Proximity Constraints Research Articles

Demand Response in NOMA-based Mobile Edge Computing: A Two-phase Game-theoretical Approach

Similar to cloud servers, edge servers running 24/7 in a mobile edge computing (MEC) system consume a large amount of energy, and thus require demand response management. Demand response has been widely employed to reduce energy consumption at data centers. However, existing demand response approaches for data centers are rendered obsolete by the new and unique characteristics of MEC systems: 1) proximity constraint - mobile users can be served by neighbor edge servers only; 2) latency constraint - mobile users' workloads should be processed by their neighbor edge servers to ensure low latency; and 3) capacity constraint - edge servers have limited computing and communication resources to serve mobile users. Demand response for MEC is further complicated by the non-orthogonal multiple access (NOMA) scheme - the emerging radio access scheme for 5G. Communication resources like channels and transmit power must be systematically considered with computing resources like CPU, memory and storage to fulfil mobile users' resource demands. This paper makes the first attempt to tackle this Edge Demand Response (EDR) problem. We first formulate this problem and prove its NP-hardness. Then, we propose a two-phase game-theoretical approach (EDRGame) to solve the EDR problem. Its performance is theoretically analyzed and experimentally evaluated against state-of-the-art approaches on a widely-used real-world dataset.

Adaptive Kernel Learning in Heterogeneous Networks

We consider learning in decentralized heterogeneous networks: agents seek to minimize a convex functional that aggregates data across the network, while only having access to their local data streams. We focus on the case where agents seek to estimate a regression function that belongs to a reproducing kernel Hilbert space (RKHS). To incentivize coordination while respecting network heterogeneity, we impose nonlinear proximity constraints. The resulting constrained stochastic optimization problem is solved using the functional variant of stochastic primal-dual (Arrow-Hurwicz) method which yields a decentralized algorithm. In order to avoid the model complexity from growing linearly over time, we project the primal iterates onto subspaces greedily constructed from kernel evaluations of agents' local observations. The resulting scheme, dubbed Heterogeneous Adaptive Learning with Kernels (HALK), allows us, for the first time, to characterize the precise trade-off between the optimality gap, constraint violation, and the model complexity. In particular, the proposed algorithm can be tuned to achieve zero constraint violation, an optimality gap of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1/2</sup> +α) after T iterations, where the number of elements retained in the dictionary is determined by 1/α. Simulations on a correlated spatio-temporal field estimation validate our theoretical results, which are corroborated in practice for networked oceanic sensing buoys estimating temperature and salinity from depth measurements.

Context-dependent trait covariances: how plasticity shapes behavioral syndromes.

The study of behavioral syndromes aims to understand among-individual correlations of behavior, yielding insights into the ecological factors and proximate constraints that shape behavior. In parallel, interest has been growing in behavioral plasticity, with results commonly showing that animals vary in their behavioral response to environmental change. These two phenomena are inextricably linked—behavioral syndromes describe cross-trait or cross-context correlations, while variation in behavioral plasticity describes variation in response to changing context. However, they are often discussed separately, with plasticity analyses typically considering a single trait (univariate) across environments, while behavioral trait correlations are studied as multiple traits (multivariate) under one environmental context. Here, we argue that such separation represents a missed opportunity to integrate these concepts. Through observations of multiple traits while manipulating environmental conditions, we can quantify how the environment shapes behavioral correlations, thus quantifying how phenotypes are differentially constrained or integrated under different environmental conditions. Two analytical options exist which enable us to evaluate the context dependence of behavioral syndromes—multivariate reaction norms and character state models. These models are largely two sides of the same coin, but through careful interpretation we can use either to shift our focus to test how the contextual environment shapes trait covariances.

