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In this paper, we investigate a very challenging task of automatically generating presentation slides for academic papers. The generated presentation slides can be used as drafts to help the presenters prepare their formal slides in a quicker way. A novel system called PPSGen is proposed to address this task. It first employs the regression method to learn the importance scores of the sentences in an academic paper, and then exploits the integer linear programming (ILP) method to generate well-structured slides by selecting and aligning key phrases and sentences. Evaluation results on a test set of 200 pairs of papers and slides collected on the web demonstrate that our proposed PPSGen system can generate slides with better quality. A user study is also illustrated to show that PPSGen has a few evident advantages over baseline methods. [ABSTRACT FROM PUBLISHER]

Recently, evolutionary computation (EC) has experienced significant advancements due to the integration of machine learning, distributed computing, and big data technologies. These developments have led to new research avenues in EC, such as distributed EC and surrogate-assisted EC. While these advancements have greatly enhanced the performance and applicability of EC, they have also raised concerns regarding privacy leakages, specifically the disclosure of optimal results and surrogate models. Consequently, the combination of evolutionary computation and privacy protection becomes an increasing necessity. However, a comprehensive exploration of privacy concerns in evolutionary computation is currently lacking, particularly in terms of identifying the object, motivation, position, and method of privacy protection. To address this gap, this paper aims to discuss three typical optimization paradigms, namely, centralized optimization, distributed optimization, and data-driven optimization, to characterize optimization modes of evolutionary computation and proposes BOOM (i.e., oBject, mOtivation, pOsition, and Method) to sort out privacy concerns related to evolutionary computation. In particular, the centralized optimization paradigm allows clients to outsource optimization problems to a centralized server and obtain optimization solutions from the server. The distributed optimization paradigm exploits the storage and computational power of distributed devices to solve optimization problems. On the other hand, the data-driven optimization paradigm utilizes historical data to address optimization problems without explicit objective functions. Within each of these paradigms, BOOM is used to characterize the object and motivation of privacy protection. Furthermore, this paper discuss the potential privacy-preserving technologies that strike a balance between optimization performance and privacy guarantees. Finally, this paper outlines several new research directions for privacy-preserving evolutionary computation. [ABSTRACT FROM AUTHOR]

Parallel test paper generation is a biobjective distributed resource optimization problem, which aims to generate multiple similarly optimal test papers automatically according to multiple user-specified assessment criteria. Generating high-quality parallel test papers is challenging due to its NP-hardness in both of the collective objective functions. In this paper, we propose a submodular memetic approximation algorithm for solving this problem. The proposed algorithm is an adaptive memetic algorithm (MA), which exploits the submodular property of the collective objective functions to design greedy-based approximation algorithms for enhancing steps of the multiobjective MA. Synergizing the intensification of submodular local search mechanism with the diversification of the population-based submodular crossover operator, our algorithm can jointly optimize the total quality maximization objective and the fairness quality maximization objective. Our MA can achieve provable near-optimal solutions in a huge search space of large datasets in efficient polynomial runtime. Performance results on various datasets have shown that our algorithm has drastically outperformed the current techniques in terms of paper quality and runtime efficiency. [ABSTRACT FROM PUBLISHER]

This paper focuses on minimum-time path tracking, a subproblem in motion planning of robotic systems. We generate a time-optimal velocity profile for robotic manipulators taking into account kinematic and dynamic constraints. Based on the special structure of the constraints (called peaked constraints), profile generation is formulated as a linear programming (LP) problem. The LP-based control problem is solved by a sequential optimization method. The presented algorithm has reduced computational time compared to a general LP solver. [ABSTRACT FROM AUTHOR]

This paper presents a review of the literature on state estimation (SE) in power systems. While covering works related to SE in transmission systems, the main focus of this paper is distribution system SE (DSSE). The critical topics of DSSE, including mathematical problem formulation, application of pseudo-measurements, metering instrument placement, network topology issues, impacts of renewable penetration, and cyber-security are discussed. Both conventional and modern data-driven and probabilistic techniques have been reviewed. This paper can provide researchers and utility engineers with insights into the technical achievements, barriers, and future research directions of DSSE. [ABSTRACT FROM AUTHOR]

Web-based testing has become a ubiquitous self-assessment method for online learning. One useful feature that is missing from today's web-based testing systems is the reliable capability to fulfill different assessment requirements of students based on a large-scale question data set. A promising approach for supporting large-scale web-based testing is static test generation (STG), which generates a test paper automatically according to user specification based on multiple assessment criteria. And the generated test paper can then be attempted over the web by users for assessment purpose. Generating high-quality test papers under multiobjective constraints is a challenging task. It is a 0-1 integer linear programming (ILP) that is not only NP-hard but also need to be solved efficiently. Current popular optimization software and heuristic-based intelligent techniques are ineffective for STG, as they generally do not have guarantee for high-quality solutions of solving the large-scale 0-1 ILP of STG. To that end, we propose an efficient ILP approach for STG, called branch-and-cut for static test generation (BAC-STG). Our experimental study on various data sets and a user evaluation on generated test paper quality have shown that the BAC-STG approach is more effective and efficient than the current STG techniques. [ABSTRACT FROM PUBLISHER]

This article discusses how a hybrid algorithm based on a genetic algorithm (GA) and linear programming helps minimize distribution costs for the Scandinavian pulp producer Sodra Cell. The GA considers a planner's perspective by defining an individual schedule as the sequences of routes for all ships in a fixed fleet. Because the GA operates on a pool of potential solutions, constantly adjusting them to better fit the requirements, it can suggest multiple solutions. Furthermore, it can serve as a core algorithm in a support system where one can apply small changes to the set of constraints without a cold start. Applying the GA to Sodra Cell's real-world case and comparing it to mixed-integer-programming approach revealed competitive results. INSET: Related Work in Genetic Algorithms..

