Depth-first Research Articles

Adaptive Visual Information Gathering for Autonomous Exploration of Underwater Environments

This work presents the development and field testing of a novel adaptive visual information gathering (AVIG) framework for autonomous exploration of benthic environments using AUVs. The objective is to adapt dynamically the robot exploration using the visual information gathered online . This framework is based on a novel decision-time adaptive replanning (DAR) behavior that works together with a sparse Gaussian process (SGP) for environmental modeling and a Convolutional Neural Network (CNN) for semantic image segmentation. The framework is executed in mission time. The SGP uses semantic data obtained from stereo images to probabilistically model the spatial distribution of certain species of seagrass that colonize the sea bottom forming widespread meadows. The uncertainty of the probabilistic model provides a measure of sampling informativeness to the DAR behavior. The DAR behavior has been designed to execute successive informative paths, without stopping, considering the newest information obtained from the SGP. We solve the information path planning (IPP) problem by means of a novel depth-first (DF) version of the Monte Carlo tree search (MCTS). The DF-MCTS method has been designed to explore the state-space in a depth-first fashion, provide solution paths of a given length in an anytime manner, and reward smooth paths for field realization with non-holonomic robots. The complete framework has been integrated in a ROS environment as a high level layer of the AUV software architecture. A set of simulations and field testing show the effectiveness of the framework to gather data in P. oceanica environments.

A Depth-First Iterative Algorithm for the Conjugate Pair Fast Fourier Transform

The Split-Radix Fast Fourier Transform has the same low arithmetic complexity as the related Conjugate Pair Fast Fourier Transform. Both transforms have an irregular datapath structure which is straightforwardly expressed only in recursive forms. Furthermore, the conjugate pair variant has a complicated input indexing pattern which requires existing iterative implementations to rely on precomputed tables. It however allows optimization of the memory bandwidth as it requires a single twiddle factor load per radix-4 butterfly. In existing algorithms, this comes at the cost of using additional precomputed tables or performing recursive function calls. In this paper we present two novel approaches that handle both the butterfly scheduling and the input index generation of the Conjugate Pair Fast Fourier Transform. The proposed algorithm is cache-friendly because it is depth-first, non-recursive and does not rely on precomputed index tables. In order to achieve this, we relate the butterfly execution pattern of the Split-Radix and Conjugate Pair FFTs to the binary carry sequence. Based on this finding, we describe how common integer arithmetic and bitwise operations can be used to perform input reordering and depth-first traversal of the transform datapath with O(1) space complexity.

Pizza Dough Service Provider at Various Area Followed by Depth-First Search Algorithm

Pizza Dough Service Provider at Various Area Followed by Depth-First Search Algorithm

Low-Cost Multi-Agent Navigation via Reinforcement Learning With Multi-Fidelity Simulator

In recent years, reinforcement learning (RL) has been widely used to solve multi-agent navigation tasks, and a high-fidelity level for the simulator is critical to narrow the gap between simulation and real-world tasks. However, high-fidelity simulators have high sampling costs and bottleneck the training model-free RL algorithms. Hence, we propose a Multi-Fidelity Simulator framework to train Multi-Agent Reinforcement Learning (MFS-MARL), reducing the total data cost with samples generated by a low-fidelity simulator. We apply the depth-first search to obtain local feasible policies on the low-fidelity simulator as expert policies to help the original reinforcement learning algorithm explore. We built a multi-vehicle simulator with variable fidelity levels to test the proposed method and compared it with the vanilla Soft Actor-Critic (SAC) and expert actor methods. The results show that our method can effectively obtain local feasible policies and can achieve a 23% cost reduction in multi-agent navigation tasks.

