bluuebunny/crossref_metadata_2025 huggingface dataset:
Blind Decision Making: Reinforcement Learning with Delayed Observations
Authors: Agarwal Mridul , Aggarwal Vaneet
Published: September 2022

In Reinforcement Learning (RL) the current state of the environment may not always be available. One approach to fix this could be to include the actions after the last-known state as a part of the state information, however, that leads to an increased state-space making the problem complex and slower in convergence. We propose an approach, where the delay in the knowledge of the state can be used, and the decisions are made to maximize the expected state-action value function. The proposed algorithm is an alternate approach where the state space is not enlarged, as compared to the case when there is no delay in the state update. Evaluations on the basic RL environments further illustrate the improved performance of the proposed algorithm.
Compositional Approach to Translate LTLf/LDLf into Deterministic Finite Automata
Authors: De Giacomo Giuseppe , Favorito Marco
Published: September 2022

The translation from temporal logics to automata is the workhorse algorithm of several techniques in computer science and AI, such as reactive synthesis, reasoning about actions, FOND planning with temporal specifications, and reinforcement learning with non-Markovian rewards, to name a few. Unfortunately, the problem is computationally intractable, requiring the implementation of several heuristics to make it usable in practice. In this paper, following the recent interest in temporal logic formalisms over finite traces, we present a compositional approach for dealing with translations of Linear Temporal Logic (LTLf) and Linear Dynamic Logic (LDLf) on finite traces into Deterministic Finite Automata (DFA). We inductively transform each LTLf/LDLf subformula into a DFA and combine them through automata operators. By relying on efficient semi-symbolic automata representations, we empirically show our approach's effectiveness and competitiveness with similar tools. Moreover, this is the first work that provides a scalable and practical tool supporting the translation to DFA not only for LTLf but also for full LDLf.
Multiple Plans are Better than One: Diverse Stochastic Planning
Authors: Ghasemi Mahsa , Scope Crafts Evan , Zhao Bo , Topcu Ufuk
Published: September 2022

In planning problems, it is often challenging to fully model the desired specifications. In particular, in human-robot interaction, such difficulty may arise due to human's preferences that are either private or complex to model. Consequently, the resulting objective function can only partially capture the specifications and optimizing that may lead to poor performance with respect to the true specifications. Motivated by this challenge, we formulate a problem, called diverse stochastic planning, that aims to generate a set of representative --- small and diverse --- behaviors that are near-optimal with respect to the known objective. In particular, the problem aims to compute a set of diverse and near-optimal policies for systems modeled by a Markov decision process. We cast the problem as a constrained nonlinear optimization for which we propose a solution relying on the Frank-Wolfe method. We then prove that the proposed solution converges to a stationary point and demonstrate its efficacy in several planning problems.
Endomorphisms of Lifted Planning Problems
Authors: Horčík Rostislav , Fišer Daniel
Published: September 2022

Classical planning tasks are usually modelled in the PDDL which is a schematic language based on first-order logic. Nevertheless, most of the current planners turn this first-order representation into a propositional one via the grounding process. It is well known that the grounding process may cause an exponential blowup. Therefore it is important to detect which grounded atoms are redundant in a sense that they are not necessary for finding a plan and therefore the grounding process does not need to generate them. This is usually done by a relaxed reachability analysis, which can be improved by employing structural symmetries. Symmetries are bijective self-maps preserving the structure of the PDDL task. In this paper, we introduce a new method which is based on self-maps preserving the structure but which need not be bijective. We call these maps PDDL endomorphisms and we show that they can be used for pruning of redundant objects even if they appear in a reachable atom. We formulate the computation of endomorphisms as a constraint satisfaction problem (CSP) that can be solved by an off-the-shelf CSP solver.
Total Completion Time Minimization for Scheduling with Incompatibility Cliques
Authors: Jansen Klaus , Lassota Alexandra , Maack Marten , Pikies Tytus
Published: September 2022

This paper considers parallel machine scheduling with incompatibilities between jobs. The jobs form a graph equivalent to a collection of disjoint cliques. No two jobs in a clique are allowed to be assigned to the same machine.

Scheduling with incompatibilities between jobs represents a well-established line of research in scheduling theory and the case of disjoint cliques has received increasing attention in recent years. While the research up to this point has been focused on the makespan objective, we broaden the scope and study the classical total completion time criterion. In the setting without incompatibilities, this objective is well-known to admit polynomial time algorithms even for unrelated machines via matching techniques. We show that the introduction of incompatibility cliques results in a richer, more interesting picture. We prove that scheduling on identical machines remains solvable in polynomial time, while scheduling on unrelated machines becomes APX-hard. Next, we study the problem under the paradigm of fixed-parameter tractable algorithms (FPT). In particular, we consider a problem variant with assignment restrictions for the cliques rather than the jobs. We prove that, despite still being APX-hard, it can be solved in FPT time with respect to the number of cliques. Moreover, we show that the problem on unrelated machines can be solved in FPT time for reasonable parameters, in particular, the parameter combination: maximum processing time, number of job kinds, and number of machines or maximum processing time, number of job kinds, and number of cliques. The latter results are extensions of known results for the case without incompatibilities, and can even be further extended to the case of total weighted completion time. All of the FPT results make use of n-fold Integer Programs that recently received great attention by proving their usefulness for scheduling problems.
Temporal Reasoning with Kinodynamic Networks
Authors: Zhang Han , Tiruviluamala Neelesh , Koenig Sven , Kumar T. K. Satish
Published: September 2022