Recent studies on cloud-radio access networks assume either signal-level or scheduling-level coordination. This paper considers a hybrid coordinated scheme as a means to benefit from both policies. Consider the downlink of a multi-cloud radio access network, where each cloud is connected to several base-stations (BSs) via high capacity links and, therefore, allows for joint signal processing within the cloud transmission. Across the multiple clouds, however, only scheduling-level coordination is permitted, as low levels of backhaul communication are feasible. The frame structure of every BS is composed of various time/frequency blocks, called power-zones (PZs), which are maintained at a fixed power level. This paper addresses the problem of maximizing a network-wide utility by associating users to clouds and scheduling them to the PZs, under the practical constraints that each user is scheduled to a single cloud at most, but possibly to many BSs within the cloud, and can be served by one or more distinct PZs within the BSs’ frame. This paper solves the problem using graph theory techniques by constructing the conflict graph. The considered scheduling problem is, then, shown to be equivalent to a maximum-weight independent set problem in the constructed graph, which can be solved using efficient techniques. This paper then proposes solving the problem using both optimal and heuristic algorithms that can be implemented in a distributed fashion across the network. The proposed distributed algorithms rely on the well-chosen structure of the constructed conflict graph utilized to solve the maximum-weight independent set problem. Simulation results suggest that the proposed optimal and heuristic hybrid scheduling strategies provide appreciable gain as compared with the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination. [ABSTRACT FROM PUBLISHER]

In the first part of this paper, a free-form surface matching method called variance-minimization matching (VMM) was proposed to address uneven/open point distributions and measuring noise. The convergence property and sensitivity to measuring defects were theoretically studied. In the second part of this paper, a series of experiments are presented to verify the feasibility of the proposed method in free-form surface matching. The experiments are divided into four sets: a measuring defects experiment, a noise experiment, a convergence experiment, and an artificial experiment. In the first set of experiments, the existing methods are prone to becoming trapped in a local optimum affected by uneven/open point distributions, which shows that measured points incline toward dense areas. However, in VMM, there is little inclination regardless of the increase in the number of measuring defects. In the second set of experiments, sensitivity to varying noise is tested. The results show that VMM helps prevent unstable sliding in the presence of Gaussian noise. In the third set of experiments, we compare convergence speed and convergence stability under different initial positions. It is verified that VMM exhibits the quadratic convergence. Finally, a set of artificial experiments is implemented, revealing that the proposed method is appropriate for use in automated manufacturing processes such as geometric inspection and allowance distribution. Note to Practitioners—Measuring defects usually occur when using a scanning device to obtain the measured points of a workpiece. Weakening the effect of measuring defects on matching results is critical to promoting manufacturing automation. This paper proposes a new method called variance-minimization matching (VMM) that considers measuring defects. In the first part of this paper, the modeling and theoretical analysis of VMM were introduced. In the second part of this paper, simulated experiments are performed to verify the feasibility of VMM in addressing uneven/open point distributions, measuring noise, and large initial positions. Next, artificial experiments employing VMM in geometric inspection and allowance distribution are presented. The proposed method also applies to other automated manufacturing processes, such as workpiece localization, deformation analysis, and complex parts repair. [ABSTRACT FROM AUTHOR]

In this paper, a new joint spectral–spatial subpixel mapping model is proposed for hyperspectral remotely sensed imagery. Conventional approaches generally use an intermediate step based on the derivation of fractional abundance maps obtained after a spectral unmixing process, and thus the rich spectral information contained in the original hyperspectral data set may not be utilized fully. In this paper, a concept of subpixel abundance map, which calculates the abundance fraction of each subpixel to belong to a given class, was introduced. This allows us to directly connect the original (coarser) hyperspectral image with the final subpixel result. Furthermore, the proposed approach incorporates the spectral information contained in the original hyperspectral imagery and the concept of spatial dependence to generate a final subpixel mapping result. The proposed approach has been experimentally evaluated using both synthetic and real hyperspectral images, and the obtained results demonstrate that the method achieves better results when compared to other seven subpixel mapping methods. The numerical comparisons are based on different indexes such as the overall accuracy and the CPU time. Moreover, the obtained results are statistically significant at 95% confidence. [ABSTRACT FROM AUTHOR]

Recently, a number of empirical studies have compared the performance of PCA and ICA as feature extraction methods in appearance-based object recognition systems, with mixed and seemingly contradictory results. In this paper, we briefly describe the connection between the two methods and argue that whitened PCA may yield identical results to ICA in some cases. Furthermore, we describe the specific situations in which CA might significantly improve on PCA. [ABSTRACT FROM AUTHOR]

The dawn of quantum computing brings on a revolution in the way combinatorially complex power system problems such as Unit Commitment are solved. The Unit Commitment problem complexity is expected to increase in the future because of the trend toward the increase of penetration of intermittent renewables. Even though quantum computing has proven effective for solving a host of problems, its applications for power systems’ problems have been rather limited. In this paper, a quantum unit commitment is innovatively formulated and the quantum version of the decomposition and coordination alternate direction method of multipliers (ADMM) is established. The above is achieved by devising quantum algorithms and by exploiting the superposition and entanglement of quantum bits (qubits) for solving subproblems, which are then coordinated through ADMM to obtain feasible solutions. The main contributions of this paper include: 1) the innovative development of a quantum model for Unit Commitment; 2) development of decomposition and coordination-supported framework which paves the way for the utilization of limited quantum resources to potentially solve the large-scale discrete optimization problems; 3) devising the novel quantum distributed unit commitment (QDUC) to solve the problem in a larger scale than currently available quantum computers are capable of solving. The QDUC results are compared with those from its classical counterpart, which validate the efficacy of quantum computing. [ABSTRACT FROM AUTHOR]

Testing scenario library generation (TSLG) is a critical step for the development and deployment of connected and automated vehicles (CAVs). In Part I of this study, a general method for TSLG is proposed, and theoretical properties are investigated regarding the accuracy and efficiency of CAV evaluation. This paper aims to provide implementation examples and guidelines, and to enhance the proposed methodology under high-dimensional scenarios. Three typical cases, including cut-in, highway-exit, and car-following, are designed and studied in this paper. For each case, the process of library generation and CAV evaluation is elaborated. To address the challenges brought by high dimensionality, the proposed method is further enhanced by reinforcement learning technique. For all three cases, results show that the proposed method can accelerate the CAV evaluation process by multiple magnitudes with same evaluation accuracy, if compared with the on-road test method. [ABSTRACT FROM AUTHOR]

In this paper, the underlying assumptions that have been used for designing adaptive particle swarm optimization (PSO) algorithms in the past years are theoretically investigated. I relate these assumptions to the movement patterns of particles controlled by coefficient values (inertia weight and acceleration coefficients) and introduce three factors, namely the autocorrelation of the particle positions, the average movement distance of the particle in each iteration, and the focus of the search, that describe these movement patterns. I show how these factors represent movement patterns of a particle within a swarm and how they are affected by particle coefficients (i.e., inertia weight and acceleration coefficients). I derive equations that provide exact coefficient values to guarantee to achieve the desired movement pattern defined by these three factors within a swarm. I then relate these movements to the searching capability of particles and provide a guideline for designing potentially successful adaptive methods to control coefficients in particle swarm. Finally, I propose a new simple time adaptive particle swarm and compare its results with previous adaptive particle swarm approaches. Experiments show that the theoretical findings indeed provide a beneficial guideline for the successful adaptation of the coefficients in the PSO algorithm. [ABSTRACT FROM AUTHOR]

