modality-independent, using constraint types that have previously been proposed for spoken language reduplication. Our formalization is the first to take into account base-reduplicant faithfulness for a sign language, and also the first to account for variation in sign language data by employing stochastic OT, whereby some noise is added to the ranking value of each constraint at evaluation time. Evaluating the modality-(in)dependence of our proposed account suggests that the types of constraints we employ as well as the evaluation in the spirit of stochastic OT are not specific to a modality, while the featural implementation is inevitably modality-dependent.

Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply

Mobile edge computing (MEC), as a new distributed computing model, satisfies the low energy consumption and low latency requirements of computation-intensive services. The task offloading of MEC has become an important research hotspot, as it solves the problems of insufficient computing capability and battery capacity of Internet of things (IoT) devices. This study investigates task offloading scheduling in a dynamic MEC system. By integrating energy harvesting technology into IoT devices, we propose a hybrid energy supply model. We jointly optimize local computing, offloading duration, and edge computing decisions to minimize system cost. On the basis of stochastic optimization theory, we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called DTOME. DTOME can make task offloading decisions by weighing system cost and queue stability. We quote dynamic programming theory to obtain the optimal task offloading strategy. Simulation results verify the effectiveness of DTOME, and show that DTOME entails lower system cost than two baseline task offloading strategies.

Micro-Grid Day-Ahead Stochastic Optimal Dispatch Considering Multiple Demand Response and Electric Vehicles

Multiple demand responses and electric vehicles are considered, and a micro-grid day-ahead dispatch optimization model with photovoltaic is constructed based on stochastic optimization theory. Firstly, an interruptible load model based on incentive-based demand response is introduced, and a demand response mechanism for air conditioning load is constructed to implement an optimal energy consumption curve control strategy for air conditioning units. Secondly, considering the travel demand and charging/discharging rules of electric vehicles, the electric vehicle optimization model is built. Further, a stochastic optimization model of micro-grid with demand response and electric vehicles is developed because of the uncertainty of photovoltaic power output. Finally, the simulation example verifies the effectiveness of the proposed model. The simulation results show that the proposed model can effectively tackle the uncertainty of photovoltaic, as well as reduce the operating cost of micro-grid. Therefore, the effective interaction between users and electric vehicles can be realized.

Blockchain Systems, Technologies, and Applications: A Methodology Perspective

In the past decade, blockchain has shown a promising vision to build trust without any powerful third party in a secure, decentralized and scalable manner. However, due to the wide application and future development from cryptocurrency to the Internet of Things, blockchain is an extremely complex system enabling integration with mathematics, computer science, communication and network engineering, etc. By revealing the intrinsic relationship between blockchain and communication, networking and computing from a methodological perspective, it provided a view to the challenge that engineers, experts and researchers hardly fully understand the blockchain process in a systematic view from top to bottom. In this article we first introduce how blockchain works, the research activities and challenges, and illustrate the roadmap involving the classic methodologies with typical blockchain use cases and topics. Second, in blockchain systems, how to adopt stochastic process, game theory, optimization theory, and machine learning to study the blockchain running processes and design the blockchain protocols/algorithms are discussed in details. Moreover, the advantages and limitations using these methods are also summarized as the guide of future work to be further considered. Finally, some remaining problems from technical, commercial and political views are discussed as the open issues. The main findings of this article will provide a survey from a methodological perspective to study theoretical model for blockchain fundamentals understanding, design network service for blockchain-based mechanisms and algorithms, as well as apply blockchain for the Internet of Things, etc.

