Arbitrary Start Research Articles

Adaptive virtual team planning and coordination: a mathematical programming approach

Purpose The rise of remote work increasingly requires organizations to coordinate a single large, consolidated talent pool into ad-hoc, short-term project teams on demand. This problem involves many simultaneous considerations including project revenues and rejection costs, conflicting projects and roles, worker assignment costs, worker utilization preferences and limits, worker reassignment costs, and arbitrary role start and end times. Moreover, plans must be continuously updated in response to changing circumstances. This paper addresses the problem of dynamic virtual team planning and coordination. Design/methodology/approach We show this problem is NP-hard and provide a dynamic mixed integer linear programming (MILP) formulation for both optimal initial plan generation as well as continuous plan adjustment and re-optimization. We utilized a factorial experiment design to generate benchmark problems spanning a wide range of characteristics and conducted extensive computational experimentation using a common MILP solver. Findings Exactly optimal solutions to large, realistically sized problems were consistently obtained in short amounts of time. All observed solution times were sufficient to support the operational decision-making requirements of real-world virtual team coordination, demonstrating the viability of this approach. Practical implications The approach developed in this research can enable organizations to optimally coordinate virtual teams on a large scale and continually adjust plans in response to changing circumstances, all in an automated manner. Originality/value This paper addresses a new and complex problem of increasing importance to organizations due to the rise in remote work. We provide a problem formulation and exact approach for optimally solving both the planning and re-planning aspects of this problem.

FDRA: A Framework for a Dynamically Reconfigurable Accelerator Supporting Multi-Level Parallelism

Coarse-grained reconfigurable architectures (CGRAs) have emerged as promising accelerators due to their high flexibility and energy efficiency. However, existing open source works often lack integration of CGRAs with CPU systems and corresponding toolchains. Moreover, there is rare support for the accelerator instruction pipelining to overlap data communication, computation, and configuration across multiple tasks. In this article, we propose FDRA, an open source exploration framework for a heterogeneous system-on-chip (SoC) with a RISC-V processor and a dynamically reconfigurable accelerator (DRA) supporting loop, instruction, and task levels of parallelism. FDRA encompasses parameterized SoC modeling, Verilog generation, source-to-source application code transformation using frontend and DRA compilers, SoC simulation, and FPGA prototyping. FDRA incorporates the extraction of periodic accumulative operators and multi-dimensional linear load/store operators from nested loops. The DRA enables accessing the shared L2 cache with virtual addresses and supports direct memory access with arbitrary start addresses and data lengths. Integrated into the RISC-V Rocket SoC, our DRA achieves a remarkable 55× acceleration for loop kernels and improves energy efficiency by 29×. Compared to state-of-the-art RISC-V vector units, our DRA demonstrates a 2.9× speed improvement and 3.5× greater energy efficiency. In contrast to previous CGRA+RISC-V SoCs, our SoC achieves a minimum speedup of 5.2×.

Implementation of Behavior Based Robotics on Hexapod Legged Robot Based on Room Mapping

The Indonesian Fire Fighting Robot Contest (KRPAI) has a mission to extinguish fires quickly, with various kinds of points and bonuses to be taken. The robot used in this research is a 6-legged robot (Hexapod). The problem faced today is the robot's ability to navigate the room, the robot still has to enter the same room, especially in room 1 (a room with 2 doors) so that the Arbitrary Start bonus or Variable Door location which prohibits the robot from entering the same room cannot be obtained. There is also a Room Factor bonus obtained using the JTS (Segment Distance) method which is an additional bonus when the robot walks outside the room. It is necessary to develop a room mapping algorithm with behavior based so that the robot can get maximum points. The main sensor used is the IMU BNO055 sensor as a robot direction orientation sensor, as well as a proximity sensor as a wall search sensor, so that the robot can know the direction when leaving and exploring the room, knowing the starting room, and ensuring the next room entered is a different room. The results of the test successfully made the robot take maximum points in the race.