Role assignment is an important and complex task in collaboration and management. Group role assignment (GRA) pursues the optimal team performance by assigning pertinent roles to agents in a team from the team’s viewpoint. There are many factors that should be considered when conducting role assignment. Budgets are such a factor. This paper presents a challenging role assignment problem in collaboration, called GRA with budget constraints (GRABC). This paper contributes a thorough investigation of the GRABC problem including: 1) the first formalization of the proposed problem; 2) a set of practical solutions to different forms of this problem by using the IBM ILOG CPLEX optimization package (CPLEX); 3) a set of necessary conditions for these problems to possess feasible solutions to improve the CPLEX solutions; and 4) a synthesized process to deal with GRABC problems. Contribution 2) actually clarifies a list of requirements in solving the GRABC problem. Simulations, experiments and a case study are used to verify the practicability and efficiency of the proposed solutions. [ABSTRACT FROM AUTHOR]

Constrained multiobjective optimization problems (CMOPs) are frequently encountered in real-world applications, which usually involve constraints in both the decision and objective spaces. However, current artificial CMOPs never consider constraints in the decision space (i.e., decision constraints) and constraints in the objective space (i.e., objective constraints) at the same time. As a result, they have a limited capability to simulate practical scenes. To remedy this issue, a set of CMOPs, named DOC, is constructed in this paper. It is the first attempt to consider both the decision and objective constraints simultaneously in the design of artificial CMOPs. Specifically, in DOC, various decision constraints (e.g., inequality constraints, equality constraints, linear constraints, and nonlinear constraints) are collected from real-world applications, thus making the feasible region in the decision space have different properties (e.g., nonlinear, extremely small, and multimodal). On the other hand, some simple and controllable objective constraints are devised to reduce the feasible region in the objective space and to make the Pareto front have diverse characteristics (e.g., continuous, discrete, mixed, and degenerate). As a whole, DOC poses a great challenge for a constrained multiobjective evolutionary algorithm (CMOEA) to obtain a set of well-distributed and well-converged feasible solutions. In order to enhance current CMOEAs’ performance on DOC, a simple and efficient two-phase framework, named ToP, is proposed in this paper. In ToP, the first phase is implemented to find the promising feasible area by transforming a CMOP into a constrained single-objective optimization problem. Then in the second phase, a specific CMOEA is executed to obtain the final solutions. ToP is applied to four state-of-the-art CMOEAs, and the experimental results suggest that it is quite effective. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization approach for sizing port rail networks and planning railway shunting operations by adopting a discrete-time model of the overall system. First, a mixed-integer linear mathematical programming problem is defined in order to optimize shunting operations to be performed on the considered network by satisfying certain arrivals and departures of import and export flows. Moreover, the proposed procedure can be used to evaluate the capacity of a port rail network, in terms of maximum number of trains that can be managed over a certain time horizon, and to carry out what-if analyses aimed at testing different scenarios. The effectiveness of the proposed approach is shown by applying the optimization problem to a real case study referred to the port rail network of La Spezia Container Terminal located in Northern Italy. A computational analysis realized by varying the dimension and complexity of the problem instances is also reported in this paper to discuss the computational performance of the proposed model. [ABSTRACT FROM PUBLISHER]

An iterated greedy algorithm (IGA) is a simple and powerful heuristic algorithm. It is widely used to solve flow-shop scheduling problems (FSPs), an important branch of production scheduling problems. IGA was first developed to solve an FSP in 2007. Since then, various FSPs have been tackled by using IGA-based methods, including basic IGA, its variants, and hybrid algorithms with IGA integrated. Up until now, over 100 articles related to this field have been published. However, to the best of our knowledge, there is no existing tutorial or review paper of IGA. Thus, we focus on FSPs and provide a tutorial and comprehensive literature review of IGA-based methods. First, we introduce a framework of basic IGA and give an example to clearly show its procedure. To help researchers and engineers learn and apply IGA to their FSPs, we provide an open platform to collect and share related materials. Then, we make classifications of the solved FSPs according to their scheduling scenarios, objective functions, and constraints. Next, we classify and introduce the specific methods and strategies used in each phase of IGA for FSPs. Besides, we summarize IGA variants and hybrid algorithms with IGA integrated, respectively. Finally, we discuss the current IGA-based methods and already-solved FSP instances, as well as some important future research directions according to their deficiency and open issues. Note to Practitioners—Many practical scheduling problems can be transformed into flow-shop scheduling problems (FSPs), most of which are NP-hard. In order to solve them in an industrial system setting, designing effective heuristics is important and practically useful and has, thus, attracted much attention from both researchers and engineers. As an easy and high-performance heuristic, an iterated greedy algorithm (IGA) is widely used and adapted to solve numerous FSPs. Its simple framework makes it easy to be implemented by practitioners, and its high performance implies its great potential to solve industrial scheduling problems. In this work, we aim to give practitioners a comprehensive overview of IGA and help them apply IGA to solve their particular industrial scheduling problems. We review the papers that solve FSPs with IGA-based methods, including basic IGA, its variants, and hybrid algorithms with IGA integrated. First, we provide practitioners with a tutorial on IGA, where an example for solving an FSP is introduced and an open platform is constructed. The platform collects and shares the related materials, e.g., open-source code, benchmarks, and website links of important papers. Then, we introduce various FSPs and specific designs of IGA-based methods. Finally, we discuss the current research and point out future research issues. [ABSTRACT FROM AUTHOR]

Multi-label classification has gained in importance in the last decade and it is today confronted to the current needs to process massive raw data from heterogeneous sources. Therefore, dimensionality reduction, which aims at reducing the number of features, labels, or both, knows a renewed interest to enhance the scaling properties of the classifiers and their predictive performances. In this paper we review more than fifty papers presenting dimensionality reduction approaches for multi-label classification and we propose an analysis in three steps : (i) a typology of the methods describing the main components of their strategies, the problem they tackle and the way they solve it (ii) a unified formalization of the problems to help to distinguish the similarities and differences between the approaches, and (iii) a meta-analysis of the published experimental results inspired by the consensus theory to identify the most efficient algorithms. [ABSTRACT FROM AUTHOR]