On the Construction of Multitype Quasi-Cyclic Low-Density Parity-Check Codes With Different Girth and Length

Multitype quasi-cyclic (QC) low-density parity-check (LDPC) codes are a class of protograph LDPC codes lifted cyclically from protographs with multiple edges, represented by two weight and slope matrices. For a given weight-matrix, an approach is proposed to find the maximum-achievable girth $g_{\max }$ of the corresponding multitype QC-LDPC codes by some inevitable chains having less complexity than the existing methods. This advantage leads to some new patterns of the weight matrices such that the corresponding codes have some improvements in terms of the maximum-achievable girths or the minimum-distance upper-bounds. In continue, for a given weight-matrix with maximum-achievable girth $g_{\max }$ , some slope-matrices are constructed by a depth-first search algorithm for which the corresponding multitype QC-LDPC codes with even girth $g$ , $g\le g_{\max }$ , have smaller lengths, higher rates, or larger minimum-distances than the state-of-the-art achievements. Simulation results show that the constructed codes have some error-rate advantages than PEG, random-like, CCSDS, and 802.11n/ac IEEE standard LDPC codes.

Интеграция методов поиска в ширину и логического вывода для удовлетворения табличных ограничений

Within the Constraint Programming technology, so-called table constraints such as typical tables, compressed tables, smart tables, segmented tables, etc, are widely used. They can be used to represent any other types of constraints, and algorithms of the table constraint propagation (logical inference on constraints) allow eliminating a lot of "redundant" values from the domains of variables, while having low computational complexity. In the previous studies, the author proposed to divide smart tables into structures of C- and D-types. The generally accepted methodology for solving constraint satisfaction problems is the combined application of constraint propagation methods and backtracking depth-first search methods. In the study, it is proposed to integrate breadth-first search methods and author`s method of table constraint propagation. D-type smart tables are proposed to be represented as a join of several orthogonalized C-type smart tables. The search step is to select a pair of C-type smart tables to be joined and then propagate the restrictions. To determine the order of joining orthogonalized smart tables at each step of the search, a specialized heuristic is used, which reduces the search space, taking into account further calculations. When the restrictions are extended, the acceleration of the computation process is achieved by applying the developed reduction rules for the case of C-type smart tables. The developed hybrid method allows one to find all solutions to the problems of satisfying constraints modeled using one or several D-type smart tables, without decomposing tabular constraints into elementary tuples.

Method of context-dependent assistance for software user solving an applied task

When using software to solve the applied tasks, the problem to implement the action in need by means of user interface arises. Partly, this problem is solved by studying reference manuals and consultations with the application developers. However, reference manuals are structured in the context of overall application functionality while difficulties arise in the data state context of the task being solved. Consultations lack this flaw, but they are costly and not always available in time. This fact stimulated the development of computer-based methods of contextual help. Yet the task of development of recommendations for performing the requested operation from the current state of the application data has not been solved so far. The research is aimed to reduce the time to get help by developing a knowledge representation model that determines user actions across the application data context, and a method for deriving model based recommendations. The model of the user action scenario is presented in the form of a colored Petri net. This decision content is based on the analogy between user action scenarios and workflow scenarios, for which Petri nets notation has been successfully used for years. For the topological analysis of the Petri net, the strategy of exhaustive depth-first search was applied. The method of the contextual recommendations is proposed to execute the operation requested by the user based on a scenario model in the form of a colored Petri net. The method novelty is application of topological analysis of the Petri net to construct a set of alternative scenarios for performing the operation, followed by filtering alternatives in the process of stepwise execution of the recommended actions. The suggested method provides context-dependent assistance in just one click. When using traditional reference manuals and files for the purpose, the number of clicks is determined by the number of options available to perform the operation.

A Fast Graph Program for Computing Minimum Spanning Trees

When using graph transformation rules to implement graph algorithms, a challenge is to match the efficiency of programs in conventional languages. To help overcome that challenge, the graph programming language GP 2 features rooted rules which, under mild conditions, can match in constant time on bounded degree graphs. In this paper, we present an efficient GP 2 program for computing minimum spanning trees. We provide empirical performance results as evidence for the program's subquadratic complexity on bounded degree graphs. This is achieved using depth-first search as well as rooted graph transformation. The program is based on Boruvka's algorithm for minimum spanning trees. Our performance results show that the program's time complexity is consistent with that of classical implementations of Boruvka's algorithm, namely O(m log n), where m is the number of edges and n the number of nodes.

