General Instances Research Articles

Unit-length Rectangular Drawings of Graphs

A rectangular drawing of a planar graph $G$ is a planar drawing of $G$ in which vertices are mapped to grid points, edges are mapped to horizontal and vertical straight-line segments, and faces are drawn as rectangles. Sometimes this latter constraint is relaxed for the outer face. In this paper, we study rectangular drawings in which the edges have unit length. We show a complexity dichotomy for the problem of deciding the existence of a unit-length rectangular drawing, depending on whether the outer face must also be drawn as a rectangle or not. Specifically, we prove that the problem is NP-complete for biconnected graphs when the drawing of the outer face is not required to be a rectangle, even if the sought drawing must respect a given combinatorial embedding, whereas it is polynomial-time solvable, both in the fixed and the variable embedding settings, if the outer face is required to be drawn as a rectangle. Furthermore, we provide a linear-time algorithm for deciding whether a plane graph admits an embedding-preserving unit-length rectangular drawing if the drawing of the outer face is prescribed. As a by-product of our research, we provide the first polynomial-time algorithm to test whether a planar graph $G$ admits a rectangular drawing, for general instances of maximum degree $4$.

Nonlinear Optimization and Adaptive Heuristics for Solving Irregular Object Packing Problems

We review and present several challenging model classes arising in the context of finding optimized object packings (OP). Except for the smallest and/or simplest general OP model instances, it is not possible to find their exact (closed-form) solution. Most OP problem instances become increasingly difficult to handle even numerically, as the number of packed objects increases. Specifically, here we consider classes of general OP problems that can be formulated in the framework of nonlinear optimization. Research experience demonstrates that—in addition to utilizing general-purpose nonlinear optimization solver engines—the insightful exploitation of problem-specific heuristics can improve the quality of numerical solutions. We discuss scalable OP problem classes aimed at packing general circles, spheres, ellipses, and ovals, with numerical (conjectured) solutions of non-trivial model instances. In addition to their practical relevance, these models and their various extensions can also serve as constrained global optimization test challenges.

An adapted savings algorithm for planning heterogeneous logistics with uncrewed aerial vehicles

This paper proposes a new extension to the Sustainable Specimen Collection Problem (SSCP), where medical specimens are transported by vans, bikes, and uncrewed aerial vehicles (UAVs, or drones) from local medical practices/offices to a central hospital laboratory for analysis, employing a two-echelon collection approach. Time restrictions from existing operations and literature are also introduced, with the study being formulated as a weighted multi-objective problem seeking to minimise (i) operating costs; (ii) transit times; and (iii) energy/environmental impacts. A new adaptation of the Clarke and Wright Savings Algorithm is subsequently presented to create collection rounds that leverage each mode’s strengths. Subsequently, routes are compiled into workable fixed shifts using a modified bin-packing algorithm in each iteration.The approach of this study is based on a case study of the UK’s National Health Service (NHS), involving the collection of pathology samples using traditional vans operating within fixed time slots. Using case study data from the Solent region (England), a novel test instance generation methodology was also developed, whereby realistic site positioning and origin-destination travel data are captured to enable effective algorithm experimentation. The findings from applying the proposed algorithm to a set of test instances based on this methodology are subsequently discussed, where it was found that the adapted savings and bin-packing approach produced effective solutions quickly, with 90% of all large instances (200 sites) being solved within 15 min. Further algorithm developments and the application of the devised problem/methodologies are also discussed.

A hierarchical heterogeneous ant colony optimization based oversampling algorithm using feature similarity for classification of imbalanced data