The Model Predictive Control (MPC) framework has shown great potential for the control of Variable Speed Limits (VSLs) and Ramp Metering (RM) installations. However, the implementation to large freeway networks remains challenging. One major reason is that, by considering the VSLs to be discrete decision variables, an extremely difficult Mixed Integer Nonlinear Programming (MINLP) optimization problem has to be solved within every controller sampling interval. Consequently, many related papers relax the MINLP problems by considering the VSLs to be continuous variables. This paper proposes two novel MPC algorithms for coordinated control of discrete VSLs and continuous RM rates that do not make this relaxation. The proposed algorithms use a distributed control architecture and an alternating optimization scheme to relax the MINLP optimization problems but still consider the VSLs as discrete variables and, hence, offer a trade-off between computational complexity and system performance. The performance of the proposed algorithms is evaluated in a case study. The case study shows that relaxing the VSLs to be continuous variables with a distributed architecture results in a significant performance loss. Furthermore, both proposed algorithms have a lower computational complexity than the more conventional centralized approach and, as a result, they do manage to solve all optimization problems within the sampling intervals. Moreover, one of the proposed algorithms has a system performance that is remarkably similar to the optimal performance of the centralized approach. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have attracted enormous attention in recent years due to their potentials in mitigating energy crisis and air pollutions. However, the long battery charging time and lack of sufficient charging infrastructure highly restrict the popularization of EVs. In this context, it is promising to establish battery charging and swapping systems (BCSSs) based on the concept of battery swapping services. To optimally achieve the combined operation of BCSSs, this paper proposes a hybrid swapped battery charging and logistics dispatch model in continuous time domain. Identifying the special structure of the mathematical models of the two problems, this paper innovatively formulated the swapped battery charging strategy as the rectangle packing problem and the battery logistics model as the vehicle routing problem. The two models are closely linked by the delivery time of transporting the well-charged batteries from battery charging stations to battery swapping stations. A hybrid optimal operation model of BCSSs is further formulated as a mixed-integer linear programming model by incorporating the interaction between the battery charging and battery logistics. Finally, case studies are conducted on several BCSSs and numerical results validate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

A fault tree (FT) defines an undesired top event, characterizing it using logic combinations of lower-level undesired events. In this paper, we focus on coherent FTs, i.e., the logic is restricted to AND/OR formulas. FT analysis is used to identify and assess the minimal cut sets (MCSs) of an FT, which define the minimal set of events leading to the undesired state. The dual of MCS is minimal path set (MPS). MCS and MPS are commonly used for qualitative evaluation of FTs in safety and reliability engineering. This paper explores computation of the MCS/MPS of an FT by means of structural analysis (namely, computation of minimal p -semiflows) of a Petri net (PN) that represents the FT. To this end, we propose a formal definition of a coherent FT and a transformation from this model to a PN subclass (namely, structurally persistent nets). We also prove the relationship between minimal p -semiflows and MCS/MPS in an FT. In addition, we propose an algorithm that uses linear programming techniques to compute the MCS/MPS in an FT. Finally, we put our findings into practice by qualitatively evaluating the FT of a pressure tank system. [ABSTRACT FROM PUBLISHER]

Rescheduling in railway networks is a challenging problem in both practice and theory. It requires good quality solutions in reasonable computation time to resolve unexpected situations, involving different problem scales, railway network infrastructures, objectives, and constraints. This paper presents a comprehensive survey on different problem models for rescheduling in railway networks by a clear classification. Some frequently used models are described in detail through reviewing their variables and constraints. This paper also focuses on the solution approaches proposed in the literature. The main ideas of the solution approaches with the objectives are described. Based on our review results, the analysis of the problem models used in various problem types and the solution approaches used in different problem models are presented. Conclusion and suggestions for further research to rescheduling in railway networks are drawn toward the end of the paper. [ABSTRACT FROM PUBLISHER]

A substantial body of literature on global design optimization techniques is based on an implicit assumption that a system under optimization is designed by a perfectly centralized design team with all team members committed to a global optimum solution. This assumption enables the system engineers to minimize all the objective functions simultaneously while controlling all design decisions at the same time. Recent research suggested that this inherent assumption may need to be revisited. This paper proposes a novel framework for design optimization using game theory, which represents a design process as a normal-form game and unifies two fundamental decisions of discipline designers, namely a system-global or a discipline-local optimum solution. This paper provides a general mathematical formulation of the framework for a design process with any number of discipline designers. The framework introduces the notion of the Nash front, which is a set of design solutions consisting of the Nash equilibria derived through the reallocation of design authority between discipline designers. This paper then tests the framework on a numerical optimization case study of a Stirling micro combined heat and power plant with two discipline designers. The result indicates that for the system capital cost and the system power output the difference between global Pareto-optimum designs and the Nash front designs on average across five different scenarios of design authority allocation was 22% and 16%, correspondingly. The application of the novel framework in different fields of engineering could help to build models that are more realistic and prevent impractical technical objectives. [ABSTRACT FROM AUTHOR]

The IEC Standard 60287-1-1 contains the formulae for the calculation of the sheath and armor loss factors of a 3-phase cable system. Sheath circulating current losses are computed for single-, multiple-, or cross-bonded systems. This paper deals with cross-bonded cable systems. For unequal minor section lengths, the standard gives a formula allowing calculation of the circulating current losses when the lengths of the minor sections are known. The origin of this formula is not documented; hence, this paper gives its derivation. When the lengths of the minor sections are not known, the standard suggests using a factor of 0.004, which multiplies the circulating current loss factor computed for a multi-point bonded system. In this paper, the cable sheath circulating loss factor was found using different methods with the aim of checking the applicability of the 0.004 multiplier to practical cable systems with differences in the length of minor sections and system voltage. Three different cable systems are studied; the first is a 132 kV circuit with known lengths of minor and major sections. The second circuit is a 220 kV system where the lengths of minor sections are unknown and the third is a 220 kV real system with known, highly irregular section lengths. The calculated values of sheath circulating current loss factor, applying the formulas from the IEC 60287-1-1, were compared to the values calculated based on practical measurements of sheath induced voltages and currents. The effect of taking the measured and IEC values on the continuous current rating (ampacity) is also presented for the first two circuits and the comparison of the unknown and known section lengths for the third circuit is offered. [ABSTRACT FROM AUTHOR]