Economic Viability of Renewable Energy Communities under the Framework of the Renewable Energy Directive Transposed to Austrian Law

This study is concerned with the national transposition of the European Renewable Energy Directive into Austrian law. The objective is to estimate the economic viability for residential customers when participating in a renewable energy community (REC), focused on PV electricity sharing. The developed simulation model considers the omission of certain electricity levies as well as the obligatory proximity constraint being linked to grid levels, thus introducing a stepwise reduction of per-unit grid charges as an incentive to keep the inner-community electricity transfer as local as possible. Results show that cost savings in residential RECs cover a broad range from 9 EUR/yr to 172 EUR/yr. The lowest savings are gained by customers without in-house PV systems, while owners of a private PV system make the most profits due to the possibility of selling as well as buying electricity within the borders of the REC. Generally, cost savings increase when the source is closer to the sink, as well as when more renewable electricity is available for inner-community electricity transfer. The presence of a commercial customer impacts savings for households insignificantly, but increases local self-consumption approximately by 10%. Despite the margin for residential participants to break even being narrow, energy community operators will have to raise a certain participation fee. Such participation fee would need to be as low as 2.5 EUR/month for customers without in-house PV systems in a purely residential REC, while other customers could still achieve a break-even when paying 5 EUR/month to 6.7 EUR/month in addition. Those results should alert policy makers to find additional support mechanisms to enhance customers’ motivations to participate if RECs are meant as a concept that should be adopted on a large scale.

Stability and robustness of edge-agreement-based consensus protocols for undirected proximity graphs

We address the consensus problem, with a guarantee of connectivity maintenance, for multiagent systems of first- and second-order, communicating over a connected, undirected graph with proximity constraints. Our approach relies on a so-called barrier Lyapunov functions, which encode the distance constraints between pairs of agents. Beyond the consensus algorithms, our primary contribution is to provide strict barrier Lyapunov functions, i.e. positive definite and with negative definite derivative in the agreement subspace. Thus, uniform asymptotic stability of the consensus manifold and the maintenance of the connectivity are guaranteed. Furthermore, with the said barrier functions, we demonstrate the robustness of consensus protocols by establishing global input-to-state stability.

Optical flow refinement using iterative propagation under colour, proximity and flow reliability constraints

This study proposes a strategy to refine optical flow based on the estimated reliability maps. These maps are firstly estimated a posteriori after the motion estimation by the well-known Kanade–Lucas–Tomasi (KLT). With two new defined criteria based, respectively, on the optical flow local variance and the temporal evolution of the KLT residuals, a global refinement of the motion map is then carried out through two stages under the control of the reliability measures and the colour local homogeneousness. According to the experiments performed on the Middlebury dataset, the authors' reliability measures prove to be a good indicator for the quality of the estimation. Indeed, the correction process increases the global reliability measures and reduces the global errors in a significant way. The experiments show that the quality is higher than classical estimation methods and ranked at 88/168 on Middlebury website

Social-aware spatial keyword top-k group query

With the increasing popularity of location-based social networking services, information in social networks has become an important basis for analyzing user preferences. However, the existing spatial keyword group query only focuses on the distance constraint between the user groups, and ignores the social relationship between the user and his friends, which may affect the query results. Therefore, in order to meet the diverse query needs of user groups and improve user satisfaction based on information in social networks, this paper proposes a social-aware spatial keyword top-k group query problem. This problem aims to retrieve a set of k groups of POI objects that satisfy the preferences of multiple users, taking into account spatial proximity, social relevance, and keyword constraints. To solve this problem, we first design a rank function to measure the correlation between the query set and the candidate set. Next, in order to improve the query efficiency, we develop a novel hybrid index structure, SAIR-tree, which comprehensively considers the attributes of social, spatial, and textual. Then, we propose an approximate algorithm and an exact algorithm, combining with the pruning strategy, can efficiently search the top-k result set. Finally, experiments on real dataset confirm the efficiency and accuracy of the proposed algorithms.