Temporal reasoning is central to Artificial Intelligence (AI) and many of its applications. However, the existing algorithmic frameworks for temporal reasoning are not expressive enough to be applicable to robots with complex kinodynamic constraints typically described using differential equations. For example, while minimum and maximum velocity constraints can be encoded in Simple Temporal Networks (STNs), higher-order kinodynamic constraints cannot be represented in existing frameworks. In this paper, we present a novel framework for temporal reasoning called Kinodynamic Networks (KDNs). KDNs combine elements of existing temporal reasoning frameworks with the idea of Bernstein polynomials. The velocity profiles of robots are represented using Bernstein polynomials; and dynamic constraints on these velocity profiles can be converted to linear constraints on the to-be-determined coefficients of their Bernstein polynomials. We study KDNs for their attractive theoretical properties and apply them to the Multi-Agent Path Finding (MAPF) problem with higher-order kinodynamic constraints. We show that our approach is not only scalable but also yields smooth velocity profiles for all robots that can be executed by their controllers.
DeepFreight: A Model-free Deep-reinforcement-learning-based Algorithm for Multi-transfer Freight Delivery
Authors: Chen Jiayu , Umrawal Abhishek K. , Lan Tian , Aggarwal Vaneet
Published: September 2022

With the freight delivery demands and shipping costs increasing rapidly, intelligent control of fleets to enable efficient and cost-conscious solutions becomes an important problem. In this paper, we propose DeepFreight, a model-free deep-reinforcement-learning-based algorithm for multi-transfer freight delivery, which includes two closely-collaborative components: truck-dispatch and package-matching. Specifically, a deep multi-agent reinforcement learning framework called QMIX is leveraged to learn a dispatch policy, with which we can obtain the multi-step joint vehicle dispatch decisions for the fleet with respect to the delivery requests. Then an efficient multi-transfer matching algorithm is executed to assign the delivery requests to the trucks. Also, DeepFreight is integrated with a Mixed-Integer Linear Programming optimizer for further optimization. The evaluation results shows that the proposed system is highly scalable and ensures a 100% delivery success while maintaining low delivery-time and fuel consumption.
Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning
Authors: Shen Macheng , How Jonathan P.
Published: September 2022

This paper studies decision-making in two-player scenarios where the type (e.g. adversary, neutral, or teammate) of the other agent (opponent) is uncertain to the decision-making agent (protagonist), which is an abstraction of security-domain applications. In these settings, the reward for the protagonist agent depends on the type of the opponent, but this is private information known only to the opponent itself, and thus hidden from the protagonist. In contrast, as is often the case, the type of the protagonist agent is assumed to be known to the opponent, and this information-asymmetry significantly complicates the protagonist's decision-making. In particular, to determine the best actions to take, the protagonist agent must infer the opponent type from the observations and agent modeling. To address this problem, this paper presents an opponent-type deduction module based on Bayes' rule. This inference module takes as input the imagined opponent's decision-making rule (opponent model) as well as the observed opponent's history of actions and states, and outputs a belief over the opponent's hidden type. A multiagent reinforcement learning approach is used to develop this game-theoretic opponent model through self-play, which avoids the expensive data collection step that requires interaction with a real opponent. Besides, this multiagent approach also captures the strategy interaction and reasoning between agents. In addition, we apply ensemble training to avoid over-fitting to a single opponent model during the training. As a result, the learned protagonist policy is also effective against unseen opponents. Experimental results show that the proposed game-theoretic modeling, explicit opponent type inference and the ensemble training significantly improves the decision-making performance over baseline approaches, and generalizes well against adversaries that have not been seen during the training.
It Costs to Get Costs! A Heuristic-Based Scalable Goal Assignment Algorithm for Multi-Robot Systems
Authors: Aakash  , Saha Indranil
Published: September 2022

The goal assignment problem for a multi-robot application involves assigning a unique goal to each robot to minimize the total cost of movement for all the robots. A significant step in the state-of-the-art algorithms solving this problem is to find the cost associated with each robot-goal pair. For a large multi-robot system with many robots and many goals in a complex workspace, the computation time required to find the paths for all robot-goal pairs may become prohibitively large. We present an algorithm that solves the optimal goal assignment problem without computing the paths between all the robot-goal pairs. Instead, our algorithm computes the obstacle-free optimal path for any robot-goal pair in a demand-driven way, i.e., if it is absolutely required to ensure the optimality of the goal assignment. We evaluate our algorithm extensively on both randomly generated and standard workspaces for hundreds of robots. Our experimental results demonstrate that the proposed algorithm achieves an order-of-magnitude speedup over the state-of-the-art baseline algorithm. To the best of our knowledge, our algorithm is the first one to solve the multi-robot optimal goal assignment problem without computing the paths for all robot-goal pairs explicitly, guaranteeing high scalability.
Flexible FOND HTN Planning: A Complexity Analysis
Authors: Chen Dillon Z. , Bercher Pascal
Published: September 2022

