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Much of recent progress in geophysics can be attributed to the adaptation of heterogeneous high-performance computing architectures. It is projected that the next major leap in many areas of science, and hence hopefully in geophysics too, will be due to the emergence of quantum computers. Finding a right combination of hardware, algorithms, and a use case, however, proves to be a very challenging task — especially when looking for a relevant application that scales efficiently on a quantum computer and is difficult to solve using classical means. We find that maximizing stack power for residual statics correction, an NP-hard combinatorial optimization problem, appears to naturally fit a particular type of quantum computing known as quantum annealing. We express the underlying objective function as a quadratic unconstrained binary optimization, which is a quantum-native formulation of the problem. We choose some solution space and define a proper encoding to translate the problem variables into qubit states. We find that these choices can have a significant impact on the maximum problem size that can fit on the quantum annealer and on the fidelity of the final result. To improve the latter, we embed the quantum optimization step in a hybrid classical-quantum workflow, which aims to increase the frequency of finding the global, rather than some local, optimum of the objective function. Finally, we find that a generic, black-box, hybrid classical-quantum solver also could be used to solve stack-power maximization problems proximal to industrial relevance and capable of surpassing deterministic solvers prone to cycle skipping. A custom-built workflow capable of solving larger problems with an even higher robustness and greater control of the user appears to be within reach in the very near future.

—The development of Multi-access edge computing (MEC) has resulted from the requirement for supporting next generation mobile services, which need high capacity, high reliability and low latency. The key issue in such MEC architectures is to decide which edge nodes will be employed for serving the needs of the different end users. Here, we take a fresh look into this problem by focusing on the minimization of migration events rather than focusing on maximizing usage of resources. This is important because service migrations can create significant service downtime to applications that need low latency and high reliability, in addition to increasing traffic congestion in the underlying network. This paper introduces a priority induced service migration minimization (PrISMM) algorithm, which aims at minimizing service migration for both high and low priority services, through the use of Markov decision process, learning automata and combinatorial optimization. We carry out extensive simulations and produce results showing its effectiveness in reducing the mean service downtime of lower priority services and the mean admission time of the higher priority services.

Made available in DSpace on 2022-04-28T19:50:39Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-01-01 Consumption patterns of electric power systems are important for distribution companies, because of their significant impact on energy losses amount. Therefore, some incentives are suggested by distribution companies to energy consumers for correcting their consumption manner. For a specific load pattern, distribution system reconfiguration (DSR) is an effective way to mitigate energy losses. Hence, some research works have included load variations in the DSR problem to show the importance of consumption patterns in reconfiguration decisions. However, some of the specialized literature has ignored load changes in their reconfiguration models due to the high computational burden and processing time. On the other hand, the energy losses are calculated inaccurately if the consumption pattern is neglected. Consequently, the main goal of this paper is to investigate load pattern impact on switching sequences to find out how much is load profile important for minimization of energy losses in DSR The evaluations have been done for three well-known distribution systems using a classic optimization tool: A Mathematical Programming Language (AMPL). Associated Laboratory of IPBEN, So Paulo State University, Brazil, (e-mail: m.mahdavi@unesp.br) INESC TEC, 112047 Porto, Portugal, 4200-465 (e-mail: msjavadi@gmail.com) Department of Electrical Engineering, North China Electric Power University, 47840 Baoding, Hebei, China, 071003 (e-mail: feiwang@ncepu.edu.cn) Faculty of Engineering, University of Porto, 26706 Porto, Porto, Portugal, 00351967344438 (e-mail: catalao@ubi.pt)

This paper provides a subspace method for closed-loop identification, which clearly specifies the model order from noisy measurement data. The method can handle long I/O data of the target system to be noise-tolerant and determine the model order via nuclear norm minimization. First, the proposed method compresses the long data by projecting them to an appropriate low dimensional subspace, then obtains a low order model whose order is specified by a combination of data compression and nuclear norm minimization. Its effectiveness is demonstrated through detailed numerical examples.

Direction-of-arrival (DOA) estimation is widely applied in acoustic source localization. A multi-frequency model is suitable for characterizing the broadband structure in acoustic signals. In this paper, the continuous (gridless) DOA estimation problem with multiple frequencies is considered. This problem is formulated as an atomic norm minimization (ANM) problem. The ANM problem is equivalent to a semi-definite program (SDP) which can be solved by an off-the-shelf SDP solver. The dual certificate condition is provided to certify the optimality of the SDP solution so that the sources can be localized by finding the roots of a polynomial. We also construct the dual polynomial to satisfy the dual certificate condition and show that such a construction exists when the source amplitude has a uniform magnitude. In multi-frequency ANM, spatial aliasing of DOAs at higher frequencies can cause challenges. We discuss this issue extensively and propose a robust solution to combat aliasing. Numerical results support our theoretical findings and demonstrate the effectiveness of the proposed method., Comment: This work has been accepted by IEEE Transactions on Signal Processing