Flexible manipulation of the onset and terminal positions of femtosecond laser filamentation in fused silica via controlling beam profile before axicon

The flexible control of femtosecond laser filamentation is crucial for many practical and promising applications. We have demonstrated a simple method to control arbitrarily the onset and the terminal positions of femtosecond laser filament in the fused silica by tailoring the profile of the annular Bessel beam. The tailoring of the inner and outer radius of the annular beam is realized by amplitude mask and iris diaphragm respectively before the axicon. Our experiments have shown that the onset of the filamentation can be modulated by changing the inner radius of the laser beam, and the filamentation termination is determined by the outer radius of the annular beam. Moreover, the results show that the onset and terminal positions of the filaments are positively correlated with the inner and outer radius of the beam, respectively, which facilitate the arbitrary start and length control of the filament in the experiment. In addition, this arbitrary regulation of the onset and termination of the filamentation is being negligibly affected by incident laser power that is lower than the damage threshold of fused silica. The flexible control of filamentation enabled by the simple method is beneficial to many applications.

A Sim-to-Real Pipeline for Deep Reinforcement Learning for Autonomous Robot Navigation in Cluttered Rough Terrain

Robots that autonomously navigate real-world 3D cluttered environments need to safely traverse terrain with abrupt changes in surface normals and elevations. In this letter, we present the development of a novel sim-to-real pipeline for a mobile robot to effectively learn how to navigate real-world 3D rough terrain environments. The pipeline uses a deep reinforcement learning architecture to learn a navigation policy from training data obtained from the simulated environment and a unique combination of strategies to directly address the reality gap for such environments. Experiments in the real-world 3D cluttered environment verified that the robot successfully performed point-to-point navigation from arbitrary start and goal locations while traversing rough terrain. A comparison study between our DRL method, classical, and deep learning-based approaches showed that our method performed better in terms of success rate, and cumulative travel distance and time in a 3D rough terrain environment.

A dynamical analysis on non-local autoregressive model and its application on image reconstruction

Abstract Natural images are highly structured, which reflects strong spatial redundancy underlying its pixels. In this paper, a dynamical analysis of the non-local autoregressive (NAR) model is researched for the first time. We prove that under the condition when the spectrum radius of NAR matrix is less than 1, the original image as the global stable fixed point of function system referring to a modified NAR model can be iteratively recovered via attractor reconstruction from an arbitrary start point.

TOTAL WEIGHTED TARDINESS MINIMIZATION FOR TASKS WITH A COMMON DUE DATE ON PARALLEL MACHINES IN CASE OF AGREEABLE WEIGHTS AND PROCESSING TIMES

We consider tasks scheduling problem on identical parallel machines by the criterion of minimizing the total weighted tardiness of tasks. All tasks arrive for processing at the same time. Weights and processing times are agreeable, that is, a greater weight of a task corresponds to a shorter processing time. In addition, we have arbitrary start times of machines for tasks processing. The times may be less or greater than the due date or to coincide with it. The problem in this formulation is addressed for the first time. It can be used to provide planning and decision making in systems with a network representation of technological processes and limited resources. We give efficient PSC-algorithm with complexity that includes the poly­nomial component and the approximation algorithm based on permutations of tasks. The polynomial component contains sufficient signs of optimality of the obtained solutions and allows to obtain an exact solution by polynomial subalgorithm. In the case when the sufficient signs of opti­mality do not fulfill, we obtain approximate solution with an estimate of de­viation from the optimum for each individual problem instance of any prac­tical dimension. We show that a schedule obtained as a result of the problem solving can be split into two schedules: the schedule on machines which start time is less than or equal to the due date, and the schedule on machines which start after the due date. Optimization is only done in the first sched­ule. The second schedule is optimal by construction. Statistical studies of the PSC-algorithm showed its high efficiency. We solved problems with dimensions up to 40,000 tasks and up to 30 ma­chines. The average time to solve the problem by the algorithm using the most efficient types of permutations was 27.3 ms for this dimension. The average frequency of an optimal solution obtaining amounted to 90.3 %. The average deviation from an optimum was no more than 0.000251.