Hierarchical Task Network (HTN) planning is an expressive planning formalism that has often been advocated to address real-world problems. Yet few extensions exist that can deal with the many challenges encountered in the real world, one being the capability to express uncertainty. Recently, a new HTN formalism for fully observable nondeterministic problems was proposed and studied theoretically. In this paper, we lay out limitations of that formalism and propose an alternative definition, which addresses and resolves such limitations. We also study its complexity for certain problems.
Task-Guided Inverse Reinforcement Learning under Partial Information
Authors: Djeumou Franck , Cubuktepe Murat , Lennon Craig , Topcu Ufuk
Published: September 2022

We study the problem of inverse reinforcement learning (IRL), where the learning agent recovers a reward function using expert demonstrations. Most of the existing IRL techniques make the often unrealistic assumption that the agent has access to full information about the environment. We remove this assumption by developing an algorithm for IRL in partially observable Markov decision processes (POMDPs). The algorithm addresses several limitations of existing techniques that do not take the information asymmetry between the expert and the learner into account. First, it adopts causal entropy as the measure of the likelihood of the expert demonstrations as opposed to entropy in most existing IRL techniques, and avoids a common source of algorithmic complexity. Second, it incorporates task specifications expressed in temporal logic into IRL. Such specifications may be interpreted as side information available to the learner a priori in addition to the demonstrations and may reduce the information asymmetry. Nevertheless, the resulting formulation is still nonconvex due to the intrinsic nonconvexity of the so-called forward problem, i.e., computing an optimal policy given a reward function, in POMDPs. We address this nonconvexity through sequential convex programming and introduce several extensions to solve the forward problem in a scalable manner.
This scalability allows computing policies that incorporate memory at the expense of added computational cost yet also outperform memoryless policies. We demonstrate that, even with severely limited data, the algorithm learns reward functions and policies that satisfy the task and induce a similar behavior to the expert by leveraging the side information and incorporating memory into the policy.
Assignment and Prioritization of Tasks with Uncertain Durations for Satisfying Makespans in Decentralized Execution
Authors: Gopalakrishnan Sriram , Borrajo Daniel
Published: September 2022

Task assignment under execution uncertainty and temporal/resource constraints is a standard problem for many organizations. Existing approaches in the AI planning & scheduling and operations research literature predominantly focus on dynamic controllability, and non-preemptive execution of tasks. Such solutions are appropriate for teams of agents under tight control requirements. However, in most organizations with human teams, once tasks have been assigned, humans tend to execute their assignments without a constant central oversight (which is needed for dynamic controllability).

In this paper we define a problem in which execution of tasks is distributed (without central oversight), and assumes humans can preempt their tasks when other tasks of higher priority are ready to be worked on. We present two algorithms based on Tabu search and Monte Carlo Tree Search to assign and prioritize tasks for such problems. Experimental results show the improved efficacy of these approaches for this problem setting over non-preemptive strategies.
Planning for Risk-Aversion and Expected Value in MDPs
Authors: Rigter Marc , Duckworth Paul , Lacerda Bruno , Hawes Nick
Published: September 2022

Planning in Markov decision processes (MDPs) typically optimises the expected cost. However, optimising the expectation does not consider the risk that for any given run of the MDP, the total cost received may be unacceptably high. An alternative approach is to find a policy which optimises a riskaverse objective such as conditional value at risk (CVaR). However, optimising the CVaR alone may result in poor performance in expectation. In this work, we begin by showing that there can be multiple policies which obtain the optimal CVaR. This motivates us to propose a lexicographic approach which minimises the expected cost subject to the constraint that the CVaR of the total cost is optimal. We present an algorithm for this problem and evaluate our approach on four domains. Our results demonstrate that our lexicographic approach improves the expected cost compared to the state of the art algorithm, while achieving the optimal CVaR.
A*pex: Efficient Approximate Multi-Objective Search on Graphs
Authors: Zhang Han , Salzman Oren , Kumar T. K. Satish , Felner Ariel , Hernández Ulloa Carlos , Koenig Sven
Published: September 2022

In multi-objective search, edges are annotated with cost vectors consisting of multiple cost components. A path dominates another path with the same start and goal vertices iff the component-wise sum of the cost vectors of the edges of the former path is ``less than'' the component-wise sum of the cost vectors of the edges of the latter path. The Pareto-optimal solution set is the set of all undominated paths from a given start vertex to a given goal vertex. Its size can be exponential in the size of the graph being searched, which makes multi-objective search time-consuming. In this paper, we therefore study how to find an approximate Pareto-optimal solution set for a user-provided vector of approximation factors. The size of such a solution set can be significantly smaller than the size of the Pareto-optimal solution set, which enables the design of approximate multi-objective search algorithms that are efficient and produce small solution sets. We present such an algorithm in this paper which we call A*pex and which builds on PP-A*, a state-of-the-art approximate bi-objective search algorithm (where there are only two cost components) but (1) makes PP-A* more efficient for bi-objective search and (2) generalizes it to multi-objective search for any number of cost components. We first analyze the correctness of A*pex and then experimentally demonstrate its efficiency advantage over existing approximate algorithms for bi- and tri-objective search.
Joint Pricing and Matching for City-Scale Ride-Pooling
Authors: Shah Sanket , Lowalekar Meghna , Varakantham Pradeep
Published: September 2022

Central to efficient ride-pooling are two challenges: (1) how to `price' customers' requests for rides, and (2) if the customer agrees to that price, how to best `match' these requests to drivers. While both of them are interdependent, each challenge's individual complexity has meant that, historically, they have been decoupled and studied individually.