Information freshness is crucial for time-critical IoT applications, e.g., monitoring and control systems. We consider an IoT status update system with multiple users, multiple energy harvesting sensors, and a wireless edge node. The users receive time-sensitive information about physical quantities, each measured by a sensor. Users send requests to the edge node where a cache contains the most recently received measurements from each sensor. To serve a request, the edge node either commands the sensor to send a status update or retrieves the aged measurement from the cache. We aim at finding the best actions of the edge node to minimize the age of information of the served measurements. We model this problem as a Markov decision process and develop reinforcement learning (RL) algorithms: model-based value iteration and model-free Q-learning methods. We also propose a Q-learning method for the realistic case where the edge node is informed about the sensors' battery levels only via the status updates. The case under transmission limitations is also addressed. Furthermore, properties of an optimal policy are analytically characterized. Simulation results show that an optimal policy is a threshold-based policy and that the proposed RL methods significantly reduce the average cost compared to several baselines., 33 pages, 6 figures

This paper presents a metaheuristic method, namely Equilibrium Optimizer (EO) to solve the economic dispatch (ED) problem. The main objective function to be achieved is to minimize the total fuel costs of all generating units to meet the total load demand and to satisfy various operating constraints. Three case studies are used to test the effectiveness of the EO method in solving ED problems, they are three generators case, six generators case, and fifteen generators case. The simulation of solving ED problems using the EO method is implemented using MATLAB software and is carried out 30 times for each case study. The results of EO method are compared with Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA) methods. The simulation results show that the EO method can solve the ED problem more optimal than all other comparison methods for all the case studies by producing the minimum total fuel costs.

This is a replication and extension of our previous studies on minimization tactics and pragmatic implication/inference. Specifically, its purpose is to more closely examine the mechanisms that may be at work in the process by which minimization tactics influence sentencing expectations.

Objective: Minimization is a legal interrogation tactic in which an interrogator attempts to alleviate a suspect’s anxiety associated with confessing to a crime. These studies aimed to examine the extent to which minimization pragmatically implies that a suspect will receive a more lenient sentence in exchange for a confession. Hypotheses: Generally, we predicted that participants who read an interrogation with a minimization theme or a direct promise of leniency would mistakenly expect more lenient sentences compared to a control condition if the suspect confessed to the crime. Hypotheses were preregistered prior to conducting each experiment. Method: In six experiments (Ns=413, 574, 496, 552, 489, 839), MTurkers read an interrogation transcript in which the suspect was (1) promised leniency, (2) subjected to minimization, or (3) questioned about the evidence (control). We tested whether warnings about direct promises and minimization induce people to adjust their expectations of sentence severity. Results: Warnings about leniency repaired sentencing expectations when participants read an interrogation with a direct promise, but were ineffective when an interrogator used minimization. Planned mediation analyses suggest that moral minimization techniques decrease sentencing expectations after a confession by influencing perceived crime severity. Honesty themes, like illegal direct promises, decrease sentence expectations by implying that leniency will be forthcoming in exchange for a confession. Conclusions: Contrary to the beliefs of American courts, who have allowed minimization but not direct promises to be used in interrogations, minimization does indeed impact sentencing expectations. There may be cause to review the legality of such tactics. ORIGINAL STUDY 1 ABSTRACT: After people receive corrections for inaccurate information, implied misinformation generates greater memory errors than explicit misinformation (Rich & Zaragoza, 2015). This effect is thought to occur because it is more difficult for people to detect the influence of implied misinformation. It is possible that pragmatic implications that suggest confessing to a crime will result in a more lenient sentence are more persistent in their influence on expectations of sentencing, compared to explicit promises of leniency. That is, even if people are explicitly warned that the police are not able to guarantee leniency in exchange for a confession, they may have substantial difficulty adjusting their expectations about punishment in the appropriate direction.

Having found no evidence that the minimization tactics we tested in Studies 1, 2, and 3 cause people to draw explicit inferences that the suspect will receive more lenient treatment (despite having an apparently reliable effect on sentencing expectations), we have decided to test an alternative form of minimization: one in which the interrogator develops a theme about the value of honesty and makes references to how other people, including the courts and prosecutors, may view honesty favorably. Such tactics were employed in the high-profile interrogation of Brendan Dassey. Indeed, we have derived the language of this condition from a transcript of Mr. Dassey’s interrogation. We expect to replicate our previous results that participants in the moral minimization condition will not draw inferences about lenient treatment of the suspect, and we predict that participants in the honesty theme condition will draw such inferences. We are also interested in testing a possible mechanism for the effects of moral minimization on sentencing expectations. It is possible that this form of minimization influences expectations by changing people’s perceptions of how severe the crime was. That is, perhaps moral minimization makes people view the crime as less blameworthy and therefore less likely to be punished severely. We will assess this possibility using a measure of perceived crime severity.

This is the project for the fifth study in our series of studies on pragmatic implicature in police interrogations. In this study we are focused on clarifying the mechanism according to which moral minimization functions to reduce sentencing expectations. We will also attempt to replicate the pragmatic implication effects we observed in Study 4.