Про швидкiсть збiжностi субградiєнтих методiв з кроком Поляка

Subgradient methods (method A and method B) are considered for finding the minimum point of a convex function for the known optimal value of the function. Both methods use known Polyak’s step, which depends on scalar parameter m ≥ 1 and guarantees monotonous decrease of the distance to the minimum point. If m = 1 the methods A and B are applicable to arbitrary convex function. Parameter m > 1 permits to take into account special classes of convex functions – convex quadratic functions (m = 2), differentiable homogeneous of degree σ (m = σ), etc. For both methods theorems are proven about the convergence rate O 1/ √ k for arbitrary convex function and convergence with geometric progression rate for a convex function with acute minimum. Method A is a subgradient method with Polyak’s step in original space of variables. The parameter m = 1 corresponds to classical Polyak’s step for arbitrary convex function. Parameter m = 2 can be used for quadratic functions minimization, for which the value of Polyak’s step is doubled comparing to the classical Polyak’s step with m = 1. For onedimentional functions method A finds the minimum point in one step from the arbitrary start point, which corresponds to one iteration of the Newton’s method. Proof of theorems about convergence rate of the method A is based on convex function characteristics in original space of variables, among which a constant c1 plays a crucial role – it bounds a subgradient norm. Method B is a subgradient method with space transformation, where the Polyak’s step is calculated in space transformed by linear operator. The method is defined by nonsingular matrix B. The parameter m = 1 corresponds to classical Polyak’s step for arbitrary convex function in transformed space of variables. If m = 2, for one-dimentional quadratic function, the method B finds the minimum point in one step with both arbitrary matrix B and start point. Proof of theorems about convergence rate of the method B is based on function characteristics in transformed space of variables, which are analogous to the method A characteristics in original space.

A Novel Fast and Flexible Holomorphic Embedding Power Flow Method

A novel, fast, and flexible holomorphic embedding (FFHE) method is proposed for solving power flow problems with Q-limits. New embedded systems are developed for power flow equations at the PV and PQ buses for flexibility that allows us taking any state, instead of accepting only some certain point (e.g., a flat start and a zero-current injection point), as the initial guess. This flexibility allows us a warm start, a dc power flow solution, or other starts, leading to the reduced runtime and a decreased number of needed steps, as compared with existing embedded systems that only accept several particular points as the initial guess. However, the proposed method may not be globally convergent in the sense that such flexibility and flexible embedded systems do not ensure convergence or a superior rate of convergence for an arbitrary start. To implement the proposed FFHE, a new numerical scheme is devised to compute the numerical solution without explicitly calculating the rational approximant. To evaluate its effectiveness and practicability, the proposed novel FFHE method is applied to a series of power flow test cases ranging from 3 to 13659 buses. It is found that the proposed FFHE method requires less runtime than Newton's method for most test cases. By using Newton's method as a reference, FFHE is shown to be more effective than the holomorphic embedding methods that had been documented in the literature; partly due to its flexibility and the new scheme for computing numerical solutions.

Evaluation of Shortest Path Algorithms for Solving Mazes.

Finding the optimal path through a maze is related to the shortest path problem in computer science. The first shortest path algorithm was introduced by Edsger W. Dijkstra in 1956 but multiple other solutions and optimizations have been suggested since then. We tested several algorithms for their applicability to the problem of solving mazes. We programmatically generated random 2D square mazes with independent variables for size and bias, where bias measures the deviation from random probability that a left or right turn contributes to a shorter path. We programmatically transformed each maze into a directed graph with arbitrary start and end positions and then solved for the shortest path through each graph. We speculated that maze bias, which tends to alter the graph density, would affect algorithm efficiency. We detected bias-dependent differences in resource usage between standard implementations of three shortest-path algorithms: Dijkstra’s, Bellman-Ford, and A*. Our results could help guide algorithm selection for robotic exploration of specific terrains given prior knowledge of their path properties. This project was supported by the NASA WV Space Grant Consortium.