Depth recovery in time of flight range sensors via compressed sensing algorithm

Stereo vision, structured light, and time of flight (ToF) are different range imaging techniques that acquire a scene and provide different features such as a depth map and an amplitude image. Compared to other imaging techniques, ToF can measure depth with high speed and good precision according to state of the art. However, it faces multipath interference (MPI) problems that give rise to an error in radiance information. Exploiting the sparsity of the received signal, we solved the multipath interference problem with the help of compressed sensing sparse recovery algorithms with some modification such as applying positivity constraint and proximity constraint. The modification in the algorithm has increased its robustness and proved to be successful in detecting the interference up to two paths successfully. We validated the approach by providing experimental results on synthetic data with ground truth that demonstrated its efficiency and accuracy to give MPI free output. Moreover, we applied a modified sparse recovery algorithm to real data and compared the result with the state-of-the-art methods. It shows better performance in separating the direct path from the multipath component with high accuracy.

Industry 4.0, proximity constraints and new challenges for industrial policy

ABSTRACT This paper looks in depth at the territorial problems posed by the incorporation of Information and Communication Technologies (ICTs) into the knowledge bases of product fields in manufacturing industry, resulting in what has become known as Industry 4.0. Using a formal, logical approach we build up a reference taxonomy that identifies various territorial problems based on the cognitive composition of the technology solutions used by manufacturing companies. We assess those problems in terms of their effects in the form of proximity constraints on technological collaboration activities and geographical location for producers. Once the various territorial problems are formally set out, it becomes possible to identify each product field with the problem that corresponds to the cognitive composition of the technical solutions that it uses.

Validation of RTOG 0813 Proximal Bronchial Tree Constraints for Pulmonary Toxicity With Stereotactic Body Radiation Therapy for Central Non-small Cell Lung Cancer

Clinical validation of protocol-specified dosimetric constraints for the proximal bronchial tree (PBT) is limited for central non-small cell lung cancer treated with stereotactic body radiation therapy. We sought to validate Radiation Therapy Oncology Group (RTOG) PBT constraints with a large institutional data set. Lesions ≤2 cm from the PBT treated with definitive stereotactic body radiation therapy from 2009 to 2016 were identified from a prospective registry of 1462 patients. Every PBT dose and volume combination, ranging from 0 cGy to 8000 cGy in increments of 10 cGy and volumes ranging from 0.03 cm3 to 50 cm3 in increments of 0.03 cm3, was analyzed. The sensitivity and specificity of these endpoints for identifying pulmonary toxicity were calculated. Pulmonary toxicity was classified as pneumonitis or nonpneumonitis toxicity (NPT) (fistula, stenosis, necrosis, hemoptysis, clinically significant pleural effusion). The optimal dosimetric predictor was chosen by calculation of F-score (highest sensitivity and specificity). The study included 132 patients, with 26.0-month median follow-up. Eight grade ≥2 NPT (2 grade 5) and 8 grade 2 pneumonitis toxicities were observed. The PBT dosimetric endpoint with the highest F-score for identification of grade 2 to 5 NPT was D0.03cc ≤5000 cGy and that for grade 3 to 5 NPT was D0.33cc ≤4710 cGy, with sensitivity and specificity of 87.5% and 76.6% and 100.0% and 85.7%, respectively. Applying the RTOG 0813 PBT constraints to our data set achieved a sensitivity and specificity of 33.3% and 92.1% for D4cc ≤1800 cGy and 37.5% and 92.7% for D0.03cc ≤5250 cGy for identification of grade 2 to 5 NPT. A PBT dosimetric correlation for pneumonitis toxicity could not be identified. This novel dosimetric analysis validates current RTOG constraints and emphasizes high-dose, small-volume constraints as better predictors for NPT. We demonstrated that a slightly lower maximum point dose PBT constraint may be optimal for identification of NPT. Validation of these findings in a larger cohort of patients with longer follow-up is necessary.