Tune-text accommodation in Optimality Theory: an account of Southern Valencian Catalan yes-no questions

Abstract This paper aims at contributing to ascertain the principles of intonational grammar that lie behind the realization of nuclear contours and at presenting them in terms of Optimality Theory constraints. In order to do so, we analyse the prosody of the nuclear configuration of Southern Valencian Catalan yes-no questions, with special emphasis on situations where text-tune accommodation phenomena take place. The empirical data, which are analysed according to the principles of the autosegmental-metrical model, show a complex interplay of different phenomena at the text-tune interface, like vowel lengthening, tonal spreading, tonal retraction and intonation-driven schwa epenthesis. We argue that the variation detected in the data can be accounted for by the interaction of nine constraints (i.e., Max-IO(µp), Dep-IO(µs), Anchor(T%,Rt,IP,Rt), Anchor(L*,Rt,ˈσ,Rt), *Anchor(T,C), *Anchor(T,-voice), Share(T*,NC), Dep-IO(Associate), Max-IO(Associate)), whose ranking is established by means of a Stochastic Optimality Theory analysis.

Joint Antenna and Device Scheduling in Full-Duplex MIMO Wireless-Powered Communication Networks

In this article, we study a joint antenna and Internet of Things device (ID) scheduling problem in the full-duplex (FD) multiple-input–multiple-output (MIMO) wireless-powered communication network (WPCN) over time-varying fading channels. We first formulate an optimization problem to maximize the average sum rate of IDs while satisfying their minimum average data rate requirements by jointly scheduling ID selection for uplink data transmission, antenna switching, and beamforming. To deal with the problem, we propose a scheduling algorithm based on Lagrangian duality and the stochastic optimization theory. The proposed scheduling algorithm necessitates solving per-time-slot problems, each of which aims at maximizing the weighted sum of the selected ID’s uplink data rate and the nonselected IDs’ harvested power from the downlink by jointly optimizing ID selection, antenna switching between uplink and downlink, and beamforming at that time slot. To solve the per-time-slot problem, we develop a joint ID selection, antenna switching, and beamforming (Joint-IAB) algorithm based on the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Through simulation, we demonstrate that our scheduling algorithm with the proposed Joint-IAB algorithm provides better performance than the other scheduling algorithms while well satisfying the given minimum average data rate requirements of IDs.

Novel Analysis of Fuzzy Fractional Emden-Fowler Equations within New Iterative Transform Method

The analytical behavior of fractional differential equations is often puzzling and difficult to predict under uncertainty. It is crucial to develop a robust, extensive, and extremely successful theory to address these problems. An application of fuzzy fractional differential equations can be found in applied mathematics and engineering. Using the iterative transform technique, the study determines the analytic solution of fractional fuzzy Emden-Fowler equation in the sense of the Caputo operator, which is applied to evaluate the physical model range in several scientific and engineering disciplines. The derived solutions to the fractional fuzzy Emden-Fowler equations are more generic and applicable to a broader range of problems. Through the translation of fractional fuzzy differential equations into equivalent crisp systems of fractional differential equations, we obtain a parametric description of the solutions. The graphical and numerical representation demonstrates the symmetry among the upper and lower fuzzy solution representations in their simplest form, which may aid in the comprehension of artificial intelligence, control system models, computer science, image processing, quantum optics, medical science, physics, measure theory, stochastic optimization theory, biology, mathematical finance, and other domains, as well as nonfinancial evaluation.

Low-Complexity Dynamic Resource Scheduling for Downlink MC-NOMA Over Fading Channels

Author(s): Kim, Do-Yup; Jafarkhani, Hamid; Lee, Jang-Won | Abstract: In this paper, we investigate dynamic resource scheduling (i.e., joint user, subchannel, and power scheduling) for downlink multi-channel non-orthogonal multiple access (MC-NOMA) systems over time-varying fading channels. Specifically, we address the weighted average sum rate maximization problem with quality-of-service (QoS) constraints. In particular, to facilitate fast resource scheduling, we focus on developing a very low-complexity algorithm. To this end, by leveraging Lagrangian duality and the stochastic optimization theory, we first develop an opportunistic MC-NOMA scheduling algorithm whereby the original problem is decomposed into a series of subproblems, one for each time slot. Accordingly, resource scheduling works in an online manner by solving one subproblem per time slot, making it more applicable to practical systems. Then, we further develop a heuristic joint subchannel assignment and power allocation (Joint-SAPA) algorithm with very low computational complexity, called Joint-SAPA-LCC, that solves each subproblem. Finally, through simulation, we show that our Joint-SAPA-LCC algorithm provides good performance comparable to the existing Joint-SAPA algorithms despite requiring much lower computational complexity. We also demonstrate that our opportunistic MC-NOMA scheduling algorithm in which the Joint-SAPA-LCC algorithm is embedded works well while satisfying given QoS requirements.