Numerical evaluation of mobile robot navigation in static indoor environment via EGAOR Iteration

One of the key issues in mobile robot navigation is the ability for the robot to move from an arbitrary start location to a specified goal location without colliding with any obstacles while traveling, also known as mobile robot path planning problem. In this paper, however, we examined the performance of a robust searching algorithm that relies on the use of harmonic potentials of the environment to generate smooth and safe path for mobile robot navigation in a static known indoor environment. The harmonic potentials will be discretized by using Laplacian’s operator to form a system of algebraic approximation equations. This algebraic linear system will be computed via 4-Point Explicit Group Accelerated Over-Relaxation (4-EGAOR) iterative method for rapid computation. The performance of the proposed algorithm will then be compared and analyzed against the existing algorithms in terms of number of iterations and execution time. The result shows that the proposed algorithm performed better than the existing methods.

Network design and analysis for multi-enzyme biocatalysis

BackgroundAs more and more biological reaction data become available, the full exploration of the enzymatic potential for the synthesis of valuable products opens up exciting new opportunities but is becoming increasingly complex. The manual design of multi-step biosynthesis routes involving enzymes from different organisms is very challenging. To harness the full enzymatic potential, we developed a computational tool for the directed design of biosynthetic production pathways for multi-step catalysis with in vitro enzyme cascades, cell hydrolysates and permeabilized cells.ResultsWe present a method which encompasses the reconstruction of a genome-scale pan-organism metabolic network, path-finding and the ranking of the resulting pathway candidates for proposing suitable synthesis pathways. The network is based on reaction and reaction pair data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the thermodynamics calculator eQuilibrator. The pan-organism network is especially useful for finding the most suitable pathway to a target metabolite from a thermodynamic or economic standpoint. However, our method can be used with any network reconstruction, e.g. for a specific organism. We implemented a path-finding algorithm based on a mixed-integer linear program (MILP) which takes into account both topology and stoichiometry of the underlying network. Unlike other methods we do not specify a single starting metabolite, but our algorithm searches for pathways starting from arbitrary start metabolites to a target product of interest. Using a set of biochemical ranking criteria including pathway length, thermodynamics and other biological characteristics such as number of heterologous enzymes or cofactor requirement, it is possible to obtain well-designed meaningful pathway alternatives. In addition, a thermodynamic profile, the overall reactant balance and potential side reactions as well as an SBML file for visualization are generated for each pathway alternative.ConclusionWe present an in silico tool for the design of multi-enzyme biosynthetic production pathways starting from a pan-organism network. The method is highly customizable and each module can be adapted to the focus of the project at hand. This method is directly applicable for (i) in vitro enzyme cascades, (ii) cell hydrolysates and (iii) permeabilized cells.

Vicinity of an arbitrary start surface in the case of emission in the T-mode

A complete hydrodynamic pattern of a 3D relativistic electron beam in a nonuniform external magnetic field near the start surface of an arbitrary shape in the case of emission in the T-mode is constructed.

The prefrontal oxygenation and ventilatory responses at start of one-legged cycling exercise have relation to central command.

When performing exercise arbitrarily, activation of central command should start before the onset of exercise, but when exercise is forced to start with cue, activation of central command should be delayed. We examined whether the in-advance activation of central command influenced the ventilatory response and reflected in the prefrontal oxygenation, by comparing the responses during exercise with arbitrary and cued start. The breath-by-breath respiratory variables and the prefrontal oxygenated-hemoglobin concentration (Oxy-Hb) were measured during one-legged cycling. Minute ventilation (V̇e) at the onset of arbitrary one-legged cycling was augmented to a greater extent than cued cycling, while end-tidal carbon dioxide tension (ETco2) decreased irrespective of arbitrary or cued start. Symmetric increase in the bilateral prefrontal Oxy-Hb occurred before and at the onset of arbitrary one-legged cycling, whereas such an increase was absent with cued start. The time course and magnitude of the increased prefrontal oxygenation were not influenced by the extent of subjective rating of perceived exertion and were the same as those of the prefrontal oxygenation during two-legged cycling previously reported. Mental imagery or passive performance of the one-legged cycling increased V̇e and decreased ETco2 Neither intervention, however, augmented the prefrontal Oxy-Hb. The changes in ETco2 could not explain the prefrontal oxygenation response during voluntary or passive one-legged cycling. Taken together, it is likely that the in-advance activation of central command influenced the ventilatory response by enhancing minute ventilation at the onset of one-legged cycling exercise and reflected in the preexercise increase in the prefrontal oxygenation.