Robust Consensus and Connectivity-maintenance under Edge-agreement-based Protocols for Directed Spanning Tree Graphs

We address the consensus problem with connectivity maintenance for networks of multi-agent systems interconnected over directed spanning tree graphs in the edge-agreement space and, for the first time in the literature, we provide a strict Lyapunov function. The importance of this contribution is that it allows to establish uniform global asymptotic stability of the consensus manifold for a multi-agent system subject to proximity constraints. Moreover, robustness in the sense of input-to-state stability with respect to external disturbances is also established. These properties have not been established before when dealing with state-dependent constraints, even for a class of directed graphs, because most often in the literature only non-uniform convergence to the consensus manifold is established.

Spontaneous activity rates and resting metabolism: Support for the allocation model of energy management at the among‐individual level

AbstractDespite continuing interest in the proximate energetic constraints on individual variation in behavior, there is presently equivocal evidence for correlations between metabolism and behavior at the among‐individual level. Possible reasons for this include imprecise estimates of individual mean behavior and metabolism due to no repeated measures on one or more of the traits, analyses that do not take into account the labile nature of these traits and the uncertainty in individual estimates, and changing environmental conditions not accounted for. In this empirical study, we repeatedly measured activity rates and resting metabolic rates (RMR) of individual male mosquitofish over an extended period, lasting several months under constant laboratory conditions. Repeatability of each trait was significant (RMR: R = .41; activity: R = .72), indicating consistent variation among individuals, making covariance between them possible. Contrary to expectations, bivariate mixed model analysis revealed that more active individuals had lower RMR (r = −.58) after accounting for mass effects and other covariates. This result suggests that high activity rates require individuals to allocate less energy toward maintenance, and thus provides evidence for the “allocation” model of energy management. We suggest that it would be valuable to study whether and how behavior‐RMR correlations change over individual lifetime, a topic that has yet to be addressed.

DSS approach for heterogeneous parallel machines scheduling considering proximate supply chain constraints

This paper describes the basis of a Decision Support System (DSS) designed to schedule fertiliser production orders to be delivered within time windows, in plants made up of multiple heterogeneous parallel processors (production lines), considering that fertiliser production rates and nomenclatures depend on lines, that setup times depend on sequence and lines, and taking into account downtime constraints (preventive maintenance …). A mixed linear programming model is encapsulated in the DSS which considers the schedule’s impacts, immediately upstream and downstream of plants in the supply chain. These side-effects may make the proposed solution unfeasible and the DSS helps redefining the problem to avoid them.

Asynchronous Online Learning in Multi-Agent Systems With Proximity Constraints

We consider the problem of distributed learning from sequential data via online convex optimization. A multi-agent system is considered where each agent has a private objective but is willing to cooperate in order to minimize the network cost, which is the sum of local cost functions. Different from the classical distributed settings, where the agents coordinate through the use of consensus constraints, we allow the neighboring agent actions to be related via a non-linear proximity function. A decentralized saddle point algorithm is proposed that is capable of handling gradient delays arising from computational issues. The proposed online asynchronous algorithm is analyzed under adversarial settings by developing bounds on the regret of O(√T), which measures the cumulative loss incurred by the online algorithm against a clairvoyant, and network discrepancy of O(T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3/4</sup> ), which measures the cumulative constraint violation or agent disagreement. By allowing the agents to utilize stale gradient information, the proposed algorithm embraces the nuances of distributed learning and serves to be the first distributed online algorithm that can handle adversarial delays. A modified saddle point algorithm is also proposed that explicitly forces the agents to agree as per the constraint function resulting in zero network discrepancy while incurring a slightly higher regret. To showcase the efficacy of the proposed asynchronous algorithm, a spatially correlated random field estimation problem is formulated and solved. Additionally, an application of vision-based target localization with moving cameras demonstrates the benefits of this approach in practice.