Nation-Wide Viral Sequence Analysis of HIV-1 Subtype B Epidemic in 2003-2012 Revealed a Contribution of Men Who Have Sex With Men to the Transmission Cluster Formation and Growth in Japan.

Background: To better understand the epidemiology of human immunodeficiency virus type 1 (HIV-1) subtype B transmission in Japan, phylodynamic analysis of viral pol sequences was conducted on individuals newly diagnosed as HIV-1 seropositive.Methodology: A total of 5,018 patients newly diagnosed with HIV-1 infection and registered in the Japanese Drug Resistance HIV Surveillance Network from 2003 to 2012 were enrolled in the analysis. Using the protease-reverse transcriptase nucleotide sequences, their subtypes were determined, and phylogenetic relationships among subtype B sequences were inferred using three different methods: distance-matrix, maximum likelihood, and Bayesian Markov chain Monte Carlo. Domestically spread transmission clusters (dTCs) were identified based on the following criteria: >95% in interior branch test, >95% in Bayesian posterior probability and <10% in depth-first searches for sub-tree partitions. The association between dTC affiliation and individuals' demographics was analyzed using univariate and multivariate analyses.Results: Among the cases enrolled in the analysis, 4,398 (87.6%) were classified as subtype B. Many of them were Japanese men who had sex with men (MSM), and 3,708 (84.3%) belonged to any of 312 dTCs. Among these dTCs, 243 (77.9%) were small clusters with <10 individuals, and the largest cluster consisted of 256 individuals. Most dTCs had median time of the most recent common ancestor between 1995 and 2005, suggesting that subtype B infection was spread among MSMs in the second half of the 1990s. Interestingly, many dTCs occurred within geographical regions. Comparing with singleton cases, TCs included more MSM, young person, and individuals with high CD4+ T-cell count at the first consultation. Furthermore, dTC size was significantly correlated with gender, age, transmission risks, recent diagnosis and relative population size of the region mainly distributed.Conclusions: Our study clarified that major key population of HIV-1 subtype B epidemic in Japan is local MSM groups. The study suggests that HIV-1 subtype B spread via episodic introductions into the local MSM groups, some of the viruses spread to multiple regions. Many cases in dTC were diagnosed during the early phase of infection, suggesting their awareness to HIV risks.

Graph Reachability on Parallel Many-Core Architectures

Many modern applications are modeled using graphs of some kind. Given a graph, reachability, that is, discovering whether there is a path between two given nodes, is a fundamental problem as well as one of the most important steps of many other algorithms. The rapid accumulation of very large graphs (up to tens of millions of vertices and edges) from a diversity of disciplines demand efficient and scalable solutions to the reachability problem. General-purpose computing has been successfully used on Graphics Processing Units (GPUs) to parallelize algorithms that present a high degree of regularity. In this paper, we extend the applicability of GPU processing to graph-based manipulation, by re-designing a simple but efficient state-of-the-art graph-labeling method, namely the GRAIL (Graph Reachability Indexing via RAndomized Interval) algorithm, to many-core CUDA-based GPUs. This algorithm firstly generates a label for each vertex of the graph, then it exploits these labels to answer reachability queries. Unfortunately, the original algorithm executes a sequence of depth-first visits which are intrinsically recursive and cannot be efficiently implemented on parallel systems. For that reason, we design an alternative approach in which a sequence of breadth-first visits substitute the original depth-first traversal to generate the labeling, and in which a high number of concurrent visits is exploited during query evaluation. The paper describes our strategy to re-design these steps, the difficulties we encountered to implement them, and the solutions adopted to overcome the main inefficiencies. To prove the validity of our approach, we compare (in terms of time and memory requirements) our GPU-based approach with the original sequential CPU-based tool. Finally, we report some hints on how to conduct further research in the area.