We consider downlink transmission whereby a multiantenna base station simultaneously transmits data to multiple single-antenna users. We focus on slow flat fading channel where the channel state information is imperfect, the channel estimation error is unbounded and its statistics are known. The aim is to design beamforming vectors such that the sum rate is maximized under the constraints on probability of successful transmission for each user and maximum transmit power. The optimization problem is intractable due to the chance constraints. To this end, we propose an efficient solution drawn upon stochastic optimization. In particular, we first use the step function and its smooth approximation to get an approximate nonconvex stochastic program of the considered problem. We then develop an iterative procedure to solve the stochastic program based on the stochastic successive convex approximation framework. The numerical results show that the proposed solution can achieve remarkable sum rate gains compared to the conventional one. Science Foundation Ireland Academy of Finland

Unequal reactive power sharing amongst distributed generators (DG) is a significant concern while operating a droop based microgrid. The reasons for this unequal reactive power sharing include the difference in feeder impedances, uneven distribution of loads and DGs in terms of size and locations. In this article, a mixed-integer linear programming (MILP) problem applicable for a droop based microgrid has been proposed to achieve proportional reactive power sharing amongst DGs while maintaining very low line losses. Firstly, the optimal sizing and placement of DGs are investigated. This includes a detailed discussion on the linearisation of various nonlinear terms involved in the formulation. Subsequently, a day–ahead dispatch is generated for these DGs for a given load profile. Additionally, network reconfiguration has been incorporated for further improvement in performance. The effect of considering a practical constant impedance-current-power (ZIP) load model, instead of simply considering constant impedance loads, has also been assessed. The proposed formulation has been tested on a 33 bus network which has been modified to represent an islanded microgrid. The results obtained validate the accuracy of the proposed planning and dispatch method and demonstrate its utility in achieving proportional reactive power sharing amongst the DGs while incurring very low line losses.

The paper discusses the derivation of an accurate coordinate measuring system consisting of two, three, or four sensors based on the records of four fixed laser triangulation sensors done for a gauge block in movement. Three-dimensional case is considered. In the simulations, using a set of distances quadruplets, parameters of sensors in a local sensors coordinate system are determined through a least squares minimization process using the Differential Evolution approach. The influence of the measurement and rounding inaccuracy on the identification accuracy using numerical simulation methods are assessed.

Considered in this article is an asynchronous single-carrier two-way relay network, where two transceivers aim to communicate through a set of amplify-and-forward (AF) relays. This network is asynchronous in the sense that the signal transmitted by any of the two transceivers arrives the other transceiver through different relaying paths, with possibly different propagation delays, thereby materializing a multipath channel. At sufficiently high data rates, this multi-path end-to-end channel causes inter-symbol-interference (ISI) in the received signals. Considering such a network, this paper presents two contributions. The first contribution is the rigorous characterization of the region of the mean-squared errors (MSEs) of the symbol estimates at the two transceivers under a total power budget, and when linear block post-channel equalization is used at the receiver front-end of the two transceivers. The importance of this MSE region characterization resides in the fact that knowing this region allows for characterization the region of un-coded probabilities of error at the two transceivers. Also, this MSE region characterization paves the way towards presenting the second contribution in this paper. Indeed, in the second contribution, this article relies on this MSE region characterization to rigorously prove that an MSE-constrained total power minimization approach and a rate-constrained total power minimization approach to design transceiver power allocation and distributed beamforming are equivalent, if the MSE thresholds in the former approach and the rate thresholds in the latter approach are properly chosen. The equivalence of these two approaches implies that the un-coded MSE performance of the network can be inferred from the rate-constrained problem, and conversely, the coded rate performance of the network can be inferred from the MSE-constrained total power minimization problem.

A power series being given as the solution of a linear differential equation with appropriate initial conditions, minimization consists in finding a non-trivial linear differential equation of minimal order having this power series as a solution. This problem exists in both homogeneous and inhomogeneous variants; it is distinct from, but related to, the classical problem of factorization of differential operators. Recently, minimization has found applications in Transcendental Number Theory, more specifically in the computation of non-zero algebraic points where Siegel's $E$-functions take algebraic values. We present algorithms and implementations for these questions, and discuss examples and experiments., Comment: 48 pages