A Target Searching Scenario in Undirected Acyclic Graph

In a generic problem in search theory we have some metric search space and two players - a target T and a searcher S. Mostly T is static in the space according to some specified probability distribution or in some cases dynamic, andS starts its search at some arbitrary start point. The usual goal will be to design a strategy that minimizes the expected time forS to findT .S knows the search space but has no other information and T could be a fully or partially informed target. In some versions, S has visibility characteristics allowing it to see a small distance ‘ ’ from its location. This kind of exploration problems relate to various contexts, such as robot motion planning in hazardous or inaccessible terrain, maintaining security of large networks, and searching, indexing, and analyzing digital data in the Internet [1] [2] [3]. One of the earliest examples of such problems is the linear search problem, proposed by Bellman [4] and Beck [5]. Here, the search space is an infinite line, with the searcher initially at an arbitrary origin, and the target located at an unknown point on the line, at distanced from the origin. The objective is to minimize the worst-case ratio of the distance traveled by the searcher over d. Different scenario (with cycles) is considered in cops and robbers problem [6] where the cops and robbers take alternate turns in movements and the question usually posed is how many pursuers are necessary to ensure the eventual capture of all the robbers. The Isaac’s Princess and Monster problem [7] is also a related problem but with different scenario. The Lost Cow problem [8] is stated as a short-sighted cow following along an infinite fence and wants to find the gate. The lost cow problem is limited in the idea that the target is static. In case of one dimensional space, if a moving target wants to escape, then it can do so by constantly moving away from the searcher. And in case of a cyclic graph, a fully informed target can move around in cycles and never be found by the searcher. The present study stands out in that it studies the problem in case of undirected acyclic graph which was not studied before. But the proposed search space offers an interesting environment to both target and the searcher. To the target, it provides an opportunity to move towards the searcher without being found; and to the searcher it provides an easier search space by removing the case of cyclic movement of the target. Sleator and Tarjan [9] suggested evaluating the performance of an online algorithm using competitive analysis. This paper proposes an online algorithm for the provided search space and also attempts to find upper and lower bounds of the competitive ratio. General Terms

Central command generated prior to arbitrary motor execution induces muscle vasodilatation at the beginning of dynamic exercise.

The purpose of this study was to examine the role of central command, generated prior to arbitrary motor execution, in cardiovascular and muscle blood flow regulation during exercise. Thirty two subjects performed 30 s of two-legged cycling or 1 min of one-legged cycling (66 ± 4% and 35% of the maximal exercise intensity, respectively), which was started arbitrarily or abruptly by a verbal cue (arbitrary vs. cued start). We measured the cardiovascular variables during both exercises and the relative changes in oxygenated-hemoglobin concentration (Oxy-Hb) of noncontracting vastus lateralis muscles as index of tissue blood flow and femoral blood flow to nonexercising leg during one-legged cycling. Two-legged cycling with arbitrary start caused a decrease in total peripheral resistance (TPR), which was smaller during the exercise with cued start. The greater reduction of TPR with arbitrary start was also recognized at the beginning of one-legged cycling. Oxy-Hb of noncontracting muscle increased by 3.6 ± 1% (P < 0.05) during one-legged cycling with arbitrary start, whereas such increase in Oxy-Hb was absent with cued start. The increases in femoral blood flow and vascular conductance of nonexercising leg were evident (P < 0.05) at 10 s from the onset of one-legged cycling with arbitrary start, whereas those were smaller or absent with cued start. It is likely that when voluntary exercise is started arbitrarily, central command is generated prior to motor execution and then contributes to muscle vasodilatation at the beginning of exercise. Such centrally induced muscle vasodilatation may be weakened and/or masked in the case of exercise with cued start.