This paper creates a framework for batched pricing and matching in which pricing is seen as a meta-level optimisation over different possible matching decisions. Our key contributions are in developing a variant of the revenue-maximizing auction corresponding to the meta-level optimization problem, and then providing a scalable mechanism for computing posted prices. We test our algorithm on real-world data at city-scale and show that our algorithm reliably matches demand to supply across a range of parameters.
Stochastic Resource Optimization over Heterogeneous Graph Neural Networks for Failure-Predictive Maintenance Scheduling
Authors: Wang Zheyuan , Gombolay Matthew
Published: September 2022

Resource optimization for predictive maintenance is a challenging computational problem that requires inferring and reasoning over stochastic failure models and dynamically allocating repair resources. Predictive maintenance scheduling is typically performed with a combination of ad hoc, hand-crafted heuristics with manual scheduling corrections by human domain experts, which is a labor-intensive process that is hard to scale. In this paper, we develop an innovative heterogeneous graph neural network to automatically learn an end-to-end resource scheduling policy. Our approach is fully graph-based with the addition of state summary and decision value nodes that provides a computationally lightweight and nonparametric means to perform dynamic scheduling. We augment our policy optimization procedure to enable robust learning in highly stochastic environments for which typical actor-critic reinforcement learning methods are ill-suited. In consultation with aerospace industry partners, we develop a virtual predictive-maintenance environment for a heterogeneous fleet of aircraft, called AirME. Our approach sets a new state-of-the-art by outperforming conventional, hand-crafted heuristics and baseline learning methods across problem sizes and various objective functions.
Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators
Authors: Gehring Clement , Asai Masataro , Chitnis Rohan , Silver Tom , Kaelbling Leslie , Sohrabi Shirin , Katz Michael
Published: September 2022

Recent advances in reinforcement learning (RL) have led to a growing interest in applying RL to classical planning domains or applying classical planning methods to some complex RL domains. However, the long-horizon goal-based problems found in classical planning lead to sparse rewards for RL, making direct application inefficient. In this paper, we propose to leverage domain-independent heuristic functions commonly used in the classical planning literature to improve the sample efficiency of RL. These classical heuristics act as dense reward generators to alleviate the sparse-rewards issue and enable our RL agent to learn domain-specific value functions as residuals on these heuristics, making learning easier. Correct application of this technique requires consolidating the discounted metric used in RL and the non-discounted metric used in heuristics. We implement the value functions using Neural Logic Machines, a neural network architecture designed for grounded first-order logic inputs. We demonstrate on several classical planning domains that using classical heuristics for RL allows for good sample efficiency compared to sparse-reward RL. We further show that our learned value functions generalize to novel problem instances in the same domain. The source code and the appendix are available at github.com/ibm/pddlrl and arxiv.org/abs/2109.14830.
Verifiable and Compositional Reinforcement Learning Systems
Authors: Neary Cyrus , Verginis Christos , Cubuktepe Murat , Topcu Ufuk
Published: September 2022

We propose a framework for verifiable and compositional reinforcement learning (RL) in which a collection of RL subsystems, each of which learns to accomplish a separate subtask, are composed to achieve an overall task. The framework consists of a high-level model, represented as a parametric Markov decision process (pMDP) which is used to plan and to analyze compositions of subsystems, and of the collection of low-level subsystems themselves. By defining interfaces between the subsystems, the framework enables automatic decompositions of task specifications, e.g., reach a target set of states with a probability of at least 0.95, into individual subtask specifications, i.e. achieve the subsystem's exit conditions with at least some minimum probability, given that its entry conditions are met. This in turn allows for the independent training and testing of the subsystems; if they each learn a policy satisfying the appropriate subtask specification, then their composition is guaranteed to satisfy the overall task specification. Conversely, if the subtask specifications cannot all be satisfied by the learned policies, we present a method, formulated as the problem of finding an optimal set of parameters in the pMDP, to automatically update the subtask specifications to account for the observed shortcomings. The result is an iterative procedure for defining subtask specifications, and for training the subsystems to meet them. As an additional benefit, this procedure allows for particularly challenging or important components of an overall task to be identified automatically, and focused on, during training. Experimental results demonstrate the presented framework's novel capabilities in both discrete and continuous RL settings. A collection of RL subsystems are trained, using proximal policy optimization algorithms, to navigate different portions of a labyrinth environment. A cross-labyrinth task specification is then decomposed into subtask specifications. Challenging portions of the labyrinth are automatically avoided if their corresponding subsystems cannot learn satisfactory policies within allowed training budgets. Unnecessary subsystems are not trained at all. The result is a compositional RL system that efficiently learns to satisfy task specifications.
Resolving Misconceptions about the Plans of Agents via Theory of Mind
Authors: Shvo Maayan , Klassen Toryn Q. , McIlraith Sheila A.
Published: September 2022