Background. Although prolonged-release tacrolimus (PR-T) is widely approved for posttransplantation immunosuppression in kidney recipients, large-scale studies are required to assess long-term outcomes. We present follow-up data from the Advagraf-based Immunosuppression Regimen Examining New Onset Diabetes Mellitus in Kidney Transplant Recipients (ADVANCE) trial, in which kidney transplant patients (KTPs) received corticosteroid minimization with PR-T. Methods. ADVANCE was a 24-wk, randomized, open-label, phase-4 study. De novo KTPs received PR-T with basiliximab and mycophenolate mofetil and were randomized to receive an intraoperative corticosteroid bolus plus tapered corticosteroids until day 10 (arm 1) or an intraoperative corticosteroid bolus (arm 2). In this 5-y, noninterventional follow-up, patients received maintenance immunosuppression according to standard practice. The primary endpoint was graft survival (Kaplan-Meier). Secondary endpoints included patient survival, biopsy-confirmed acute rejection-free survival, and estimated glomerular filtration rate (4-variable modification of diet in renal disease). Results. Follow-up study included 1125 patients. Overall graft survival at 1 and 5 y posttransplantation was 93.8% and 88.1%, respectively, and was similar between treatment arms. At 1 and 5 y, patient survival was 97.8% and 94.4%, respectively. Five-year graft and patient survival rates in KTPs who remained on PR-T were 91.5% and 98.2%, respectively. Cox proportional hazards analysis demonstrated similar risk of graft loss and death between treatment arms. Five-year biopsy-confirmed acute rejection-free survival was 84.1%. Mean ± standard deviation values of estimated glomerular filtration rate were 52.7 ± 19.5 and 51.1 ± 22.4 mL/min/1.73 m²at 1 and 5 y, respectively. Fifty adverse drug reactions were recorded, probably tacrolimus-related in 12 patients (1.5%). Conclusions. Graft survival and patient survival (overall and for KTPs who remained on PR-T) were numerically high and similar between treatment arms at 5 y posttransplantation. CA extern

We develop a workflow, implemented as a plugin to the molecular visualization program VMD, that can fix ring penetrations with minimal user input. LongBondEliminator, detects ring piercing artifacts by the long, strained bonds that are the local minimum energy conformation during minimization for some assembled simulation system. The LongBondEliminator tool then automatically treats regions near these long bonds using multiple biases applied through NAMD. By combining biases implemented through the collective variables module, density-based forces, and alchemical techniques in NAMD, LongBondEliminator will iteratively alleviate long bonds found within molecular simulation systems. Through three concrete examples with increasing complexity, a lignin polymer, an viral capsid assembly, and a large, highly glycosylated protein aggrecan, we demonstrate the utility for this method in eliminating ring penetrations from classical MD simulation systems. The tool is available via gitlab as a VMD plugin, and has been developed to be generically useful across a variety of biomolecular simulations.

Existing works for depth estimation and image deblurring in the presence of depth-dependent blur work with the assumption of a multi-layered scene wherein each layer is modeled in the form of a fronto-parallel plane. In this work, we attempt to relax these constraints by considering more generalized settings of a 3D scene with piecewise planar structure, i.e., a scene that can be modeled as a combination of multiple planes with arbitrary orientations. To this end, we first propose a novel approach to estimate the normal of a planar surface from a single motion-blurred image. We then extend this idea and develop an algorithm for automatic recovery of the number of planes, the parameters corresponding to each plane, and camera motion from a single motion-blurred image of a multi-planar 3D scene. Finally, we propose a first-of-its-kind approach to recover the planar geometry and latent image of the scene by adopting an alternating minimization framework built on our findings. Experiments on synthetic and real data reveal that our proposed method achieves state-of-the-art results on the dual problem of depth recovery and image deblurring.

Delivering timely status information of a random process has become increasingly important for time-sensitive applications, e.g., vehicle tracking and environment monitoring. We consider an IoT sensing network, where a cache-enabled wireless edge node receives on-demand requests from multiple users to send status updates on physical quantities, each measured by an energy harvesting sensor. To serve users’ requests, the edge node uses the current information state (i.e., the number of requests, battery level, and AoI for each sensor) to decide whether to command a sensor to send a status update or to retrieve the most recently received sensor’s measurements from the cache. We aim at finding the best actions of the edge node to minimize the average AoI of the served measurements at the users, i.e., average on-demand AoI. We model this as a Markov decision process problem and derive a relative value iteration algorithm to find an optimal policy. Simulation results illustrate the threshold-based structure of an optimal policy and show that the proposed on-demand updating policy outperforms the greedy (myopic) policy and also, by accounting for the per-sensor request frequencies and intensities, the pure average AoI minimization policy that keeps the edge node updated regardless of requests.