A model of mood as integrated advantage.

Mood is an integrative and diffuse affective state that is thought to exert a pervasive effect on cognition and behavior. At the same time, mood itself is thought to fluctuate slowly as a product of feedback from interactions with the environment. Here we present a new computational theory of the valence of mood-the Integrated Advantage model-that seeks to account for this bidirectional interaction. Adopting theoretical formalisms from reinforcement learning, we propose to conceptualize the valence of mood as a leaky integral of an agent's appraisals of the Advantage of its actions. This model generalizes and extends previous models of mood wherein affective valence was conceptualized as a moving average of reward prediction errors. We give a full theoretical derivation of the Integrated Advantage model and provide a functional explanation of how an integrated-Advantage variable could be deployed adaptively by a biological agent to accelerate learning in complex and/or stochastic environments. Specifically, drawing on stochastic optimization theory, we propose that an agent can utilize our hypothesized form of mood to approximate a momentum-based update to its behavioral policy, thereby facilitating rapid learning of optimal actions. We then show how this model of mood provides a principled and parsimonious explanation for a number of contextual effects on mood from the affective science literature, including expectation- and surprise-related effects, counterfactual effects from information about foregone alternatives, action-typicality effects, and action/inaction asymmetry. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

OPTIMAL INVESTMENT POLICY AND CAPITAL MANAGEMENT IN A FINANCIAL INSTITUTION

This research work considered an asset optimization problem where we examine how a financial institution can optimally allocate its total wealth among three assets namely; treasury, security and loan in stochastic interest rate setting and also determined how a financial institution can manage its capital. The optimal investment policy was derived through the application of stochastic optimization theory for the case of constant relative risk aversion (CRRA) utility function. Also, the Stochastic Differential Equation (SDE) for the capital adequacy ratio under Basel Accord, the SDE for the Total Risk – Weighted Assets (TRWA), the SDEs for the capital required to maintain the capital adequacy ratio under Basel II and Central Bank of Nigeria (CBN) standards were derived and solved numerically to study the capital management problem of the financial institution. Numerical examples using published data obtained from Central Bank of Nigeria (CBN) statistical bulletin and Nigeria Stock Exchange were presented to illustrate the dynamics of the optimal investment policy and how a financial institution can manage its capital. From the results, the optimal investment strategy can be achieved by shifting the financial institution investment away from the risky assets (security and loan) towards the riskless asset (treasury). It was also observed that if a financial institution observes the Basel II standard or Nigeria CBN standard of capital requirement, the financial institution would be considered to be strongly capitalized and guarantees the ability to absorb unexpected losses.

Research on dynamic target tracking algorithm in mobile sensor networks

With the increasing complexity of networked cooperative detection environment, new targets appear and leave in the detection area, and the mobility of targets is increasing. The emergence of sensors makes the sensors in the sensor network have certain motion characteristics. These uncertainties increase the complexity of the problem. At the same time, the dynamic selection of sensor resources needs to consider the needs of different priority tasks, platform survival risk, sensor cost and other factors. In view of these problems to be solved, based on the qualitative synthesis theory of predictive control and stochastic dynamic programming optimization theory, the dynamic sensor selection decision theory and method based on multi-step forward decision are explored. This paper studies the dynamic sensor strategy based on multi-step forward decision scheduling, and carries out the research on the optimization decomposition method of sensor selection under different structural characteristics, the dynamic optimization method of sensor selection under complex and uncertain environment, and the multi-objective optimization method of dynamic sensor selection under heterogeneous targets, so as to explore a new way of dynamic sensor selection algorithm design and performance analysis under networked collaborative tracking A new way. The research of dynamic target tracking algorithm in mobile sensor networks has important theoretical and engineering significance to improve the resource efficient utilization and the overall target tracking performance of sensor networks.