Low-Complexity Transmit Antenna Selection for Large-Scale Spatial Modulation

A novel low-complexity transmit antenna selection (TAS) scheme is proposed for spatial modulation (SM) multiple-input multiple-output (MIMO) systems towards large-scale antennas. In contrast to the conventional optimized Euclidean distance antenna selection (EDAS) scheme, the proposed scheme is capable of reducing the search complexity by efficiently searching the candidate antenna subsets assisted by depth-first search. Our performance results demonstrate that the developed scheme can substantially reduce the search complexity in the context of large-scale SM-MIMO configuration, while achieving comparable bit error rate (BER) performance to the optimal EDAS scheme.

Destination-Aware Task Assignment in Spatial Crowdsourcing: A Worker Decomposition Approach

With the proliferation of GPS-enabled smart devices and increased availability of wireless network, spatial crowdsourcing (SC) has been recently proposed as a framework to automatically request workers (i.e., smart device carriers) to perform location-sensitive tasks (e.g., taking scenic photos, reporting events). In this paper, we study a destination-aware task assignment problem that concerns the optimal strategy of assigning each task to proper worker such that the total number of completed tasks can be maximized whilst all workers can reach their destinations before deadlines after performing assigned tasks. Finding the global optimal assignment turns out to be an intractable problem since it does not imply optimal assignment for individual worker. Observing that the task assignment dependency only exists amongst subsets of workers, we utilize tree-decomposition technique to separate workers into independent clusters and develop an efficient depth-first search algorithm with progressive bounds to prune non-promising assignments. In order to make our proposed framework applicable to more scenarios, we further optimize the original framework by proposing strategies to reduce the overall travel cost and allow each task to be assigned to multiple workers. Extensive empirical studies verify that the proposed technique and optimization strategies perform effectively and settle the problem nicely.

Low‐complexity block tree image coder for visual sensor networks

The wavelet block tree coding (WBTC) algorithm is an efficient wavelet-based image coder. In this coder, multiple spatial orientation trees are combined together to make a single block tree. It uses three ordered lists to keep track of significant/insignificant coefficients and sets while coding, which increases its memory requirement as well as computational complexity. Also, it uses memory inefficient conventional discrete wavelet transform (DWT) to compute the transformed coefficients. In this study, a Low-Complexity Block Tree Coding (LCBTC) algorithm that uses two state-tables and two very small lists, is proposed. Similar to WBTC, it also uses sorting and refinement passes in each bit-plane. However, it encodes the coefficients in block-tree manner using depth-first search approach to reduce the computational complexity. It uses DWT coefficients obtained from modified fractional wavelet filter (MFrWF) rather than conventional DWT, which further reduces the overall memory and complexity of the image coder. The simulation results show that the memory requirement and computational complexity of LCBTC is much less than WBTC and other state-of-the-art coding algorithms. These features make the image coder a better candidate for compression in memory constrained and real-time visual sensor networks.

Optimization of mine ventilation network feature graph.

A ventilation network feature graph can directly and quantitatively represent the features of a ventilation network. To ensure the stability of airflow in a mine and improve ventilation system analysis, we propose a new algorithm to draw ventilation network feature graphs. The independent path method serves as the algorithm's main frame, and an improved adaptive genetic algorithm is embedded so that the graph may be drawn better. A mathematical model based on the node adjacency matrix method for unidirectional circuit discrimination is constructed as the drawing algorithm may not be valid in such cases. By modifying the edge-seeking strategy, the improved depth-first search algorithm can be used to determine all of the paths in the ventilation network with unidirectional circuits, and the equivalent transformation method of network topology relations is used to draw the ventilation network feature graph. Through the analysis of the topological relation of a ventilation network, a simplified mathematical model is constructed, and network simplification technology makes the drawing concise and hierarchical. The rapid and intuitive drawing of the ventilation network feature graphs is significant for optimization of the ventilation system and day-to-day management.