El objetivo de la presente investigación es evaluar la asignación óptima de los recursos de una empresa agroindustrial dedicada a producción de nopal en salmuera en tres presentaciones, a través del método simplex de programación lineal usando la metodología de investigación de operaciones. Como herramienta tecnológica se utiliza el complemento Solver de la hoja de cálculo de Excel. El tema es relevante ya que, en México, se identifica alta mortandad de empresas en sus primeros años sobre todo en las micro y pequeñas, mientras que, las que permanecen en un mercado dinámico y competitivo manifiestan problemáticas con relación a la toma de decisiones que garantice una asignación eficiente de recursos. De los principales hallazgos destaca la construcción de un modelo matemático aplicando el método simplex para calcular el nivel de producción óptimo que minimice los costos de producción, resultando útil su aplicación para contribuir en la toma de decisiones de los empresarios., The objective of this research is to evaluate the optimal allocation of resources of an agroindustrial enterprise dedicated to the production of nopal in brine in three presentations, through the simplex method of linear programming using the operations research methodology. As a technological tool, the Solver complement of the Excel spreadsheet is used. The issue is relevant since, in Mexico, high mortality of companies is identified in their first years, especially in micro and small ones, while those that remain in a dynamic and competitive market manifest problems in relation to decision-making that ensure efficient allocation of resources. The main findings include the construction of a mathematical model applying the simplex method to calculate the optimal production level that minimizes production costs, its application being useful to contribute to the decision-making of entrepreneurs., O objetivo desta pesquisa é avaliar a alocação ótima de recursos de uma empresa agroindustrial dedicada à produção de nopal em salmoura em três apresentações, através do método simplex de programação linear utilizando a metodologia de pesquisa operacional. O suplemento Solver da planilha do Excel é usado como uma ferramenta tecnológica. O tema é relevante já que, no México, é identificada uma alta taxa de mortalidade de empresas em seus primeiros anos, especialmente em micro e pequenas empresas, enquanto aquelas que permanecem em um mercado dinâmico e competitivo apresentam problemas em relação à tomada de decisões para garantir uma alocação eficiente de recursos. As principais conclusões incluem a construção de um modelo matemático aplicando o método simplex para calcular o nível ideal de produção que minimiza os custos de produção, o que é útil para contribuir para o processo de tomada de decisão dos empreendedores.

Autonomous driving systems are among the most interesting technologies in the transportation field nowadays, ensuring a high level of safety and comfort while also enhancing energy saving. For the following case study, a Battery Electric Vehicle (BEV) is considered able to communicate with other vehicles through vehicle-to-vehicle (V2V) technology by exchanging information on position and velocity. In this framework, an innovative real-time velocity planner has been developed aiming at maximizing the battery energy savings while improving the passenger comfort as well. This controller uses the principles of the equivalent consumption minimization strategy (ECMS) when the preceding vehicle is accelerating. Simulation results demonstrate improvements in comfort ranging from 26% to 42% ca. and in energy consumption (from 0.4% to 1.3%) when performing different drive cycles in V2V automated driving mode thanks to the proposed controller.

One of the biggest problems that human beings face is climate change and construction industry is one of the sectors causing the greatest impact, which has led him to adopt sustainable strategies. However, these have not been effective enough to reduce CO2 emissions, since solutions are required accompanied by other alternatives. Therefore, the objective of this study is to reduce CO2 by implementing CDW strategies under the following method: 1) analysis of CO2 of the major raw materials consumed, 2) use of the materials at the construction site was determined, 3) waste streams generated at the construction site were identified and those that could be reuse were highlighted, 4) CDW recycling strategies to avoid the use of the major raw materials consumption are proposed, and 5) one of the strategies proposed is further analyzed. The results show that natural aggregates generate the greater amount of CO2 due to the consumption of fuel required for their transportation. By replacing 68% of natural aggregates by recycled aggregates, CO2 can be reduced by 53%. Finally, these results can help to promote the opening of new CDW recycling companies in Nayarit and thereby contribute to the sum of sustainability practices in housing building.

In this paper we consider a distributed optimization scenario in which a set of processors aims at cooperatively solving a class of min-max optimization problems. This set-up is motivated by peak-demand minimization problems in smart grids. Here, the goal is to minimize the peak value over a finite horizon with: (i) the demand at each time instant being the sum of contributions from different devices, and (ii) the device states at different time instants being coupled through local constraints (e.g., the dynamics). The min-max structure and the double coupling (through the devices and over the time horizon) makes this problem challenging in a distributed set-up (e.g., existing distributed dual decomposition approaches cannot be applied). We propose a distributed algorithm based on the combination of duality methods and properties from min-max optimization. Specifically, we repeatedly apply duality theory and properly introduce ad-hoc slack variables in order to derive a series of equivalent problems. On the resulting problem we apply a dual subgradient method, which turns out to be a distributed algorithm consisting of a minimization on the original primal variables and a suitable dual update. We prove the convergence of the proposed algorithm in objective value. Moreover, we show that every limit point of the primal sequence is an optimal (feasible) solution. Finally, we provide numerical computations for a peak-demand optimization problem in a network of thermostatically controlled loads., Submitted to journal

Randomized controlled trial is widely accepted as the best design for evaluating the efficacy of a new treatment because of the advantages of randomization (random allocation). Randomization eliminates accidental bias, including selection bias, and provides a base for allowing the use of probability theory. Despite its importance, randomization has not been properly understood. This article introduces the different randomization methods with examples: simple randomization; block randomization; adaptive randomization, including minimization; and response-adaptive randomization. Ethics related to randomization are also discussed. The study is helpful in understanding the basic concepts of randomization and how to use R software.