Proximity-Aware IaaS in an Edge Computing Environment With User Dynamics

Edge computing enables application services to leverage low-latency responses. This paper describes the design of infrastructure-as-a-service for edge computing (EC-IaaS) to realize multiple application services on the same edge servers. In the provision of application services, it is important for application service providers (ASPs) to satisfy proximity constraints regarding the edge servers hosting virtual machines (VMs). State-of-the-art methods to provide VMs satisfying these proximity constraints require ASPs to have a large amount of information from an EC-IaaS provider, which incurs large computational and communication overheads. Otherwise, the edge servers will incur the large power draw. In this paper, we propose a virtual region model. The virtual region model abstracts the details of an edge computing infrastructure. We extend the VM request and placement processes based on the virtual region model, to support dynamics of end-user devices. We confirm that the amount of information, which ASPs receive from an EC-IaaS provider, is the same level of the least amount required by the state-of-the-art methods. Additionally, the power draw of edge servers is the same as the minimum in the state-of-the-art methods, and this level is even sustained when the number of end-user devices changes in a city.

Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems

A good product-to-shelf assignment strategy not only helps customers easily find desired product items but also increases retailer profit. Recent research has attempted to solve product-to-shelf problems using product association analysis, a powerful data mining tool that can detect significant co-purchase rules underlying a large amount of purchase transaction data. While some studies have developed efficient approaches for this task, they largely overlook important factors related to optimizing product-to-shelf assignment, including product characteristics, physical proximity, and category constraints. This paper proposes a three-stage product-to-shelf assignment method to address this shortcoming. The first stage constructs a product relationship network that represents the purchase association among product items. The second stage derives the centrality value of each product item through network analysis. Based on the centrality of each product, an item is classified as an attraction item, an opportunity item, or a trivial item. The third stage considers purchase association, physical relationship, and category constraint when evaluating the location preference of each product. Based on the location preference values, a product assignment algorithm is then developed to optimize locations for opportunity items. A series of analyses and comparisons on the performance of different network types are conducted. It is found that the two network types provide variant managerial meanings for store managers. In addition, the implementation and experimental results show the proposed method is feasible and helpful.

Asynchronous Saddle Point Algorithm for Stochastic Optimization in Heterogeneous Networks

We consider expected risk minimization in multi-agent systems comprised of distinct subsets of agents operating without a common time scale. Each individual in the network is charged with minimizing the global objective function, which is an average of sum of the statistical average loss function of each agent in the network. Since agents are not assumed to observe data from identical distributions, the hypothesis that all agents seek a common action is violated, and thus the hypothesis upon that consensus constraints are formulated is violated. Thus, we consider nonlinear network proximity constraints, which incentivize nearby nodes to make decisions that are close to one another but do not necessarily coincide. Moreover, agents are not assumed to receive their sequentially arriving observations on a common time index, and thus seek to learn in an asynchronous manner. An asynchronous stochastic variant of the Arrow–Hurwicz saddle point method is proposed to solve this problem that operates by alternating primal stochastic descent steps and Lagrange multiplier updates that penalize the discrepancies between agents. This tool leads to an implementation that allows for each agent to operate asynchronously with local information only and message passing with neighbors. Our main result establishes that the proposed method yields convergence in expectation both in terms of the primal sub-optimality and constraint violation to radii of sizes ${\mathcal O}(\sqrt{T})$ and ${\mathcal O}(T^{3/4})$ , respectively. Empirical evaluation on an asynchronously operating wireless network that manages user channel interference through an adaptive communications pricing mechanism demonstrates that our theoretical results translates well to practice.

Coupled Active Shape Models for Automated Segmentation and Landmark Localization in High-Resolution CT of the Foot and Ankle.