Intelligent task offloading and collaborative computation over D2D communication

In this paper, the problem of computation offloading in the edge server is studied in a mobile edge computation (MEC)-enabled cell networks that consists of a base station (BS) integrating edge servers, several terminal devices and collaborators. In the considered networks, we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation offloading. First, a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high power. Second, based on the Lyapunov stochastic optimization theory, the system stability problem is transformed into a queue stability issue, and the optimal computation offloading is obtained by solving these three sub-problems: task allocation control, task execution control and queue update, respectively. Moreover, rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency, energy consumption and time delay compared to the mobility-aware and migration-enabled approach, Full BS and Full local.

Computation offloading to edge cloud and dynamically resource-sharing collaborators in Internet of Things

With the diversity of the communication technology and the heterogeneity of the computation resources at network edge, both the edge cloud and peer devices (collaborators) can be scavenged to provide computation resources for the resource-limited Internet-of-Things (IoT) devices. In this paper, a novel cooperative computing paradigm is proposed, in which the computation resources of IoT device, opportunistically idle collaborators and dedicated edge cloud are fully exploited. Computation/offloading assistance is provided by collaborators at idle/busy states, respectively. Considering the channel randomness and opportunistic computation resource share of collaborators, we study the stochastic offloading control for an IoT device, regarding how much computation load is processed locally, offloaded to the edge cloud and a collaborator. The problem is formulated into a finite horizon Markov decision problem with the objective of minimizing the expected total energy consumption of the IoT device and the collaborator, subject to satisfying the hard computation deadline constraint. Optimal offloading policy is derived based on the stochastic optimization theory, which demonstrates that the energy consumption can be reduced by a proportional factor through the cooperative computing. More energy saving is achieved with better wireless channel condition or higher computation energy efficiency of collaborators. Simulation results validate the optimality of the proposed policy and the efficiency of the cooperative computing between end devices and edge cloud, compared to several other offloading schemes.

A wug-shaped curve in sound symbolism: the case of Japanese Pokémon names

An experiment showed that Japanese speakers’ judgement of Pokémons’ evolution status on the basis of nonce names is affected both by mora count and by the presence of a voiced obstruent. The effects of mora count are a case of counting cumulativity, and the interaction between the two factors a case of ganging-up cumulativity. Together, the patterns result in what Hayes (2020) calls ‘wug-shaped curves’, a quantitative signature predicted by MaxEnt. I show in this paper that the experimental results can indeed be successfully modelled with MaxEnt, and also that Stochastic Optimality Theory faces an interesting set of challenges. The study was inspired by a proposal made within formal phonology, and reveals important previously understudied aspects of sound symbolism. In addition, it demonstrates how cumulativity is manifested in linguistic patterns. The work here shows that formal phonology and research on sound symbolism can be mutually beneficial.

Calibration of Constraint Promotion Does Not Help with Learning Variation in Stochastic Optimality Theory

The Calibrated Error-Driven Ranking Algorithm (CEDRA; Magri 2012 ) is shown to fail on two test cases of phonologically conditioned variation from Boersma and Hayes 2001 . The failure of the CEDRA raises a serious unsolved challenge for learnability research in stochastic Optimality Theory, because the CEDRA itself was proposed to repair a learnability problem ( Pater 2008 ) encountered by the original Gradual Learning Algorithm. This result is supported by both simulation results and a detailed analysis whereby a few constraints and a few candidates at a time are recursively “peeled off” until we are left with a “core” small enough that the behavior of the learner is easy to interpret.