Collaborative business processes are indeed complex and difficult to be correctly designed as business processes involved in them are typically developed by different participating organizations and there is no way to foresee all potential interactions at design time. To this end, we propose a novel correctness enforcement approach for collaborative business processes. In this approach, given an original process, we first propose an algorithm for automatically detecting its correctness. Then, in case the original process is partially correct, we prune it to generate its core. The core captures all legal traces in the original process. Finally, an enforced process is yielded from the core through coordination mapping (i.e., inserting additional coordination activities into original processes). Our approach is implemented as a prototype tool called cet (a correctness enforcement tool for collaborative business processes), and a series of experiments is conducted to validate the applicability and effectiveness of the proposed approach.

Software engineering comprises several disciplines. Testing, the subject of this paper, is an important phase which is still largely unpredictable, expensive, and ad hoc. Software testing has already been framed as an optimization problem, i.e., to solve problems in software testing various optimization techniques can be used. To adequately test the software, various rules called test adequacy criteria are used. In this paper, a review of optimization techniques used in domains, test case generation, selection, minimization, and prioritization of testing, has been presented. The review is conducted by selecting quality literature from journals, conferences, workshops, and symposium. The state-of-the-art issues in software testing have been addressed by analyzing the reviewed literature on the basis of the domain of testing, test adequacy criteria, and optimization technique used. After analysis of the considered literature, authors were able to conclude the limitations of existing work, list of traditional and recently proposed adequacy criteria as well as combinations of adequacy criteria for multi-objective optimization and optimization techniques which may be used in the optimization of software testing.

The subject of the research is to identify the risks of entrepreneurial activity, their types and nature. It also discusses the classification of the main types of risks, the substantiation of evaluation and analysis methods in order to avoid, minimize or neutralize to ensure the efficiency and competitiveness of enterprises. The purpose of the work is to identify the business risks, their content, the classification of species, the substantiation of the leading methods of evaluation and analysis in order to avoid and minimize and on this basis – ensuring the efficiency and competitiveness of the operation and development of enterprises. The methodological basis of the article became the main methodological provisions of the enterprise's economy, regulatory and legislative acts on the development of entrepreneurial activity, general scientific and special economic methods of cognition. Among them are generalization, grouping, extrapolation, method of comparative analysis, statistical, expert estimations, mathematical forecasting and programming. Results of work. In the process of writing the article, the essence of entrepreneurial risks was identified, their types were identified, classification and grouping according to the types and areas of manifestation were made. This gave an opportunity to substantiate the methods of their further analysis and evaluation. In turn, taking into account and preventing risks on this basis will allow to ensure the tactics and strategy of the development of enterprises with proper efficiency and competitiveness. The field of application of results. The results of the study can be used in the theory and practice of business risk management. Risk assessment and analysis will be appropriate in the economy, in the finances of enterprises, in the analysis of entrepreneurial activity, in developing strategies for its development in the future. Conclusions. Risks of entrepreneurial activity have different spheres of manifestation and degree of influence on it. They can be evaluated both qualitatively and quantitatively. In addition, the proposed grouping and classifications allow, in a certain way, to identify managerial decisions, at least in terms of spheres of manifestation. In general, assessing the types and extent of the impact of business risks and their inclusion in tactical and strategic management will be a significant factor in ensuring the efficiency and competitiveness of enterprises in the long run.

The aim of this paper is to propose a novel methodology for estimating the social influence among agents interacting in a sparse social network described by the Friedkin and Johnsen model. In this classical model, $n$ agents discuss $m\ll n$ topics, which are influenced by the others’ opinions, but are not completely open minded, being persistently driven by their prejudices. We reconstruct the social network topology and the strength of the interconnections starting from observations of the initial and final opinions’ profile only. The intrinsic sparsity of the graph is exploited via an $\ell _0/\ell _1$ minimization. Different from the techniques previously proposed in the literature, no partial knowledge of the social graph is assumed, and there is no need to optimally place external stubborn agents injecting prescribed inputs, thus changing the terminal behavior of the opinion dynamics. Under suitable assumptions, we derive theoretical conditions that guarantee the problem is well posed and sufficient requirements on the number of topics under discussion that ensure perfect recovery. Extensive simulations on synthetic and real networks corroborate theoretical results.

Pontryagin’s Minimum Principle is a way of solving hybrid powertrain optimal energy management. This paper presents an improvement of a classical implementation. The core of this improvement consists in relaxing the tolerance on some intermediate steps of the algorithm in order to reduce the number of iterations and thereby reducing the number of operations required to compute an optimal solution. The paper describes both a classical implementation of Pontryagin’s Minimum Principle as well as the improved version. Numerical simulations are conducted on an academic example to demonstrate the benefits of the proposed approach.