Imbalanced data classification is one of the challenging problems in machine learning. Oversampling is a promising technique that generates synthetic minority instances to balance the dataset. Inappropriate minority instances generated may deteriorate the performance of the classifier. Majority of the oversampling algorithms create new minority instances by choosing nearest neighbors for random interpolation. However, these methods do not provide new information to the dataset and therefore standard classifiers do not show good performance on such datasets. Therefore, it is necessary to generate diverse minority class instances to increase the performance of the classifier. Since, every feature of each minority class instance contribute valuable information, generating synthetic instances from the features of all minority instances would produce diverse minority instances, thereby increasing the performance of the classifier. This paper proposes a Hierarchical Heterogeneous Ant Colony Optimization based oversampling algorithm using Feature Similarity (HHACO-FSOTe) for generation of synthetic minority instances. Instead of choosing few neighbors for interpolation, the proposal considers all minority instances for generation of synthetic instances. HHACO-FSOTe generates new feature values by computing the minimum absolute difference between the features of a given minority instance and the corresponding features of the remaining minority instances. The features in the dataset are distributed among the ant agents enabling parallelism, thereby reducing the time taken for oversampling. HHACO-FSOTe do not require parameter tuning or training. The proposal is evaluated on 41 low dimensional, 11 high dimensional and 8 noisy datasets. Experiments reveal that HHACO-FSOTe is competent with the state-of-art oversampling techniques. Results were validated using non-parametric statistical tests.

Dynamic rebalancing optimization for bike-sharing systems: A modeling framework and empirical comparison

Station-based Bike-sharing systems have been implemented in multiple major cities, offering a low-cost and environmentally friendly transportation alternative. As a remedy to unbalanced stations, operators typically rebalance bikes by trucks. The resulting dynamic planning has received significant attention from the Operations Research community. Due to its modeling flexibility, mixed-integer programming remains a popular choice. However, the complex planning problem requires significant simplifications to obtain a computationally tractable model. As a result, existing models have used a large variety of modeling assumptions and techniques regarding decision variables and constraints. Unfortunately, the impact of such assumptions on the solutions’ performance in practice remains generally unexplored.In this paper, we first systematically survey the literature on rebalancing problems and their modeling assumptions. We then propose a general mixed-integer programming model for multi-period rebalancing problems that can be easily adapted to different assumptions, including trip modeling, time discretization, trip distribution, and event sequences. We develop an instance generator to synthesize realistic station networks and customer trips, as well as a realistic fine-grained simulator to evaluate the operational performance of rebalancing strategies. Finally, extensive numerical experiments are carried out, both on the synthetic and real-world data, to analyze the effectiveness of various modeling assumptions and techniques. Based on our results, we identify the assumptions that empirically provide the most effective rebalancing strategies in practice. Specifically, a set of specific trip distribution constraints and event sequences ignored in the previous literature seem to provide particularly good results.

The Continuous Time-Resource Trade-off Scheduling Problem with Time Windows

We introduce a variant of the cumulative scheduling problem (CuSP) characterized by continuous modes, time windows, and a criterion that involves safety margin maximization. The study of this variant is motivated by the Geospatial based Environment for Optimisation Systems Addressing Fire Emergencies Horizon 2020 Project, which is devoted to the design of evacuation plans in the face of natural disasters and more specifically, wildfire. People and goods have to be transferred from endangered places to safe places, and evacuation planning consists of scheduling evacuee moves along precomputed paths under arc capacities and deadlines. The resulting model is relevant in other contexts, such as project or industrial process scheduling. We consider here several formulations of the continuous time-resource trade-off scheduling problem (CTRTP-TW) with a safety maximization objective. We establish a complete complexity characterization distinguishing polynomial and NP-hard special cases depending on key parameters. We show that the problem with fixed sequencing (i.e., with predetermined overlap or precedence relations between activities) is convex. We then show that the preemptive variant is polynomial, and we propose lower and upper bounds based on this relaxation. A flow-based mixed-integer linear programming formulation is presented, from which a branch-and-cut exact method and an insertion heuristic are derived. An exact dedicated branch-and-bound algorithm is also designed. Extensive computational experiments are carried out to compare the different approaches on evacuation planning instances and on general CTRTP-TW instances. The experiments also show the interest of the continuous model compared with a previously proposed discrete approximation. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This work was funded by the Horizon 2020 Marie Skłodowska-Curie Research and Innovation Staff Exchange European Project 691161 GEO-SAFE (Geospatial based Environment for Optimisation Systems Addressing Fire Emergencie). This work has also been supported by ANITI, the Artificial and Natural Intelligence Toulouse Institute. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0142 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0142 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