When solving constrained optimization problems (COPs) by evolutionary algorithms, the search algorithm plays a crucial role. In general, we expect that the search algorithm has the capability to balance not only diversity and convergence but also constraints and objective function during the evolution. For this purpose, this paper proposes a composite differential evolution (DE) for constrained optimization, which includes three different trial vector generation strategies with distinct advantages. In order to strike a balance between diversity and convergence, one of these three trial vector generation strategies is able to increase diversity, and the other two exhibit the property of convergence. In addition, to accomplish the tradeoff between constraints and objective function, one of the two trial vector generation strategies for convergence is guided by the individual with the least degree of constraint violation in the population, and the other is guided by the individual with the best objective function value in the population. After producing offspring by the proposed composite DE, the feasibility rule and the $\boldsymbol {\varepsilon }$ constrained method are combined elaborately for selection in this paper. Moreover, a restart scheme is proposed to help the population jump out of a local optimum in the infeasible region for some extremely complicated COPs. By assembling the above techniques together, a constrained composite DE is proposed. The experiments on two sets of benchmark test functions with various features, i.e., 24 test functions from IEEE CEC2006 and 18 test functions with 10 dimensions and 30 dimensions from IEEE CEC2010, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication is a promising technique to enhance the spectrum efficiency of a cellular network. However, one of the major challenges in D2D communications is to select appropriate communication modes for all possible D2D pairs in a D2D network. In this paper, we study the mode selection problems in a multi-mode and multi-pair D2D network, where the eNB can assign one of the three D2D communication modes (including local route mode, direct D2D mode, and relay D2D mode) to each D2D pair. To achieve the maximal cell-wise capacity, an optimal mode selection algorithm is proposed in this paper with the aim to share cellular users’ uplink channels. The optimal mode selection problem can be modeled by an integer programming problem. Particularly, in a fully loaded cell, we discover that the optimal mode selection problem can be solved using a linear programming algorithm with a significantly reduced complexity. [ABSTRACT FROM AUTHOR]

This paper investigates the impacts of incorporating energy storage devices (ESD) into a virtual power plant (VPP) model and subsequently using it for performing generating system adequacy studies. A two-step linearized optimization formulation is proposed for developing the overall VPP model featuring the aggregated characteristics and capacities of several distributed generators (DG), ESDs and loads together with distribution network information like topology and constraints. While the first step determines the (charging or discharging) operating mode of the batteries, the second step outputs the optimal dispatch schedules for the DGs and ESDs in accordance with a fair energy allocation based operating strategy adopted in this paper. The VPP is modeled using an augmented IEEE 69-bus distribution network, and the overall generation adequacy studies incorporating the VPP are carried out on the IEEE Reliability Test System using a sequential simulation algorithm. Several indices are presented for quantifying the impacts of the ESDs’ incorporation on the VPP model’s performance. Finally, sensitivity studies conducted for investigating the effects of variations in the ESD rated capacities and charging/discharging periods on the VPP performance and system adequacy indices are also described. [ABSTRACT FROM AUTHOR]

Home healthcare (HHC) is a service that dispatches caregivers to people in need of healthcare who live in the home. The task assignment and route generation for caregivers can be formulated as an extension of the well-known vehicle routing problem with time windows (VRPTW). Currently, the most successful exact algorithms for VRPTW are based on a framework combining column generation (CG) and branching. Although such methods could be successful for HHC routing and scheduling problem (HHCRSP) as well, fast approximate algorithms are appealing, especially for large problems. In an early version of this paper, we employed a heuristic distributed gossip algorithm to solve HHCRSP. In this paper, we integrate the gossip algorithm with a local solver based on CG, which makes it an effective algorithm for larger problem instances. As it will be shown with extensive numerical experiments, for large problem instances, gossip-CG performs better than the pure CG. Note to Practitioners—The task of routing and scheduling caregivers for HHC providers can be formulated as a mathematical problem that is theoretically challenging to solve to optimality for large-scale problems involving many clients and caregivers. Although extensive research and tools for tackling this problem already exist, in practice, even in developed countries, it is still common to rely on schedules generated manually by staff. What we present in this paper is the summary of our theoretical work on tackling such problems. The benefit of the framework that we propose, the CG-based gossip method, is that it can be preempted at any time if the staff requires to obtain a good schedule as soon as possible. If ample time is available, one can let our algorithm to run for a longer period of time to generate even shorter routes. [ABSTRACT FROM AUTHOR]

Community structures are critical towards understanding not only the network topology but also how the network functions. However, how to evaluate the quality of detected community structures is still challenging and remains unsolved. The most widely used metric, normalized mutual information (NMI), was proven to have finite size effect, and its improved form relative normalized mutual information (rNMI) has reverse finite size effect. Corrected normalized mutual information (cNMI) was thus proposed and has neither finite size effect nor reverse finite size effect. However, in this paper, we show that cNMI violates the so-called proportionality assumption. In addition, NMI-type metrics have the problem of ignoring importance of small communities. Finally, they cannot be used to evaluate a single community of interest. In this paper, we map the computed community labels to the ground-truth ones through integer linear programming, and then use kappa index and F-score to evaluate the detected community structures. Experimental results demonstrate the advantages of our method. [ABSTRACT FROM AUTHOR]

N-ary relationships, which relate $N$ entities where $N$ is not necessarily two, can be visually represented as polygons whose vertices are the entities of the relationships. Manually generating a high-quality layout using this representation is labor-intensive. In this paper, we provide an automatic polygon layout generation algorithm for the visualization of N-ary relationships. At the core of our algorithm is a set of objective functions motivated by a number of design principles that we have identified. These objective functions are then used in an optimization framework that we develop to achieve high-quality layouts. Recognizing the duality between entities and relationships in the data, we provide a second visualization in which the roles of entities and relationships in the original data are reversed. This can lead to additional insight about the data. Furthermore, we enhance our framework for a joint optimization on the primal layout (original data) and the dual layout (where the roles of entities and relationships are reversed). This allows users to inspect their data using two complementary views. We apply our visualization approach to a number of datasets that include co-authorship data and social contact pattern data. [ABSTRACT FROM AUTHOR]

This paper presents an optimization framework for sequential reconfiguration using an assortment of switching devices and repair process in distribution system restoration. Compared to existing studies, this paper considers types, capabilities and operational limits of different switching devices, making it applicable in practice. We develop a novel multi-phase method to find the optimal sequential operation of various switching devices and repair faulted areas. We consider circuit breakers, reclosers, sectionalizers, load breaker switches, and fuses. The switching operation problem is decomposed into two mixed-integer linear programming (MILP) subproblems. The first subproblem determines the optimal network topology and estimates the number of steps to reach that topology, while the second subproblem generates a sequence of switching operations to coordinate the switches. For repairing the faults, we design an MILP model that dispatches repair crews to clear faults and replace melted fuses. After clearing a fault, we update the topology of the network by generating a new sequence of switching operations, and the process continues until all faults are cleared. To improve the computational efficiency, a network reduction algorithm is developed to group line sections, such that only switchable sections are present in the reduced network. The proposed method is validated on the IEEE 123-bus and 8500-bus systems. [ABSTRACT FROM AUTHOR]