For a plan to achieve some goal -- to be valid -- a set of sufficient and necessary conditions must hold. In dynamic settings, agents (including humans) may come to hold false beliefs about these conditions and, by extension, about the validity of their plans or the plans of other agents. Since different agents often believe different things about the world and about the beliefs of other agents, discrepancies may occur between agents' beliefs about the validity of plans. In this work, we explore how agents can use their Theory of Mind to resolve such discrepancies by communicating and/or acting in the environment. We appeal to an epistemic logic framework to allow agents to reason over other agents' nested beliefs, and demonstrate how epistemic planning tools can be used to resolve discrepancies regarding plan validity in a number of domains. Our work shows promise for human decision support as demonstrated by a user study that showcases the ability of our approach to resolve misconceptions held by humans.
Occurrence of fungal strawberry diseases in Central Anatolia Region of Turkey and reactions of some varieties grown widely against the important pathogens
Authors: Sarıgül Ertek Tülin , Uzunok Servet , Özben Süreyya , Tanıker Ülkem , Kurbetli İlker , Maden Salih
Published: September 2022

Strawberry diseases, occurring in Central Anatolia region of Turkey was studied in the years 2013-2016 in the strawberry growing areas of Bartın, Kayseri, Konya, and Zonguldak provinces. Totally 515.5 da strawberry fields in the above mentioned provinces were visited and plants showing disease symptoms were counted and their percentages were determined. Foliage and root diseases were determined by using 383 samples collected from various fields. Seventeen fungal pathogens: namely Alternaria alternata (Fr.) Keissler, Boeremia exigua (Desmazières) Aveskamp, Gruyter & Verkley, Mycosphaerella fragariae (Tul.) Lindau., Colletotrichum acutatum J.H. Simmonds, Colletotrichum gleosporoides (Penz.) Penz., Botrytis cinerea (de Bary) Whetzel, Lasiodiplodia theobromae (Pat.) Griffon & Maubl., Diaporthe actinidae N.F. Sommer & Beraha., Diaporthe eres Nitschke, Didymella americana Saccardo ex Saccardo, Didymella pomorum (Thümen) Aveskamp, Gruyter & Verkley, Diplodia seriata de Notaris, Macrophomina phaseolina (Tassi) Goidanich, Peyronellae prosopidis, Phytophthora cactorum (Lebert & Cohn) J.Schröter, Phytophthora plurivora Jung & Burgess., Phytophthora kelmania were identified. A. alternata, was the most frequently isolated fungus and was obtained from 41.94% of the plant samples while root rot diseases caused by various fungi occurred on 38.13% of the fields. Reactions of twelve strawberry cultivars; (Festival, Kabarla, Sweetann, Alison, Osmanlı, Kara çilek, Tüylü, Sabrina, Rubigem, San Andreas, Albion, Festival, Monterey) were tested against the three most widespread pathogen; A. alternata, M. phaseolina and L. theobromae. With the 86% disease severity, Kabarla was found the most susceptible cv. against A. alternata while Tüylü was the most susceptible cv. against M. phaseolina and L. theobromae. Only one variety, Osmanlı, showed low disease intensity against three of the pathogens.
Investigation of Cardinium endosymbiont in the micro-fauna of granaries and surroundings
Authors: Kaya Tayfun
Published: September 2022

Cardinium is an endosymbiotic bacterium known as a reproductive manipulator in terrestrial ecosystem arthropods. Although Cardinium host species have been identified in recent studies, its prevalence is relatively low, perhaps due to the investigation of fewer taxonomic groups. This study was aimed to investigate Cardinium bacteria in micro-fauna samples in granaries of Turkey, which has suitable conditions for the distribution of arthropods. For this purpose, Stratiolaelaps scimitus (Womersley, 1956) (Acari: Laelapidae), Entomobrya sp. (Collembola: Entomobryidae), and Balaustium sp. (Acari: Actinotrichida) samples were investigated using the Cardinium Clo primer set and compared with the endosymbiont of Bemisia tabaci (Hemiptera: Aleyrodidae). It was determined that the Cardinium sequences from S. scimitus and Entomobrya sp., obtained from granaries and in close contact with each other, were quite similar and could be considered as a monophyletic group. This data can be considered as an indicator of horizontal transfer of Cardinium between these two taxa. However, Balaustium sp. and B. tabaci endosymbiont Cardinium are phylogenetically distant from them. It is thought that this study, which presents the first data on Cardinium endosymbiont in the granary microfauna, will contribute to studies on endosymbiotic bacteria, which are promising reproductive manipulators in the fight against harmful species, especially in agricultural production under pressure due to global warming, decreasing production, and increasing food demand. However, considering the diversity, distribution, and ecological niches of the studied species, more studies are needed to determine the prevalence of Cardinium.
Kuzey Irak’taki sera sebzelerinde kök-ur nematodu türlerinin dağılımı ve yaygınlığı
Authors: HAMAD Hoshang , AYDINLI Gökhan , MENNAN Sevilhan
Published: September 2022