Computing the Number of Loop-Free k-hop Paths of Networks

Computing the number of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -hop paths is crucial for selecting services in social networks and analyzing graph data, for example, a service consumer require to evaluate the trustworthiness of a service provider along the social trust paths from a service consumer to the service provider, there are usually many social trust paths between two unconnected participants, people need to know the number of loop-free <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -hop trust propogation paths; other applications include the similarity computation for services recommendation, information diffusion, etc. Previously, the number of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -hop paths is roughly estimated by the elements in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> multiplications of the network adjacency matrix. This method calculates much more <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -hop paths than those actually exist, due to many paths with loops counted as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -hop paths, which may result in obvious errors in applications. Based on the idea of loops removing, accurate mathematical formulas for counting loop-free paths are obtained in this article for paths with five or less hops, an approximate method is provided for larger hops. Based on the proposed loop removing algorithm (LRA), the typical method for predicting trust between any two people in social networks is improved, the error rate is dramatically reduced; the traditional path based similarity indices are improved, which are much accurate than their antecedent counterparts; and a method for computing the spreading probability for information spreading between two unconnected vertices in the famous independent cascade (IC) model is also obtained. To reveal the effectiveness of the proposed LRA, this article also provide a traversal depth-first search algorithm (DFSA) for finding the true number of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -hop loop-free paths.

Reliability assessment of a stochastic air transport network with late arrivals

With the rapid development of low-fare airlines with cheaper fares, the demand for safe and fast air-travel is growing. However, weather shocks, unpredictable situations, or mismatched interactions between related players sometimes impair the on-time landing of air transport networks. This work considers late arrivals and assesses the reliability of an air transport network and its performance indicator in terms of meeting the requested number of passengers under the time constraint. The available number of seats in each flight is regarded as stochastic owing to the number of seats that have been reserved by both travel agents and individual customers. An air transport network is thus formulated as a stochastic-flow air transport network with late arrivals, and all minimal itineraries feasible under the constraint of time are found through a depth-first search based approach. The reliability is derived from all flow vectors such that its capacity and tardiness-related valid conditions are not bounded while fulfilling the given number of passengers. Evaluating the reliability with considering late arrivals implies handling the rapid increase in flights for each tardiness level and facing the influence on the validity of each minimal itinerary. This study also contributes towards potential improvements in the reliability of air transport networks, which can benefit travel agents.

Determination of optimal breakpoint set of overcurrent relays using modified depth‐first search and mixed‐integer linear programming

In the process of determination of breakpoints for overcurrent relays (OCR), different sets with equal number of OCRs can be selected as minimum breakpoint set (MBPS). Therefore, determination of the most appropriate MBPS is an issue which must be considered. This paper proposes a prioritization between different MBPSs based on the sum of operating times of OCRs. In this case, a set which results the minimum sum of operating times is selected as the appropriate MBPS. For this purpose, the proposed method combines two problems: determination of MBPS and coordination of OCRs, and is expressed in mixed-integer linear programming (MILP) form. Also, a modified depth-first search (DFS) algorithm is applied to determine the OCRs contained loops of studied networks. It is shown that the proposed method has the capability to be combined with previous defined expert rules in this field in order to consider the network conditions in the process of determination of optimal breakpoint set (OBPS). The proposed method has been implemented on various size networks, and the results show the effectiveness of proposed method in determining breakpoint set with the least number in first priority and minimum operating times of relays in second priority.