Spectral clustering (SC) algorithms have been successful in discovering meaningful patterns since they can group arbitrarily shaped data structures. Traditional SC approaches typically consist of two sequential stages, i.e., performing spectral decomposition of an affinity matrix and then rounding the relaxed continuous clustering result into a binary indicator matrix. However, such a two-stage process could make the obtained binary indicator matrix severely deviate from the ground true one. This is because the former step is not devoted to achieving an optimal clustering result. To alleviate this issue, this paper presents a general joint framework to simultaneously learn the optimal continuous and binary indicator matrices for multi-view clustering, which also has the ability to tackle the conventional single-view case. Specially, we provide theoretical proof for the proposed method. Furthermore, an effective alternate updating algorithm is developed to optimize the corresponding complex objective. A number of empirical results on different benchmark datasets demonstrate that the proposed method outperforms several state-of-the-arts in terms of six clustering metrics. [ABSTRACT FROM AUTHOR]

During the detailed design phase of an aerospace program, one of the most important consistency checks is to ensure that no two distinct objects occupy the same physical space. Since exact geometrical modeling is usually intractable, geometry models are discretized, which often introduces small interferences not present in the fully detailed model. In this paper, we focus on computing the depth of the interference, so that these false positive interferences can be removed, and attention can be properly focused on the actual design. Specifically, we focus on efficiently computing the penetration depth between two polyhedra, which is a well-studied problem in the computer graphics community. We formulate the problem as a constrained five-variable global optimization problem, and then derive an equivalent unconstrained, two-variable nonsmooth problem. To solve the optimization problem, we apply a popular stochastic multistart optimization algorithm in a novel way, which exploits the advantages of each problem formulation simultaneously. Numerical results for the algorithm, applied to 14 randomly generated pairs of penetrating polytopes, illustrate both the effectiveness and efficiency of the method. [ABSTRACT FROM AUTHOR]

The ever-increasing integration of non-dispatchable distributed generation, i.e., renewable energy sources (RES), arises new challenges in the field of power system's reliability. Distribution network reconfiguration (DNR) is a cost-effective approach for the distribution system operator (DSO) that wishes to enhance system's reliability without infrastructure upgrades. This paper introduces a novel path-based mixed-integer second-order cone programming model to optimally solve the reliability-oriented DNR problem. The DSO's objectives that are optimized are: a) improvement of distribution system's reliability indices and b) minimization of power losses. The proposed model is enriched with a scenario-based stochastic programming formulation that considers multiple levels of load and RES production. The standard 33-nodes distribution system and a real-world 83-nodes distribution system are employed to prove the efficiency and applicability of the model. Firstly, the multi-objective nature of the reliability-oriented DNR problem is investigated by conducting a sensitivity analysis, which reveals a trade-off region between reliability indices and power losses. Moreover, the obtained results show different global optimal solutions when the variability of load and RES production is considered. This highlights the importance of considering a scenario-based approach for load and RES production when solving the reliability-oriented DNR problem. [ABSTRACT FROM AUTHOR]

It is an important topic to organize a team efficiently and keep it in a good state. In most cases, administrators try to avoid critical members. With this requirement, administrators prefer that their team members are able to be good at many things and expert in one (GMEO). However, that too many people are “good at many things” is definitely a waste. Role-based collaboration and its environments—classes, agents, roles, groups, and objects (E-CARGO) model are a good means to provide modeling and solutions to such a challenging problem. This paper formalizes the problem of GMEO into GMEO-1 (the fundamental form of GMEO) with the support of E-CARGO, clarifies two different forms of GMEO-1 and provides two highly practical solutions. The proposed solutions are verified by comparing with initial solutions using a linear programming solver, i.e., the IBM ILOG CPLEX Optimization Platform. The contributions of this paper are a thorough investigation of GMEO-1 that has no exact solutions even for a small group (e.g., ten people) and is normally managed by highly qualified administrators. The proposed solutions provide digital results with algorithms that form a solid foundation for decision making in dealing with similar issues. [ABSTRACT FROM AUTHOR]

The primary goal of this paper is to develop an optimization framework for studying the efficacy of transmission line reactance tweaking as a mechanism for post-disturbance control in a transmission network. We start by developing a mixed-integer linear programming (MILP) formulation for tracking the redistribution of direct current (DC) flows and the graph-theoretic evolution of a network topology over the course of cascading failures. Next, we propose a min–max setup for studying the impact of post-disturbance reactance tweaking on the resilience of the system to a worst-case disturbance. We devise a MILP reformulation scheme for the underlying bilevel non-convex mixed-integer nonlinear program to facilitate the computation of its globally optimal solution. We then develop a MILP framework for computing a tight upper bound (best-case scenario) on the efficacy of post-disturbance reactance tweaking for a given set of bus loads. Our numerical case study suggests that post-disturbance reactance tweaking, even on only a small number of lines, can be effective in reducing the amount of load shed in some scenarios in the tested system. [ABSTRACT FROM AUTHOR]

For accommodating more electric vehicles (EVs) to battle against fossil fuel emission, the problem of charging station placement is inevitable and could be costly if done improperly. Research considers a general setup using conditions such as driving ranges for planning. However, most of the EV growths in the next decades will happen in urban areas where driving range is not the biggest concern. For such a need, we consider several practical aspects of urban systems, such as voltage regulation cost and protection device upgrade resulting from the large integration of EVs. Notably, our diversified objective can reveal the trade-off between different factors in different cities worldwide. To understand the global optimum of large-scale analysis, we studied each feature to preserve the problem convexity. Our sensitivity analysis before and after convexification shows that our approach is not only universally applicable but also has a small approximation error for prioritizing the most urgent constraint in a specific setup. Finally, numerical results demonstrate the trade-off, the relationship between different factors and the global objective, and the small approximation error. A unique observation in this paper shows the importance of incorporating the protection device upgrade in urban system planning on charging stations. [ABSTRACT FROM AUTHOR]

This paper proposes a coordination method for vehicles on merging roads to realize smooth traffic merging. The proposed method can be implemented with a low communication volume in an environment in which the automated/connected vehicles and manually operated vehicles are mixed. The method is based on a pseudo-perturbation-based broadcast controller (PBC), which has the potential to coordinate multiple vehicles with low-cost V2I communication without V2V communication. The PBC indiscriminately broadcasts an identical signal to the vehicles. The volume of such a broadcast communication is less than that of the commonly used unicast communication. This paper overcomes the following three issues to apply the PBC to the traffic merging problem. First, the time-invariant metrics are derived to design a time-invariant objective function corresponding to the traffic merging problem because the PBC is based on the minimization of such a time-invariant function. Second, a (locally) convex objective function to be globally minimized is designed. Finally, the collision avoidance between vehicles is guaranteed. The microscopic traffic simulations demonstrate the effectiveness of the proposed PBC-based coordination method in the presence of uncooperative manually operated vehicles. [ABSTRACT FROM AUTHOR]