Bu çalışmanın amacı, Kuzey Irak’ın Süleymaniye, Erbil ve Duhok illerindeki sera sebzelerinde kök-ur nematodlarının (Meloidogyne spp.) dağılımının ve yaygınlığının belirlenmesidir. Yüz seksen yedi serada 2018 yılı Kasım ve Aralık aylarında sürvey yapılmıştır. Meloidogyne spp., perineal patternler ve esteraz fenotipi kullanılarak teşhis edilmiştir. Sürvey yapılan seraların %37’sinde Meloidogyne varlığı tespit edilmiş ve yaygınlık Süleymaniye’de %40, Duhok’da %38 ve Erbil’de %34’dür. Meloidogyne ile bulaşık seraların %64’ünde Meloidogyne javanica (Treub, 1885) ve %36’sında Meloidogyne incognita (Kofoid & White, 1919) (Tylenchida: Meloidogynidae) bulunmuştur. Sürvey yapılan ile göre, M. incognita ve M. javanica, sırasıyla Duhok’da seraların %23 ve %15’de, Erbil’de seraların %12 ve %22’de, Süleymaniye’de seraların %10 ve %30’da tespit edilmiştir. Meloidogyne spp., roka, karnabahar, hıyar, patlıcan, marul, domates ve kabakta bulunmuştur. Meloidogyne spp.’nin en yüksek yaygınlığı, çalışma alanındaki seralarda en yoğun yetiştirilen sebzeler olan hıyar (%58) ve domates (%33)’de tespit edilmiştir.
Beauty and the Beast
Authors: Luckhurst Roger
Published: September 2022

The article is a reflection on the category of the beautiful in the work of Professor Hilary Fraser in the context of Victorian Studies at Birkbeck College.
NEA (New Electronic Assistant), un sistema para la co-creatividad computacional musical
Authors: Gómez Marín Daniel
Published: September 2022

Este artículo presenta un sistema para la creatividad computacional colaborativa artística aplicada a la creación musical, llamado New Electronic Assistant (NEA). NEA es un sistema que puede aprender de estilos musicales en formato simbólico, generar piezas siguiendo los estilos aprendidos y transformar sus resultados a través de la interacción en tiempo real. A lo largo del texto, NEA es introducido y enmarcado dentro de los conceptos de la creatividadcolaborativa computacional. Para analizar el desempeño de NEA en entornos reales de co-creación, se realiza un proceso de generación sistemática de piezas musicales, de las que se extraen fragmentos que son usados en un experimento de validación con humanos. Los resultados del experimento sugieren altos niveles de eficiencia en el proceso de co-creación, relacionados con el tiempo de producción de nuevas piezas, la sorpresa expresada porlos participantes del experimento y el valor percibido de las piezas generadas por NEA. El experimento concluye con una sección donde se comentan detalles de las piezas percibidas con mayor valor por los participantes del experimento. Al final del texto se hace una reflexión sobre el rol de los sistemas generativos, que requieren interacción humana, en procesos reales de creación colaborativa.
Refusal in Data Ethics: Re-Imagining the Code Beneath the Code of Computation in the Carceral State
Authors: Barabas Chelsea
Published: September 2022

In spite of a growing interest in ethical approaches to computation, engineers and quantitative researchers are often not equipped with the conceptual tools necessary to interrogate, resist, and reimagine the relationships of power which shape their work. A liberatory vision of computation requires de-centering the data in “data ethics” in favor of cultivating an ethics of encounter that foregrounds the ways computation reproduces structures of domination. This article draws from a rich body of feminist scholarship that explores the liberatory potential of refusal as a practice of generative boundary setting. To refuse is to say no—to reject the default categories, assumptions and problem formulations which so often underpin data-intensive work. But refusal is more than just saying no; it can be a generative and strategic act, one which opens up space to renegotiate the assumptions underlying sociotechnical endeavors. This article explores two complementary modalities of refusal in computation: “refusal as resistance” and “refusal as re-centering the margins.” By exploring these two modes of refusal, the goal of this paper is to provide a vocabulary for identifying and rejecting the ways that sociotechnical systems reinforce dependency on oppressive structural conditions, as well as offer a framework for flexible collective experimentation towards more free futures.
Vision in Ehlers-Danlos syndrome. Role of orthoptics
Authors: Claude Hamonet , Lucette Ducret
Published: September 2022