Single-Grasp Object Classification and Feature Extraction with Simple Robot Hands and Tactile Sensors.

Classical robotic approaches to tactile object identification often involve rigid mechanical grippers, dense sensor arrays, and exploratory procedures (EPs). Though EPs are a natural method for humans to acquire object information, evidence also exists for meaningful tactile property inference from brief, non-exploratory motions (a 'haptic glance'). In this work, we implement tactile object identification and feature extraction techniques on data acquired during a single, unplanned grasp with a simple, underactuated robot hand equipped with inexpensive barometric pressure sensors. Our methodology utilizes two cooperating schemes based on an advanced machine learning technique (random forests) and parametric methods that estimate object properties. The available data is limited to actuator positions (one per two link finger) and force sensors values (eight per finger). The schemes are able to work both independently and collaboratively, depending on the task scenario. When collaborating, the results of each method contribute to the other, improving the overall result in a synergistic fashion. Unlike prior work, the proposed approach does not require object exploration, re-grasping, grasp-release, or force modulation and works for arbitrary object start positions and orientations. Due to these factors, the technique may be integrated into practical robotic grasping scenarios without adding time or manipulation overheads.

Increased oxygenation of the cerebral prefrontal cortex prior to the onset of voluntary exercise in humans.

To determine whether output from the forebrain (termed central command) may descend early enough to increase cardiac and renal sympathetic outflows at the onset of voluntary exercise, we examined the changes in regional tissue blood flows of bilateral prefrontal cortices with near-infrared spectroscopy, precisely identifying the onset of voluntary ergometer 30-s exercise at 41 ± 2% of the maximal exercise intensity in humans. Prefrontal oxygenated-hemoglobin (Oxy-Hb) concentration was measured as index of regional blood flow unless deoxygenated-hemoglobin concentration remained unchanged. Prefrontal Oxy-Hb concentration increased significantly (P < 0.05) 5 s prior to the onset of exercise with arbitrary start, whereas such increase in prefrontal Oxy-Hb was absent before exercise abruptly started by a verbal cue. Furthermore, the increase in prefrontal Oxy-Hb observed at the initial 15-s period of exercise was greater with arbitrary start than cued start. The prefrontal Oxy-Hb, thereafter, decreased during the later period of exercise, irrespective of either arbitrary or cued start. The reduction in prefrontal Oxy-Hb had the same time course and response magnitude as that during motor-driven passive exercise. Cardiac output increased at the initial period of exercise, whereas arterial blood pressure and total peripheral resistance decreased. The depressor response was more pronounced (P < 0.05) with arbitrary start than cued start. Taken together, it is suggested that the increase in prefrontal Oxy-Hb observed prior to the onset of voluntary exercise may be in association with central command, while the later decrease in the Oxy-Hb during exercise may be in association with feedback stimulated by mechanical limb motion.

Limitations of Front-To-End Bidirectional Heuristic Search

We present an intuitive explanation for the limited effectiveness of front-to-end bidirectional heuristic search, supported with extensive evidence from many commonly-studied domains. While previous work has proved the limitations of specific algorithms, we show that any front-to-end bidirectional heuristic search algorithm will likely be dominated by unidirectional heuristic search or bidirectional brute-force search. We also demonstrate a pathological case where bidirectional heuristic search is the dominant algorithm, so a stronger claim cannot be made. Finally, we show that on the four-peg Towers Of Hanoi with arbitrary start and goal states, bidirectional brute-force search outperforms unidirectional heuristic search using pattern-database heuristics.

B105 車両の形状を考慮した目標経路の自動生成(OS1 適応学習制御とその応用2)

In this paper, we propose a method of a path-planning for a mobile robot. The method uses a grid map to define any shape of obstacles and considers shape of mobile robot. The shortest path can be generated by searching the grid map. This method consists of two steps to expand obstacles, and can provide practical and suitable path-planning for a control of a mobile robot. Moreover, the path-planning can set arbitrary start posture and end one. The effectiveness of this method is shown by simulation.