Droop control is a viable method for the operation of island DC microgrids in a decentralized architecture. This paper presents a mixed-integer conic optimization formulation for the design of generator droop control, comprising the parameters of a piecewise linear droop curve. The mixed-integer formulation originates from a stochastic optimization framework that considers several operating scenarios for finding the optimal design. The convexity of the mixed-integer problem continuous relaxation gives global optimality guarantees for the design problem. The paper presents computational results using a tight polyhedral approximation of the conic program, leading to a mixed-integer linear programming (MILP) problem that is solved using a state-of-the-art commercial solver. The results from the proposed approach are contrasted with both a classic linear droop control design and a recent piecewise linear formulation. The Monte-Carlo simulation results quantify the extent to which the MILP solution is superior in reducing voltage violations and power loss, and the degree to which the loss is close to that from a conic optimal power flow solution. [ABSTRACT FROM AUTHOR]

This paper proposes a path tracking controller to assist the electronic-four-wheel drive (e-4WD) vehicle in following the desired path profile. It utilizes only readily available in-vehicle sensors and a standalone global positioning system (GPS). Noteworthy, both the lateral distance error and the heading angle error between the desired path profile and the vehicle are calculated using only a standalone GPS. Therefore, the proposed algorithm can be easily applied to mass-produced e-4WD vehicles. A model predictive control (MPC) is selected as the controller type, which derives optimal control input considering both state and input constraints of in-wheel motor (IWM) in the e-4WD vehicle. Due to the advantage of this MPC predicting the vehicle's future dynamic behavior in advance, it is possible to output a more preemptive and stable yaw moment for vehicle path tracking. Finally, a weighted least square (WLS) allocation that is suitable for redundant actuator configuration is utilized to distribute the optimal IWM torques to the front left and right wheels of the e-4WD vehicle. The major contributions of the proposed control algorithm are as follows. 1) It is the first time in this paper that the MPC is applied to path tracking of e-4WD vehicles. 2) The condensed constraint matrix of the proposed MPC has a structure that can be easily applied to the linear programming. Using the CarSim simulation and the real-car based experiment, the effectiveness of the proposed MPC algorithm is verified by a comparative analysis. Thus, the high path tracking accuracy of the proposed algorithm is confirmed. [ABSTRACT FROM AUTHOR]

Uplink-downlink duality refers to the fact that under a sum-power constraint, the capacity regions of a Gaussian multiple-access channel and a Gaussian broadcast channel with Hermitian transposed channel matrices are identical. This paper generalizes this result to a cooperative cellular network, in which remote access-points are deployed as relays in serving the users under the coordination of a central processor (CP). In this model, the users and the relays are connected over noisy wireless links, while the relays and the CP are connected over noiseless but rate-limited fronthaul links. Based on a Lagrangian technique, this paper establishes a duality relationship between such a multiple-access relay channel and broadcast relay channel, under the assumption that the relays use compression-based strategies. Specifically, we show that under the same total transmit power constraint and individual fronthaul rate constraints, the achievable rate regions of the Gaussian multiple-access and broadcast relay channels are identical, when either independent compression or Wyner-Ziv and multivariate compression strategies are used. The key observations are that if the beamforming vectors at the relays are fixed, the sum-power minimization problems under the achievable rate and fronthaul constraints in both the uplink and the downlink can be transformed into either a linear programming or a semidefinite programming problem depending on the compression technique, and that the uplink and downlink problems are Lagrangian duals of each other. Moreover, the dual variables corresponding to the downlink rate constraints become the uplink powers; the dual variables corresponding to the downlink fronthaul constraints become the uplink quantization noises. This duality relationship enables an efficient algorithm for optimizing the downlink transmission and relaying strategies based on the uplink. [ABSTRACT FROM AUTHOR]

Earth geomagnetic field provides very important information for autonomous navigation. However, in practice, magnetometer measurements are easily distorted by outer disturbances, as shown in the right diagram. In a recent study, the real-time magnetometer disturbance estimation problem has been solved via a constrained nonlinear programming for better underwater navigation accuracy. However, the employed interior-point optimizer will require considerable computational resources and cannot always guarantee optimality during optimization updates. This paper further investigates this problem and refines the solution by introducing the trust-region method. The challenge of the designed approach mainly falls into finding out the feasible regions of the possible solutions. Geometric analysis and algebraic elimination methods are taken into account to give globally optimal solutions to the trust-region solver. Experimentations are conducted to verify the correctness of the proposed method. Moreover, it is also confirmed that the trust-region technique can continuously and accurately propagate so that high-frequency dynamic magnetic disturbances can be efficiently estimated. [ABSTRACT FROM AUTHOR]

This paper proposes a sequential convex optimization method to solve broader classes of optimal power flow (OPF) problems over radial networks. The non-convex branch power flow equation is decomposed as a second-order cone inequality and a non-convex constraint involving the difference of two convex functions. Provided with an initial solution offered by an inexact second-order cone programming relaxation model, this approach solves a sequence of convexified penalization problems, where concave terms are approximated by linear functions and updated in each iteration. It could recover a feasible power flow solution, which usually appears to be very close, if not equal, to the global optimal one. Two variations of the OPF problem, in which non-cost related objectives are optimized subject to power flow constraints and the convex relaxation is generally inexact, are elaborated in detail. One is the maximum loadability problem, which is formulated as a special OPF problem that seeks the maximal distance to the boundary of power flow insolvability. The proposed method is shown to outperform commercial nonlinear solvers in terms of robustness and efficiency. The other is the bi-objective OPF problem. A non-parametric scalarization model is suggested, and is further reformulated as an extended OPF problem by convexifying the objective function. It provides a single trade-off solution without any subjective preference. The proposed computation framework also helps retrieve the Pareto front of the bi-objective OPF via the $\varepsilon $ -constraint method or the normal boundary intersection method. This paper also discusses extensions for OPF problems over meshed networks based on the semidefinite programming relaxation method. [ABSTRACT FROM PUBLISHER]