GenArchBench: A genomics benchmark suite for arm HPC processors

Arm usage has substantially grown in the High-Performance Computing (HPC) community. Japanese supercomputer Fugaku, powered by Arm-based A64FX processors, held the top position on the Top500 list between June 2020 and June 2022, currently sitting in the fourth position. The recently released 7th generation of Amazon EC2 instances for compute-intensive workloads (C7 g) is also powered by Arm Graviton3 processors. Projects like European Mont-Blanc and U.S. DOE/NNSA Astra are further examples of Arm irruption in HPC. In parallel, over the last decade, the rapid improvement of genomic sequencing technologies and the exponential growth of sequencing data has placed a significant bottleneck on the computational side. While most genomics applications have been thoroughly tested and optimized for x86 systems, just a few are prepared to perform efficiently on Arm machines. Moreover, these applications do not exploit the newly introduced Scalable Vector Extensions (SVE).This paper presents GenArchBench, the first genome analysis benchmark suite targeting Arm architectures. We have selected computationally demanding kernels from the most widely used tools in genome data analysis and ported them to Arm-based A64FX and Graviton3 processors. Overall, the GenArch benchmark suite comprises 13 multi-core kernels from critical stages of widely-used genome analysis pipelines, including base-calling, read mapping, variant calling, and genome assembly. Our benchmark suite includes different input data sets per kernel (small and large), each with a corresponding regression test to verify the correctness of each execution automatically. Moreover, the porting features the usage of the novel Arm SVE instructions, algorithmic and code optimizations, and the exploitation of Arm-optimized libraries. We present the optimizations implemented in each kernel and a detailed performance evaluation and comparison of their performance on four different HPC machines (i.e., A64FX, Graviton3, Intel Xeon Skylake Platinum, and AMD EPYC Rome). Overall, the experimental evaluation shows that Graviton3 outperforms other machines on average. Moreover, we observed that the performance of the A64FX is significantly constrained by its small memory hierarchy and latencies. Additionally, as proof of concept, we study the performance of a production-ready tool that exploits two of the ported and optimized genomic kernels.

The phase transition in random regular exact (3s + s, k)-SAT problem

A CNF formula with each clause of length k and each variable occurring 4s times, where positive occurrences are 3s and negative occurrences are s, is a regular (3s + s, k)-CNF formula (F3s+s,k formula). The random regular exact (3s + s, k)-SAT problem is whether there exists a set of Boolean variable assignments such that exactly one literal is true for each clause in the F3s+s,k formula. By introducing a random instance generation model, the satisfiability phase transition of the solution is analyzed by using the first moment method, the second moment method, and the small subgraph conditioning method, which gives the phase transition point s* of the random regular exact (3s + s, k)-SAT problem for k≥3. When s < s*, F3s+s,k formula is satisfiable with high probability; when s > s*, F3s+s,k formula is unsatisfiable with high probability. Finally, through the experimental verification, the results show that the theoretical proofs are consistent with the experimental results.

Spatio-temporal void fraction visualization in air-water two-phase flow regime transitions by combination of convolutional neural network and long short-term memory implemented into multiple current-voltage (MCV-CNN_LSTM)

Spatio-temporal void fraction in air-water two-phase flow regime transitions has been visualized by combination of convolutional neural network and long short-term memory implemented into multiple -current-voltage (MCV-CNN_LSTM). The MCV-ML is composed of two components, which are MCV-CNN_LSTM training and evaluation. In the first component, four steps are carried out as 1) true void fraction α measurement was conducted by wire-mesh sensor (WMS) as objective variables, 2) simulated MCV voltage U calculation by simulation of MCV measurement for explanatory variable, 3) CNN_LSTM training instance generation, and 4) CNN_LSTM model training and testing. In the second component, two additional steps are experimentally conducted which are 5) actual MCV voltage V measurement by MCV for input of trained ML, and 6) spatio-temporal void fraction α prediction. For the dataset generation, the experiment was conducted on air-water two phase flow regime transitions in vertical pipe with an inner diameter of 25 mm and a length of 350 mm from the elbow. The superficial velocity of liquid and gas phase are varied to present the flow regime transition of bubbly to slug flow. As a result, the estimated spatio-temporal void fraction αˆ by MCV-CNN_LSTM enables the visualization of detailed gas distribution in vertical upward two-phase flow. The spatial averaged instantaneous void fraction measured by WMS and estimated by MCV-CNN_LSTM show qualitative agreement in normalized cross correlation (INCC) of 0.364 on its temporal variation.