We develop an Active Shape Model (ASM) framework for automated bone segmentation and anatomical landmark localization in weight-bearing Cone-Beam CT (CBCT). To achieve a robust shape model fit in narrow joint spaces of the foot (0.5 - 1 mm), a new approach for incorporating proximity constraints in ASM (coupled ASM, cASM) is proposed. In cASM, shape models of multiple adjacent foot bones are jointly fit to the CBCT volume. This coupling enables checking for proximity between the evolving shapes to avoid situations where a conventional single-bone ASM might erroneously fit to articular surfaces of neighbouring bones. We used 21 extremity CBCT scans of the weight-bearing foot to compare segmentation and landmark localization accuracy of ASM and cASM in leave-one-out validation. Each scan was used as a test image once; shape models of calcaneus, talus, navicular, and cuboid were built from manual surface segmentations of the remaining 20 scans. The models were augmented with seven anatomical landmarks used for common measurements of foot alignment. The landmarks were identified in the original CBCT volumes and mapped onto mean bone shape surfaces. ASM and cASM were run for 100 iterations, and the number of principal shape components was increased every 10 iterations. Automated landmark localization was achieved by applying known point correspondences between landmark vertices on the mean shape and vertices of the final active shape segmentation of the test image. Root Mean Squared (RMS) error of bone surface segmentation improved from 3.6 mm with conventional ASM to 2.7 mm with cASM. Furthermore, cASM achieved convergence (no change in RMS error with iteration) after ~40 iterations of shape fitting, compared to ~60 iterations for ASM. Distance error in landmark localization was 25% to 55% lower (depending on the landmark) with cASM than with ASM. The importance of using a coupled model is underscored by the finding that cASM detected and corrected collisions between evolving shapes in 50% to 80% (depending on the bone) of shape model fits. The proposed cASM framework improves accuracy of shape model fits, especially in complexes of tightly interlocking, articulated joints. The approach enables automated anatomical analysis in volumetric imaging of the foot and ankle, where narrow joint spaces challenge conventional shape models.

Control of Magnetic Microrobot Teams for Temporal Micromanipulation Tasks

In this paper, we present a control framework that allows magnetic microrobot teams to accomplish complex micromanipulation tasks captured by global linear temporal logic (LTL) formulas. To address this problem, we propose an optimal control synthesis method that constructs discrete plans for the robots that satisfy both the assigned tasks as well as proximity constraints between the robots due to the physics of the problem. The proposed algorithm relies on an existing optimal control synthesis approach combined with a novel sampling-based technique to reduce the state-space of the product automaton that is associated with the LTL specifications. The synthesized discrete plans are executed by the microrobots independently using local magnetic fields. Simulation studies show that the proposed algorithm can address large-scale planning problems that cannot be solved using existing optimal control synthesis approaches. Moreover, we present experimental results that also illustrate the potential of the method in practice. To the best of our knowledge, this is the first control framework that allows independent control of teams of magnetic microrobots for temporal micromanipulation tasks.

Hybrid Resource Allocation Scheme in Multi-hop Device-to-Device Communication for 5G Networks

The 5G communication paradigm provides architecture of coexistence of device-to-device (D2D) communication with the current cellular communication. Direct D2D communication offloads the major traffic by enabling the localized communication between the users with the advantage of close proximity by reusing cellular resource block. However, direct D2D communication suffers from limited proximity constraint. In order to increase the proximity, direct D2D communication can be extended to multi-hop D2D communication. By sharing the cellular resource with multi-hop D2D pairs, a significant interference may occur that further reduces the system throughput. In order to reduce the interference and to increase the throughput of the network, a hybrid resource allocation scheme for the multi-hop D2D communication is proposed in this work. This scheme is divided into two parts. In first part, an interference matrix is constructed by using graph-based technique. Particle swarm optimization (PSO) algorithm is applied in second part. The application of PSO not only reduces the interference at significant level but also harvests true potential gains of each resource block with improved overall throughput of the system. The extensive simulation results demonstrate the effectiveness of the proposed scheme with the random resource allocation scheme and graph-based resource allocation scheme. In addition, proposed scheme performs better in case of increased proximity and supports the minimum data rate compared to the orthogonal sharing-based resource allocation and cellular-oriented resource allocation schemes.