Dispatching optimization model of gas-electricity virtual power plant considering uncertainty based on robust stochastic optimization theory

With the rapid development of renewable energy, virtual power plant technology has gradually become a key technology to solve the large-scale development of renewable energy. This paper focuses on the stochastic dispatching optimization of gas-electric virtual power plant (GVPP). Based on this, wind power plant, photovoltaic power generation and convention gas turbines are used as the power generation side of GVPP. Power-to-gas (P2G) equipment and gas storage tank can realize the conversion and storage of electricity-gas energy. Price based demand response and incentive based demand response are introduced into the terminal load side to regulate the user’s electricity consumption behavior. GVPP bilaterally connects power network and natural gas network, which realizes the bidirectional flow of electricity-gas energy. Firstly, taking the maximization of economic benefits as the objective function, combined with the constraints of power balance, system reserve and so on, a dispatching optimization model of GVPP participating in multi-energy markets is constructed to determine the operation strategy. Secondly, wind, solar and other clean energy have the characteristics of random and fluctuation, which threaten the stable operation of the system. Therefore, a stochastic dispatching optimization model of GVPP considering wind and solar uncertainty is established based on robust stochastic optimization theory. Thirdly, the evaluation indicators of GVPP operation is determined, which can comprehensively evaluate the economic benefits, environmental benefits and system operation of virtual power plant. Finally, in order to verify the validity and feasibility of the model, a virtual power plant is selected for example analysis. The results show that: (1) After the implementation of price based demand response and incentive based demand response, the system load variance changes from 0.03 to 0.013. Through the comparison of load curves, it is found that demand response can play a role of peak-shaving and valley-filling and smooth the power load curve; (2) Stochastic optimization theory can overcome the uncertainty of wind and solar by setting different robust coefficients Γ which reflects the ability of the system to withstand risks; (3) The optimization effect after introducing the P2G subsystem makes the amount of abandoned clean energy close to zero. The operation risk of system is reduced, and the carbon emissions are reduced by 370 m3 too. The market space is expanded from electricity market mainly to natural gas market and carbon trading market.

Cost-Efficient Request Scheduling and Resource Provisioning in Multiclouds for Internet of Things

To satisfy the increasingly complex demands of the Internet of Things (IoT) applications, multiclouds are a promising solution that can provide scalable, various, and abundant resources. However, in multiclouds, each cloud has its specific virtual machine (VM) type and pricing scheme. In addition, the request arrival, network bandwidth, and VM’s price all vary with time and are hardly predicted. In such cases, the request scheduling and resource provisioning (RSRP) for cost efficiency becomes a highly challenging work. In this article, to capture the dynamics in the multiclouds environment, we formulate a stochastic optimization problem where the aim is to minimize the system cost and guarantee the IoT applications’ queueing delay. By applying stochastic optimization theory, the original problem is transformed into a deterministic optimization problem in each slot, and then the deterministic problem is further decomposed into three independent subproblems. An online RSRP algorithm is devised to obtain these subproblems’ optimal solutions. Mathematical analysis shows that RSRP can approach the optimal system cost while bounding the queueing delay, and make an arbitrary tradeoff between system cost and queueing delay as well. Moreover, trace-driven simulation results show the effectiveness of RRSP.

Stochastic Profit Maximization of Service Provider in Millimeter-Wave High-Speed Railway Networks

We study the problem of profit maximization for service providers using a newly emerged business model in millimeter-wave (mmWave) high-speed railway (HSR) networks. In such business model, a service provider purchases data transmission resource from a network operator with a low price. Then, mobile users only need to pay for requested services provided by the service provider. To optimize the profit of service provider, the profit analysis and optimization framework tailored toward the new business model and mmWave HSR networks is developed. This framework consists of three main components including admission control, transmission control, and resource purchasing control. To obtain optimal policies for these components, a stochastic profit optimization problem is formulated while taking into account differentiated service quality requirements, wireless transmission capacity, and network system stability. Then, the formulated problem is solved by using stochastic network optimization theory and stochastic profit maximization mechanism (SPMM) is established. An extensive performance evaluation of SPMM is conducted in order to demonstrate its superiority.