Ehlers-Danlos disease, usually called Ehlers-Danlos syndrome, is an inherited connective tissue disorder that spares the central nervous system except in its connective component. It is manifested by a set of symptoms and clinical signs which are the expression of the connective tissue’s frailty. Visual manifestations are explained by tissue’s fragility and proprioception disturbance increased by muscle dysfunctions: cornea’s fragility, photophobia, diplopia, blepharospasms, eyelids’ retractions, myopia, and astigmatism. The orthoptist’s role is to help in the prescription of prism correction rather than rehabilitation, which is ineffective. In combination, global effect treatments, in particular for dystonia, and dysautonomia… is essential in this frequent disease, poorly described and therefore very seldom diagnosed. It is transmitted to all the children of an affected person. The objective of this work is to contribute to the diagnosis of Ehlers-Danlos disease more often and more frequently in affected families. The orthoptist's role is very important because the correction of diplopia has functional consequences. Pine-hole glasses may give some relief by filtering out the light that dazzles these patients significantly.
Scholars Raising their Voices Up: An Approximation to Discourses of Hegemony and Resistance in ELT in Colombia
Authors: Mosquera Pérez Jhon Eduardo
Published: September 2022

In the last few years, Colombian ELT scholars have become aware of the importance of discourse for the dissemination of ideologies and agendas. As a result, the number of studies on this area has shown an unprecedented growth. Nevertheless, few investigations have explored and analyzed both sides simultaneously so as to display not only the types of hegemonic discourses that have permeated the field but also those which have recently emerged in response to such a situation. Considering these elements, this paper reports on a qualitative case study carried out with the purpose of analyzing the latest types of hegemonic discourses and discourses of resistance that have taken place in elt in Colombia. After analyzing the data gathered, which consisted of empirical and conceptual articles, as well as linguistic policies emitted by the Colombian Ministry of Education (men), the results showed that discourses revolving around bilingualism (understood as the English-Spanish relationship), identity, and native speakerism have been a recurrent aspect in the Colombian elt field. Yet, findings also suggest that, especially in the last decade, elt scholars have been resisting these discourses by promoting alternative ones that acknowledge initiatives in different areas of knowledge. In light of these aspects, it is recommended to keep resisting and promoting an agenda of decolonization so that alternative discourses, as is the case of those that acknowledge the incorporation of epistemologies that have been historically overlooked, continue gaining traction within the field.
Aortic dissection in childbirth as a cause of maternal mortality
Authors: Babich T. Yu. , Sulima A. N. , Voronaya V. V. , Kalinina N. A.
Published: September 2022

Introduction. Aortic dissection (AD) is a rare and extremely dangerous pathology in obstetrics and gynecology. AD frequency in pregnant women comprises only 14.5 cases per 1 million subjects and leads to maternal death in 30 % cases. AD is characterized by the difficulty in performing differential diagnostics.

Aim: to describe a clinical case demonstrating the difficulties in timely AD diagnostics and treatment during delivery.

Materials and Methods. A rare clinical case of asymptomatic AD resulting in death of the woman in labor is presented after assessing the patient medical history, the conclusion of the pathoanatomical examination and the protocol of the mortality review.

Results. The presented AD clinical case in labor with subtle clinical symptoms demonstrate the extreme difficulty of timely diagnostics of this pathology for practitioners, which inevitably resulted in maternal mortality.

Conclusion. AD during pregnancy and labor is the condition with extremely rare and difficult diagnostics, high probability of maternal and perinatal mortality, which should be known and remembered by obstetrician-gynecologists.
On the issue of pregnancy management in monochorionic twins
Authors: Tsibizova V. I. , Saprykina D. O. , Pervunina T. M. , Bitsadze V. O. , Makatsariya N. A. , Blinov D. V. , Di Renzo G. C.
Published: September 2022

Nowadays, the management and delivery of multiple pregnancies in the era of increased rate of multiple pregnancies is essential in work of an obstetrician-gynecologist. However, it is an ultrasound scanning that becomes a starting point to choose management strategies for multiple pregnancies. Despite tremendous advances in understanding the pathogenesis of developing specific complications, multiple pregnancies still account for a large proportion of perinatal complications. Therefore, continuously improving knowledge in antenatal diagnostics, the principles of prenatal care and optimal approach to delivery is fundamental for reducing morbidity and mortality rate related to multiple pregnancies.
Assessing sexual dysfunction in patients with external genital endometriosis
Authors: Begovich E. , Solopova A. G. , Khlopkova S. V. , Son E. A. , Ungiadze J. Yu. , Idrisova L. E.
Published: September 2022

Introduction. External genital endometriosis (EGE) is a chronic, relapsing disease that affects various aspects of sexual function, the quality of sexual and interpersonal relationships, physical and psycho-emotional health and leads to social maladjustment and anxiety-depressive disorders, resulting in progressively decreased quality of life (QoL).

Aim: to analyze an impact of sexual disorders on QoL of women with painful and painless EGEs.

Materials and Methods. A prospective comparative randomized interventional study involved 160 patients (110 and 50 women with painful and painless EGE, respectively) of reproductive age, with laparoscopically and morphologically verified diagnosis according to the revised American Fertility Society scoring system (R-AFS, 1996). Pain syndrome and sexual dysfunction were assessed using specialized questionnaires: visual analogue scale (VAS), PainDETECT, and Female Sexual Function Index (FSFI). After surgical treatment, 8 patients refused to participate in the study (6 with painful EGE and 2 with painless EGE). Thus, only 152 women passed all stages of the study. The patients were divided into 4 groups depending on the presence and/or absence of pain syndrome and the tactics of the rehabilitation period ("active" or "passive"): group IA consisted of 49 (47.1 %) women with pain and "active" rehabilitation; group IБ – 55 (52.9 %) women with pain syndrome and "passive" rehabilitation tactics; group IIA – 23 (47.9 %) patients without pain syndrome with "active" rehabilitation; group IIБ – 25 (52.1 %) patients without pain syndrome with "passive" rehabilitation tactics.