The social sciences are witnessing a growing body of multilevel theories and debates about the proper methodological tools for the analysis of multilevel data. In a recent contribution to this journal, Thomas Denk (2010) proposes multilevel Qualitative Comparative Analysis (QCA) as a new methodological tool for discerning set-relational patterns in multilevel data. I argue that the presentation of multilevel QCA is erroneous in two respects. First, multilevel QCA ignores the fact that equifinal solutions entail diversity and therefore leads one to overestimate the complexity of QCA solutions. Second, the ordinary minimization procedure of truth tables that contain multilevel data yields the same solutions as multilevel QCA, but is much easier to implement. I conclude that the established inventory of QCA does not need to be extended by a special multilevel approach., {"references":["Rohlfing, Ingo (2011): Analyzing Multilevel Data with Qca: A Straightforward Procedure. International Journal of Social Research Methodology 15 (6): 497-506."]}

Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary solution to enhance wireless communications by changing propagation environment in a cost-effective and hardware-efficient fashion. In addition, symbol-level precoding (SLP) has attracted considerable attention recently due to its advantages in converting multiuser interference (MUI) into useful signal energy. Therefore, it is of interest to investigate the employment of IRS in symbol-level precoding systems to exploit MUI in a more effective way by manipulating the multiuser channels. In this paper, we focus on joint symbol-level precoding and reflecting designs in IRS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both power minimization and quality-of-service (QoS) balancing problems are considered. In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems. An efficient gradient-projection-based algorithm is utilized to design the symbol-level precoding and a Riemannian conjugate gradient (RCG)-based algorithm is employed to solve the reflecting design problem. Simulation results demonstrate the significant performance improvement introduced by the IRS and illustrate the effectiveness of our proposed algorithms., 13 pages, 13 figures, published on TWC

Topic modeling, or identifying the set of topics that occur in a collection of articles, is one of the primary objectives of text mining. One of the big challenges in topic modeling is determining the correct number of topics: underestimating the number of topics results in a loss of information, i.e., omission of topics, underfitting, while overestimating leads to noisy and unexplainable topics and overfitting. In this paper, we consider a semantic-assisted non-negative matrix factorization (NMF) topics model, which we call SeNMFk, based on Kullback-Leibler(KL) divergence and integrated with a method for determining the number of latent topics. SeNMFk involves (i) creating a random ensemble of pairs of matrices whose mean is equal to the initial words-by-documents matrix representing the text corpus and the Shifted Positive Pointwise Mutual Information (SPPMI) matrix, which encodes the context information, respectively, and (ii) jointly factorizing each of these pairs with different number of topics to acquire sets of latent topics that are stable to noise. We demonstrate the performance of our method by identifying the number of topics in several benchmark text corpora, when compared to other state-of-the-art techniques. We also show that the number of document classes in the input text corpus may differ from the number of the extracted latent topics, but these classes can be retrieved by clustering the column-vectors of one of the factor matrices. Additionally, we introduce a software called pyDNMFk to estimate the number of topics. We demonstrate that our unsupervised method, SeNMFk, not only determines the correct number of topics, but also extracts topics with a high coherence and accurately classifies the documents of the corpus.

Background. The study considers various aspects of constructing iterative anytime algorithms for solving the problem of state minimization of nondeterministic finite automata. Although this problem was posed back in the 60s of the 20th century, it is NP-hard; therefore, the development of efficient algorithms for its solution remains a relevant problem. The purpose of the study is to analysis of the choice influence of different variants of subproblems’ weight characteristics using branch and bound method on the efficiency of the basic algorithm version and its modifications, using various heuristics. Materials and methods. The study is based on the analysis of numerical experiments performed using the software implementation of the described algorithms. The program is implemented in C# 6.0 for the .NET Framework. Results. The results are the revealed patterns associated with the choice of the weight characteristics of subtasks in the branch and bound method for the algorithm of the nondeterministic finite automata state minimization. Conclusions. We determine optimal weight characteristics both for the basic algorithm and for its modifications supplied with additional heuristics. We also show that the use of these weight characteristics together with additional heuristics can significantly increase efficiency of the developed algorithms.

Recently, we have developed an efficient generic partition refinement algorithm, which computes behavioural equivalence on a state-based system given as an encoded coalgebra, and implemented it in the tool CoPaR. Here we extend this to a fully fledged minimization algorithm and tool by integrating two new aspects: (1) the computation of the transition structure on the minimized state set, and (2) the computation of the reachable part of the given system. In our generic coalgebraic setting these two aspects turn out to be surprisingly non-trivial requiring us to extend the previous theory. In particular, we identify a sufficient condition on encodings of coalgebras, and we show how to augment the existing interface, which encapsulates computations that are specific for the coalgebraic type functor, to make the above extensions possible. Both extensions have linear run time., LIPIcs, Vol. 195, 6th International Conference on Formal Structures for Computation and Deduction (FSCD 2021), pages 28:1-28:19