Cooperative Knowledge Distillation: A Learner Agnostic Approach

Knowledge distillation is a simple but powerful way to transfer knowledge between a teacher model to a student model. Existing work suffers from at least one of the following key limitations in terms of direction and scope of transfer which restrict its use: all knowledge is transferred from teacher to student regardless of whether or not that knowledge is useful, the student is the only one learning in this exchange, and typically distillation transfers knowledge only from a single teacher to a single student. We formulate a novel form of knowledge distillation in which many models can act as both students and teachers which we call cooperative distillation. The models cooperate as follows: a model (the student) identifies specific deficiencies in it's performance and searches for another model (the teacher) who encodes learned knowledge into instructional virtual instances via counterfactual instance generation. Because different models may have different strengths and weaknesses, all models can act as either students or teachers (cooperation) when appropriate and only distill knowledge in areas specific to their strengths (focus). Since counterfactuals as a paradigm are not tied to any specific algorithm, we can use this method to distill knowledge between learners of different architectures, algorithms, and even feature spaces. We demonstrate our approach not only outperforms baselines such as transfer learning, self-supervised learning, and multiple knowledge distillation algorithms on several datasets, but it can also be used in settings where the aforementioned techniques cannot.

Instance generation tool for on-demand transportation problems

We present REQreate, a tool to generate instances for on-demand transportation problems. Such problems consist of optimizing vehicle routes according to passengers’ demand for transportation under space and time restrictions (called requests). REQreate is flexible and can be configured to generate instances for a variety of problems types in this problem class. In this paper, we exemplify this with the Dial-a-Ride Problem (DARP) and On-demand Bus Routing Problem (ODBRP). In most of the literature, researchers either test their solution algorithms with instances based on artificial networks or they perform real-life case studies on instances derived from a specific city or region. Furthermore, locations of requests for on-demand transportation problems are mostly randomly chosen according to a uniform distribution, rather than being derived from actual data.The aim of REQreate is to overcome any shortcomings from synthetic or specific instances. Rather than relying on artificial or limited data, we retrieve real-world street networks from OpenStreetMaps (OSM). To the best of our knowledge, this is the first tool to make use of real-life networks to generate instances for an extensive catalog of existing and upcoming on-demand transportation problems. Additionally, we present a simple method that can be embedded in the instance generation process to produce distinct urban mobility patterns. We perform an analysis with real-life data sets reported by rideshare companies and compare them with properties of synthetic instances generated with REQreate. Another contribution of this work is the introduction of the concept of instance similarity that serves as support to create a set of diverse instances, in addition to properties (size, dynamism, urgency, and geographic dispersion) that can be used to comprehend which characteristics of the problem instances have an impact on the performance quality (or efficiency) of a solution algorithm.

Code and Data Repository for An Approximate Dynamic Programming Approach to Dynamic Stochastic Matching

This archive is distributed in association with the INFORMS Journal on Computing under the MIT License. The purpose of this repository is to share the source code used in the paper "An Approximate Dynamic Programming Approach to Dynamic Stochastic Matching" by F. You and T. Vossen. The code contains implementation of main model of the paper, benchmark approaches for comparision, numerical instance generation and experimentation.

Ontology learning from object-relational mapping metadata and relational database

Ontologies play an important role in representing the semantics of data sources. Building an ontology as a representation of domain knowledge from available data sources is not a simple process, particularly when dealing with relational data, which remains prevalent in existing knowledge systems. In this study, we create an ontology from a relational database using object-relational mapping (ORM) metadata as additional rules for mapping. Our method comprises two main phases: ontology schema construction using ORM metadata and the generation of ontology instances from the relational database. During the initial phase, we analyzed the ORM metadata to map it to an resource description framework schema (RDF(S))-OWL representation of the ontology. In the subsequent phase, we applied mapping rules to convert the relational database (RDB) data into ontological instances, which are then represented as RDF triples. Using ORM metadata, we enhance the accuracy of the resulting ontology, particularly in terms of extracting concepts and hierarchical relationships. This study contributes to the field of ontology learning by showcasing a novel approach that leverages ORM metadata to create ontologies from relational databases.