Results. Women with painful vs. painless EGE were found to have a more severe course of the disease and a markedly decreased quality of sexual life. It was observed that patients with "active" rehabilitation tactics (groups IA and IIA) had intensity of the pain syndrome and the neuropathic pain component significantly decreased, improved parameters of the quality of sexual life, sexual desire and sexual excitability compared to patients with "passive" rehabilitation tactics applied with the measures within the framework of the National Clinical Guidelines (groups IБ and IIБ).

Conclusion. A significantly decreased quality of sexual life of EGE patients indicates a need for assessing and correcting sexual disorders as well as introducing early onset of rehabilitation measures by using a multidisciplinary approach.
A role of genetic studies in aggravated obstetric history: a clinical observation
Authors: Dobrokhotova Ju. E.
Published: September 2022

Primary health care specialists play one of the defining roles in shaping treatment and diagnostic strategy by attracting colleagues from related specialties, including those dealing with medical genetic counseling. At the current stage, additional examinations allow to implement a patient-oriented integrated approach. Genetic studies can substantially improve quality of diagnostics and should be more actively used in primary care. This confirms the clinical observation using extended diagnostic tactics in the patient with non-specific complaints and a burdened obstetric history.
Professional values and educational needs in mental health professionals: survey results
Authors: Kibitov Andrey A. , Chumakov Egor M. , Nechaeva Anastasia I. , Sorokin Mikhail Y. , Petrova Nataliia N. , Vetrova Marina V.
Published: September 2022

BACKGROUND: Exploring the professional values and educational needs of future and practicing mental health specialists is required to develop effective measures aiming at improving their skills and interest in their work.
AIM: Our aim was to explore professional values and educational needs of mental health specialists in Russia.
METHODS: We conducted a survey that captured socio-demographic data, professional characteristics, professional values, and educational needs. Fisher's exact test, logistic regression, and the k-means cluster analysis were used in our statistical analysis.
RESULTS: The survey included 133 participants, 71% of whom had completed their postgraduate education. The following items were mentioned as important professional values by the respondents: Job opportunities, Stimulation of intellectual activity, and Work-life balance. The most popular options for educational activities were English language (63.4%), the principles of evidence-based medicine (63.4%), and developing skills for conducting scientific research (59.4%).
In comparison with practicing specialists, respondents who had not yet completed their postgraduate education were more interested in developing their curriculum vitae (39% vs. 60%, p=0.044) and communication skills (49% vs. 77%, p=0.0048). Compared to male respondents, female respondents were more interested in developing skills in conducting scientific research (47 vs. 70%, p=0.0165).
A cluster analysis showed that specialists who attached more importance to almost all professional values, in comparison to those who did not, were more interested in activities aimed at developing their research skills (64% vs. 41%, p=0.0287), learning about the principles of evidence-based medicine (70% vs. 41%, p=0.0063), and participating in journal clubs (39% vs. 11%, p=0.0193).
CONCLUSION: The present study suggests that job opportunities, intellectual stimulation, and work-life balance are the most important professional values for future and practicing mental health specialists. These findings might be used as a basis for developing educational activities for mental health specialists.
The Mechanisms of Tsunami Amplification and the Earthquake Source of the 2021M 7 Acapulco, Mexico, Earthquake
Authors: Melgar Diego , Ruiz-Angulo Angel , Crowell Brendan W. , Fielding Eric J. , Solano-Hernandez Ericka A.
Published: September 2022

We present a slip model for the 2021 M 7 Acapulco, Mexico, earthquake produced by inversion of strong motion, Global Navigation Satellite Systems, tide gauge, and Interferometric Synthetic Aperture Radar data. The earthquake occurs within the Guerrero gap, identified as a region of concern for its seismogenic potential and paucity of large events. We find that rupture was compact, constrained to depths between 10 and 20 km, and consistent of two main slip patches. The slip model leaves a broad swath of the megathrust unbroken, and, whether the event signals a reactivation of large earthquakes in the region remains unknown. We find that tide gauge recordings inside Acapulco Bay for the M 7 1962 earthquake and the 2021 event are strikingly similar, thus we interpret this as weak evidence that 2021 is a repeat of 1962. We also produce a high-resolution hydrodynamic model of the resulting tsunami using the slip model as initial condition and place special emphasis in understanding the long duration (∼17 hr) of waves inside the bay. We find that simple bay resonance alone does not account for the features of the event. Rather it is a complex interaction with shelf modes and edge waves that continuously re-excite the bay resonance that leads to the protracted tsunami disturbances. Furthermore, we find that significant currents in excess of 1 m/s occur in localized portions of the bay even when wave amplitudes remain small.