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The article is devoted to the study of the influence of autumn cultivation systems on the water-physical properties of the soil (moisture, hardness and density) and the yield of Novosibirskaya 31 wheat at different levels of chemicalization. Materials and methods. The research was carried out on the experimental field of SibNIIZiKh in the ��Elitnoye�� OPKh and on the SibIME test site, located in the central forest-steppe. Land type ��� leached medium loamy chernozem in combination with gray forest soils. The experiment was carried out on two intensification backgrounds: low-intensity (normal) ��� without the use of fertilizers + herbicides; intensive ��� complex application of fertilizers and plant protection chemicals. The field was divided into plots. The method of split plots was applied to four backgrounds of chemicalization across the main (autumn) treatments. The experiments were carried out in 4 repetitions. The harvest was taken into account by the method of continuous combining. During the growing season, soil samples were taken in the upper 1-meter profile to determine moisture reserves and density. At the same time, the SC 900 penetrometer was used to measure soil hardness to a depth of 45 cm. Results. This work reflects the regularities of changes in soil moisture depending on the systems of the main cultivation before sowing and after harvesting, graphs of changes in soil hardness depending on the methods of processing and the depth of immersion of the penetrometer plunger (hardness tester) are presented. Also given are the dependences of soil hardness and density on moisture content and immersion depth of the penetrometer plunger. The results of changes in yield for four variants of chemicalization at two levels of chemicalization are presented. Discussion. As a result of the research, it was found that where the moldboard plowing was carried out annually to a depth of 20���22 cm, the soil moisture at a depth of 20 cm was 18.3 %, at a depth of 40 cm ��� 14.9 %. The maximum soil hardness was at a depth of 20 cm. As the humidity decreased, the soil hardness increased. In terms of moisture re-serves, flat-cut flat-cut strip tillage at different depths did not differ significantly from the annual non-moldboard tillage and the minimum one. In the area where moldboard plowing was carried out on an untreated background, soil hardness reached a maximum only at a depth of 27.5 cm. The resulting crop at various levels of chemicalization makes it possible to identify the optimal method of basic processing for each crop rotation field when cultivating grain crops. Conclusion. The main tillage systems affect the yield of wheat Novosibirskaya 31 In particular, against the background without chemicals, a higher yield was noted for autumn plowing, significantly lower for shallow autumn tillage and the No-Till system. With a full complex of chemicalization, the maximum yield of spring wheat was observed in the variant with flat-cut strip cutting at different depths. Against this background, the considered indicator was significantly less for fine processing and the No-Till system. Wheat yields were intermediate in other main processing systems.

As the scale of the current network keeps increasing at a dramatic rate, the future Internet-of-Things applications will need to connect billions of sensor nodes. This brings great challenges to the traditional "transmit then compute" mode, as communication latency will be excessive for a large-scale communication network. The massive access points will also exhaust bandwidth resources in a conventional multiple-access scheme. By exploiting the superposition property of a multi-access channel, Over-the-Air Computation (AirComp) has been recognized as a low-latency solution for wireless sensor data fusion, where multiple sensors simultaneously send their measurement signals to a receiver for computation. Most existing work only considered performing AirComp over a single frequency channel. However, for a sensor network with a massive number of nodes, a single frequency channel may not be sufficient to accommodate the large number of sensors, and the AirComp performance will be very limited. So it is highly desirable to have more frequency channels for large-scale AirComp systems to benefit from multi-channel diversity. In this thesis, we propose an M-frequency AirComp system where each sensor selects a subset of the M frequencies and broadcasts its signal over these channels under a certain power constraint. We derive the optimal sensors' transmission and receiver's signal processing methods separately, and develop an algorithm for joint design to achieve the best AirComp performance. We further investigate the property of the optimal design problem to reduce its complexity. Numerical results show that increasing one frequency channel can improve the AirComp performance threefold compared to the single-frequency case.

We develop approximation algorithms for set-selection problems with deterministic constraints, but random objective values, i.e., stochastic probing problems. When the goal is to maximize the objective, approximation algorithms for probing problems are well-studied. On the other hand, few techniques are known for minimizing the objective, especially in the adaptive setting, where information about the random objective is revealed during the set-selection process and allowed to influence it. For minimization problems in particular, incorporating adaptivity can have a considerable effect on performance. In this work, we seek approximation algorithms that compare well to the optimal adaptive policy. We develop new techniques for adaptive minimization, applying them to a few problems of interest. The core technique we develop here is an approximate reduction from an adaptive expectation minimization problem to a set of adaptive probability minimization problems which we call threshold problems. By providing near-optimal solutions to these threshold problems, we obtain bicriteria adaptive policies. We apply this method to obtain an adaptive approximation algorithm for the MIN-ELEMENT problem, where the goal is to adaptively pick random variables to minimize the expected minimum value seen among them, subject to a knapsack constraint. This partially resolves an open problem raised in Goel et. al's "How to probe for an extreme value". We further consider three extensions on the MIN-ELEMENT problem, where our objective is the sum of the smallest k element-weights, or the weight of the min-weight basis of a given matroid, or where the constraint is not given by a knapsack but by a matroid constraint. For all three variations we explore, we develop adaptive approximation algorithms for their corresponding threshold problems, and prove their near-optimality via coupling arguments., Comment: Accepted to ITCS 2022