Target-aware transformer tracking with hard occlusion instance generation.

Visual tracking is a crucial task in computer vision that has been applied in diverse fields. Recently, transformer architecture has been widely applied in visual tracking and has become a mainstream framework instead of the Siamese structure. Although transformer-based trackers have demonstrated remarkable accuracy in general circumstances, their performance in occluded scenes remains unsatisfactory. This is primarily due to their inability to recognize incomplete target appearance information when the target is occluded. To address this issue, we propose a novel transformer tracking approach referred to as TATT, which integrates a target-aware transformer network and a hard occlusion instance generation module. The target-aware transformer network utilizes an encoder-decoder structure to facilitate interaction between template and search features, extracting target information in the template feature to enhance the unoccluded parts of the target in the search features. It can directly predict the boundary between the target region and the background to generate tracking results. The hard occlusion instance generation module employs multiple image similarity calculation methods to select an image pitch in video sequences that is most similar to the target and generate an occlusion instance mimicking real scenes without adding an extra network. Experiments on five benchmarks, including LaSOT, TrackingNet, Got10k, OTB100, and UAV123, demonstrate that our tracker achieves promising performance while running at approximately 41 fps on GPU. Specifically, our tracker achieves the highest AUC scores of 65.5 and 61.2% in partial and full occlusion evaluations on LaSOT, respectively.

Routing and Wavelength Assignment for Multiple Multicasts in Optical Network-on-Chip (ONoC)

Optical Network-on-Chip (ONoC) is an emerging chip-scale optical interconnection technology to realize high-performance and power-efficient inter-core communication for many-core processors. Multicast communication is popularly used in parallel applications on chip. However, existing researches for multicast in ONoC mainly focus on the optimization of one multicast. This limits the practical applications of the research outcomes because we often face the dynamic formation of multiple multicast groups in real network systems. In this paper, we define the problem of routing and wavelength assignment for multiple multicasts in ONoC with the objective of minimizing the number of wavelengths required. To solve the problem, we first formulate it as an integer programming model for general topologies. Then we design routing policies for special instances that optimally use only one wavelength on mesh topology. For general instances, we design a Group-Partitioning Routing algorithm for Multiple Multicasts (GPRMM). GPRMM decouples a group of multicasts into a number of sub-groups, each of which matching one of the special instances. Theoretical results show that the number of wavelengths required by GPRMM is no more than the Destination Density σd, i.e., the maximum number of multicasts with destinations in the same row or column. Moreover, we find the upper bound and the lower bound on the number of wavelengths required for GPRMM. The wavelength requirement is also upper bounded by the network size n for an n×n mesh network. Simulation results show that GPRMM can reduce the number of wavelengths by 26.7% compared with previous methods. GPRMM has the advantages of low routing complexity, low wavelength requirement, low power consumption, and good scalability.

Verifying new instances of the multidemand multidimensional knapsack problem with instance space analysis

The optimization literature contains numerous studies defining a problem and showing a state-of-the-art implementation of a solution method. These studies may involve an empirical evaluation of the solution methods but assume the current collection of test instances is sufficient. This paper is concerned with instance generation methods for the multidemand multidimensional knapsack problem. We use instance space analysis to characterize the landscape of existing instances and validate the novelty of new instances. We discuss gaps present within the current instance space landscape and fill them with three new sets of instances. We find feasibility issues within instance generation methods and address them through a primal problem instance generator (PPIG). The instance generator is capable of producing feasible and diverse instances by directly controlling the problem features. PPIG contributes to the previous collections of instances and is validated through instance space analysis.