The non-negative matrix factorization (NMF) algorithm represents the original image as a linear combination of a set of basis images. This image representation method is in line with the idea of “parts constitute a whole” in human thinking. The existing deep NMF performs deep factorization on the coefficient matrix. In these methods, the basis images used to represent the original image is essentially obtained by factorizing the original images once. To extract features reflecting the deep localization characteristics of images, a novel deep NMF architecture based on underlying basis images learning is proposed for the first time. The architecture learns the underlying basis images by deep factorization on the basis images matrix. The deep factorization architecture proposed in this paper has strong interpretability. To implement this architecture, this paper proposes a deep non-negative basis matrix factorization algorithm to obtain the underlying basis images. Then, the objective function is established with an added regularization term, which directly constrains the basis images matrix to obtain the basis images with good local characteristics, and a regularized deep non-negative basis matrix factorization algorithm is proposed. The regularized deep nonlinear non-negative basis matrix factorization algorithm is also proposed to handle pattern recognition tasks with complex data. This paper also theoretically proves the convergence of the algorithm. Finally, the experimental results show that the deep NMF architecture based on the underlying basis images learning proposed in this paper can obtain better recognition performance than the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The task of point of interest (POI) recommendation aims to recommend unvisited places to users based on their check-in history. A major challenge in POI recommendation is data sparsity, because a user typically visits only a very small number of POIs among all available POIs. In this paper, we propose AUC-MF to address the POI recommendation problem by maximizing Area Under the ROC curve (AUC). AUC has been widely used for measuring classification performance with imbalanced data distributions. To optimize AUC, we transform the recommendation task to a classification problem, where the visited locations are positive examples and the unvisited are negative ones. We define a new lambda for AUC to utilize the LambdaMF model, which combines the lambda-based method and matrix factorization model in collaborative filtering. Many studies have shown that geographic information plays an important role in POI recommendation. In this study, we focus on two levels geographic information: local similarity and global similarity. We further show that AUC-MF can be easily extended to incorporate geographical contextual information for POI recommendation. Specifically, we propose two novel methods to incorporate geographical information in AUC-MF. Different from most existing models where the contextual information are incorporated into the objective function, the incorporation of contextual information in AUC-MF is a refinement of the model and a sampling strategy. The sampling strategy could speedup convergence and the refining of recommendations is independent of training of the model. This mechanism also enables AUC-MF to be able produce recommendations refined towards different contextual information, with minimum computational cost. Experiments on two datasets show that the proposed AUC-MF outperforms state-of-the-art methods significantly in terms of recommendation accuracy. [ABSTRACT FROM AUTHOR]

The number of individuals and groups of users offloading independent and inter-related computational tasks to mobile edge computing (MEC) servers is rapidly increasing, thus overloading them and raising the risk of service interruptions. Hence, reactive service replication has been suggested to enable individuals and groups of users to access services from remote edge servers, thus guaranteeing system scalability. This paper proposes a task offloading and service replication scheme on local and remote MEC servers. The scheme minimizes the response time of all users while satisfying the delay requirements of user groups in traffic-heavy and multimedia-intense applications (e.g., online gaming, multimedia conferencing, augmenting reality). We formulate an integer linear problem that minimizes the average response time of all users while satisfying the time and time difference constraints of the user groups running the same applications. We then use linear relaxation programming using Lagrangian analysis and solve the problem using a numerical solver. In addition, we compare the optimal solution to distance-based and resource-based greedy approaches. The results demonstrate the merits of our proposed optimized decision scheme compared to these two greedy approaches. [ABSTRACT FROM AUTHOR]

In this paper, we reveal that metric learning would suffer from serious inseparable problem if without informative sample mining. Since the inseparable samples are often mixed with hard samples, current informative sample mining strategies used to deal with inseparable problem may bring up some side-effects, such as instability of objective function, etc. To alleviate this problem, we propose a novel distance metric learning algorithm, named adaptive neighborhood metric learning (ANML). In ANML, we design two thresholds to adaptively identify the inseparable similar and dissimilar samples in the training procedure, thus inseparable sample removing and metric parameter learning are implemented in the same procedure. Due to the non-continuity of the proposed ANML, we develop an ingenious function, named log-exp mean function to construct a continuous formulation to surrogate it, which can be efficiently solved by the gradient descent method. Similar to Triplet loss, ANML can be used to learn both the linear and deep embeddings. By analyzing the proposed method, we find it has some interesting properties. For example, when ANML is used to learn the linear embedding, current famous metric learning algorithms such as the large margin nearest neighbor (LMNN) and neighbourhood components analysis (NCA) are the special cases of the proposed ANML by setting the parameters different values. When it is used to learn deep features, the state-of-the-art deep metric learning algorithms such as Triplet loss, Lifted structure loss, and Multi-similarity loss become the special cases of ANML. Furthermore, the log-exp mean function proposed in our method gives a new perspective to review the deep metric learning methods such as Prox-NCA and N-pairs loss. At last, promising experimental results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Reorganizing bus frequencies to cater for the actual travel demands can significantly save the cost of the public transport system. Many, if not all, previous studies formulate this as a bus frequency optimization problem that tries to minimize passengers’ average waiting time. On the other hand, many investigations have confirmed that the user satisfaction drops faster as the waiting time increases. Consequently, this paper studies the bus frequency optimization problem considering the user satisfaction. Specifically, for the first time to our best knowledge, we study how to schedule the buses such that the total number of passengers who could receive their bus services within the waiting time threshold can be maximized. We propose two variants of the problem, FAST and FASTCO, to cater for different application needs and prove that both are NP-hard. To solve FAST effectively and efficiently, we first present an index-based $(1-1/e)$ (1 - 1 / e) -approximation algorithm. By exploiting the locality property of routes in a bus network, we further propose a partition-based greedy method that achieves a $(1-\rho)(1-1/e)$ (1 - ρ) (1 - 1 / e) approximation ratio. Then we propose a progressive partition-based greedy method to further boost the efficiency while achieving a $(1-\rho)(1-1/e-\varepsilon)$ (1 - ρ) (1 - 1 / e - ɛ) approximation ratio. For the FASTCO problem, two greedy-based heuristic methods are proposed. Experiments on a real city-wide bus dataset in Singapore have been conducted to verify the efficiency, effectiveness, and scalability of our methods in addressing FAST and FASTCO respectively. [ABSTRACT FROM AUTHOR]

This paper tackles the classical problem of transmission rate allocation in satellite networks where fading may negatively impact communications. Within the framework of multiobjective programming (MOP), this paper introduces a transmission rate allocation criterion among Earth stations (ESs) called L_p-problem-based rate allocation (L_{p}RA). The allocations provided by L_{p}RA are representative of a compromise among the need of different performance metrics such as packet loss and transmission power (TP). This paper determines the condition for the existence and the value of a LpRA transmission rate allocation bound R^{\mathrm{bound}}, to which the transmission rate globally allocated by LpRA converges when the overall available transmission rate R\mathrm{TOT} tends to infinity. The performance analysis, which is carried out through simulations under different satellite channel conditions, is aimed at investigating LpRA features, at showing the existence of the rate bound and the advantages concerning the rate allocation given by using LpRA, and at comparing LpRA with two other schemes in the literature concerning allocated rate, packet loss rate, transmit power, and execution time. [ABSTRACT FROM AUTHOR]