Speeding up distributed pseudo-tree optimization procedures with cross edge consistency to solve DCOPs

The Distributed Pseudo-tree Optimization Procedure (DPOP) is a well-known message passing algorithm that provides optimal solutions to Distributed Constraint Optimization Problems (DCOPs) in cooperative multi-agent systems. However, the traditional DCOP formulation does not consider constraints that must be satisfied (hard constraints), rather it concentrates only on constraints that place no restriction on satisfaction (soft constraints). This is a serious shortcoming as many real-world applications involve both types of constraints. Traditional DPOP algorithms are not able to benefit from the existence of hard constraints, where an additional calculation is required to handle such constraints. This results in longer runtimes. Thus scalability remains an issue. Additionally, in the standard DPOP, the agents are arranged as a Depth First Search (DFS) pseudo-tree, but recent work has shown that the construction of pseudo-trees in this way often leads to chain-like communication structures that greatly impair the algorithm’s performance. To address these issues, we develop an algorithm that speeds up the DPOP algorithm by reducing the size of the messages exchanged and increases parallelism in the pseudo tree. For this purpose, initially, we improve the path for exchanging messages. Next, we introduce a new form of constraint propagation, which we call cross-edge consistency. Our theoretical evaluation shows that our proposed algorithm is complete and correct. In empirical evaluations, our algorithm achieves a significant reduction in the runtime, ranging from 4% to 96%, compared to the state-of-the-art.

Conflict-Driven Heuristics for Mixed Integer Programming

Two essential ingredients of modern mixed-integer programming solvers are diving heuristics, which simulate a partial depth-first search in a branch-and-bound tree, and conflict analysis, which learns valid constraints from infeasible subproblems. So far, these techniques have mostly been studied independently: primal heuristics for finding high-quality feasible solutions early during the solving process and conflict analysis for fathoming nodes of the search tree and improving the dual bound. In this paper, we pose the question of whether and how the orthogonal goals of proving infeasibility and generating improving solutions can be pursued in a combined manner such that a state-of-the-art solver can benefit. To do so, we integrate both concepts in two different ways. First, we develop a diving heuristic that simultaneously targets the generation of valid conflict constraints from the Farkas dual and the generation of improving solutions. We show that, in the primal, this is equivalent to the optimistic strategy of diving toward the best bound with respect to the objective function. Second, we use information derived from conflict analysis to enhance the search of a diving heuristic akin to classic coefficient diving. In a detailed computational study, both methods are evaluated on the basis of an implementation in the source-open-solver SCIP. The experimental results underline the potential of combining both diving heuristics and conflict analysis. Summary of Contribution. This original article concerns the advancement of exact general-purpose algorithms for solving one of the largest and most prominent problem classes in optimization, mixed-integer linear programs. It demonstrates how methods for conflict analysis that learn from infeasible subproblems can be combined successfully with diving heuristics that aim at finding primal solutions. For two newly designed diving heuristics, this paper features a thoroughly computational study regarding their impact on the overall performance of a state-of-the-art MIP solver.

BTWalk: Branching Tree Random Walk for Multi-Order Structured Network Embedding

Multi-order proximity is useful for effective network embedding. In contrast to many previous works that only consider order-level weights, this paper proposes to explore a more expressive node-level weighting mechanism to encode the diverse local structure, with a scalable and theoretically justified sampling strategy for its learning. Specifically, we start with a formal definition of multi-order proximity matrix which leads to our new multi-order objective based on Laplacian Eigenmaps and Skip-Gram. Then we instantiate the node-specific multi-order weights in the objective with the help of neighborhood size estimation, which indicates node-specific multi-order information. For objective learning, it is implicitly fulfilled with our proposed branching tree-like random walk strategy termed by BTWalk, which differs from the dominant chain-like walk in existing sampling techniques. BTWalk is designed by a synergetic combination of BFS (breadth-first search) and DFS (depth-first search), which is modulated according to the weights of the considered proximity orders. We theoretically analyze its cost-efficiency, and further propose the so-called Vec4Cross framework that incorporates joint node embedding and network alignment for two partially overlapped networks based on the seed matchings, whereby BTWalk is also adopted for embedding. Promising experimental results are obtained on real-world datasets across popular tasks.