A Diversity-Based Synthetic Oversampling Using Clustering for Handling Extreme Imbalance

Imbalanced data are typically observed in many real-life classification problems. However, mainstream machine learning algorithms are mostly designed with the underlying assumption of a relatively well-balanced distribution of classes. The mismatch between reality and algorithm assumption results in a deterioration of classification performance. One form of approach to address this problem is through re-sampling methods, although its effectiveness is limited; most re-sampling methods fail to consider the distribution of minority and majority instances and the diversity within synthetically generated data. Diversity becomes increasingly important when minority data becomes more sparse, as each data point becomes more valuable. They should all be considered during the generation process instead of being regarded as noise. In this paper, we propose a cluster-based diversity re-sampling method, combined with NOAH algorithm. Neighbourhood-based Clustering Diversity Over-sampling (NBCDO) is introduced with the aim to complement our previous cluster-based diversity algorithm Density-based Clustering Diversity Over-sampling (DBCDO). It first uses a neighbourhood-based clustering algorithm to consider the distribution of both minority and majority class instances, before applying NOAH algorithm to encourage diversity optimisation during the generation of synthetic instances. We demonstrate the implementation of both cluster-based diversity methods by conducting experiments over 10 real-life datasets with ≤ 5% imbalance ratio and show that our proposed cluster-based diversity algorithm (NBCDO, DBCDO) brings performance improvements over its comparable methods (DB-SMOTE, MAHAKIL, KMEANS-SMOTE, MC-SMOTE).

Generating linear programming instances with controllable rank and condition number

Instance generation is crucial for linear programming algorithms, especially for the evaluation and verification of corresponding methods. This study proposes a general framework for designing linear programming instances based on the preset optimal solution. First, we give a constraint matrix generation method with controllable condition number and rank from the perspective of matrix decomposition. Based on the preset optimal solution, a bounded feasible linear programming instance is generated with the right-hand side and objective coefficients satisfying the primal feasibility and dual feasibility. In addition, we provide three neighborhood exchange operators and prove that instances generated under this method can fill the entire space of feasible bounded linear programming instances. We experimentally validate that the proposed schedule generates more controllable linear programming instances in rank and condition number, while neighborhood exchange operators construct more complex instances.

Approximately EFX allocations for indivisible chores

In this paper, we study how to fairly allocate a set of m indivisible chores to a group of n agents, each of which has a general additive cost function on the items. Since envy-free (EF) allocations are not guaranteed to exist, we consider the notion of envy-freeness up to any item (EFX). In contrast to the fruitful results regarding the (approximation of) EFX allocations for goods, very little is known for the allocation of chores. Prior to our work, for the allocation of chores, it is known that EFX allocations always exist for two agents or general number of agents with identical ordering cost functions. For general instances, no non-trivial approximation result regarding EFX allocation is known. In this paper, we make progress in this direction by providing several polynomial time algorithms for the computation of EFX and approximately EFX allocations. We show that for three agents we can always compute a (2+6)-approximation of EFX allocation. For n≥4 agents, our algorithm always computes a (3n2−n)-approximation. We also study the bi-valued instances, in which agents have at most two cost values on the chores. For three agents, we provide an algorithm for the computation of EFX allocations. For n≥4 agents, we present algorithms for the computation of partial EFX allocations with at most (n−1) unallocated items; and (n−1)-approximation of EFX allocations.

Evolutionary Approaches for Adversarial Attacks on Neural Source Code Classifiers

As the prevalence and sophistication of cyber threats continue to increase, the development of robust vulnerability detection techniques becomes paramount in ensuring the security of computer systems. Neural models have demonstrated significant potential in identifying vulnerabilities; however, they are not immune to adversarial attacks. This paper presents a set of evolutionary techniques for generating adversarial instances to enhance the resilience of neural models used for vulnerability detection. The proposed approaches leverage an evolution strategy (ES) algorithm that utilizes as the fitness function the output of the neural network to deceive. By starting from existing instances, the algorithm evolves individuals, represented by source code snippets, by applying semantic-preserving transformations, while utilizing the fitness to invert their original classification. This iterative process facilitates the generation of adversarial instances that can mislead the vulnerability detection models while maintaining the original behavior of the source code. The significance of this research lies in its contribution to the field of cybersecurity by addressing the need for enhanced resilience against adversarial attacks in vulnerability detection models. The evolutionary approach provides a systematic framework for generating adversarial instances, allowing for the identification and mitigation of weaknesses in AI classifiers.