3 Materially Optimal Behavior

The internal cushioning systems of hydraulic linear actuators avoid mechanical shocks at the end of their stroke. The design where the piston with perimeter grooves regulates the flow by standing in front of the outlet port has been investigated. First, a bond graph dynamic model has been developed, including the flow throughout the internal cushion design, characterized in detail by computational fluid-dynamic simulation. Following this, the radial movement of the piston and the fluid-dynamic coefficients, experimentally validated, are integrated into the dynamic model. The registered radial movement is in coherence with the significant drag force estimated in the CFD simulation, generated by the flow through the grooves, where the laminar flow regime predominates. Ultimately, the model aims to predict the behavior of the cushioning during the movement of the arm of an excavator. The analytical model developed predicts the performance of the cushioning system, in coherence with empirical results. There is an optimal behavior, highly influenced by the mechanical stress conditions of the system, subject to a compromise between an increasing section of the grooves and an optimization of the radial gap.

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Aggregation of cohorts for histopathological diagnosis with deep morphological analysis

Scientific Reports ◽

10.1038/s41598-021-82642-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jeonghyuk Park ◽

Yul Ri Chung ◽

Seo Taek Kong ◽

Yeong Won Kim ◽

Hyunho Park ◽

...

Keyword(s):

Cancer Detection ◽

Morphological Analysis ◽

Deep Neural Networks ◽

Large Datasets ◽

Histopathological Diagnosis ◽

Single Model ◽

Trade Off ◽

Detection Model ◽

Optimal Behavior ◽

The Cost

AbstractThere have been substantial efforts in using deep learning (DL) to diagnose cancer from digital images of pathology slides. Existing algorithms typically operate by training deep neural networks either specialized in specific cohorts or an aggregate of all cohorts when there are only a few images available for the target cohort. A trade-off between decreasing the number of models and their cancer detection performance was evident in our experiments with The Cancer Genomic Atlas dataset, with the former approach achieving higher performance at the cost of having to acquire large datasets from the cohort of interest. Constructing annotated datasets for individual cohorts is extremely time-consuming, with the acquisition cost of such datasets growing linearly with the number of cohorts. Another issue associated with developing cohort-specific models is the difficulty of maintenance: all cohort-specific models may need to be adjusted when a new DL algorithm is to be used, where training even a single model may require a non-negligible amount of computation, or when more data is added to some cohorts. In resolving the sub-optimal behavior of a universal cancer detection model trained on an aggregate of cohorts, we investigated how cohorts can be grouped to augment a dataset without increasing the number of models linearly with the number of cohorts. This study introduces several metrics which measure the morphological similarities between cohort pairs and demonstrates how the metrics can be used to control the trade-off between performance and the number of models.

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Comparing quantum hybrid reinforcement learning to classical methods

Human-Intelligent Systems Integration ◽

10.1007/s42454-021-00025-3 ◽

2021 ◽

Author(s):

Maximilian Moll ◽

Leonhard Kunczik

Keyword(s):

Reinforcement Learning ◽

Quantum Circuits ◽

Memory Usage ◽

Computational Power ◽

Classical Domain ◽

Q Learning ◽

Optimal Behavior ◽

Computational Speed ◽

Complex Decision ◽

Hybrid Reinforcement

AbstractIn recent history, reinforcement learning (RL) proved its capability by solving complex decision problems by mastering several games. Increased computational power and the advances in approximation with neural networks (NN) paved the path to RL’s successful applications. Even though RL can tackle more complex problems nowadays, it still relies on computational power and runtime. Quantum computing promises to solve these issues by its capability to encode information and the potential quadratic speedup in runtime. We compare tabular Q-learning and Q-learning using either a quantum or a classical approximation architecture on the frozen lake problem. Furthermore, the three algorithms are analyzed in terms of iterations until convergence to the optimal behavior, memory usage, and runtime. Within the paper, NNs are utilized for approximation in the classical domain, while in the quantum domain variational quantum circuits, as a quantum hybrid approximation method, have been used. Our simulations show that a quantum approximator is beneficial in terms of memory usage and provides a better sample complexity than NNs; however, it still lacks the computational speed to be competitive.

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Reinforcement Learning Approaches in Social Robotics

Sensors ◽

10.3390/s21041292 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1292

Author(s):

Neziha Akalin ◽

Amy Loutfi

Keyword(s):

Reinforcement Learning ◽

Real World ◽

Social Robotics ◽

Research Field ◽

Social Robots ◽

Learning Approaches ◽

Reward Function ◽

Optimal Behavior ◽

Learning Challenges ◽

Starting Point

This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field.

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Evaluation-driven design of a robust coreference resolution system

Natural Language Engineering ◽

10.1017/s135132490300319x ◽

2003 ◽

Vol 9 (3) ◽

pp. 281-306 ◽

Cited By ~ 1

Author(s):

ANDREI POPESCU-BELIS

Keyword(s):

Working Memory ◽

Theoretical Analysis ◽

Coreference Resolution ◽

Knowledge Sources ◽

Narrative Texts ◽

Data Annotation ◽

Evaluation Measures ◽

Optimal Behavior ◽

Resolution System

In this paper, we describe a system for coreference resolution and emphasize the role of evaluation for its design. The goal of the system is to group referring expressions (identified beforehand in narrative texts) into sets of coreferring expressions that correspond to discourse entities. Several knowledge sources are distinguished, such as referential compatibility between a referring expression and a discourse entity, activation factors for discourse entities, size of working memory, or meta-rules for the creation of discourse entities. For each of them, the theoretical analysis of its relevance is compared to scores obtained through evaluation. After looping through all knowledge sources, an optimal behavior is chosen, then evaluated on test data. The paper also discusses evaluation measures as well as data annotation, and compares the present approach to others in the field.

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The Role of the Conclusions of the Council of Judges of the Russian Federation in the Prevention of the Emergence and Development of Conflicts of Legal Interests in the Judicial System of Russia

RUSSIAN JUSTICE ◽

10.37399/issn2072-909x.2020.12.56-66 ◽

2020 ◽

Vol 12 ◽

pp. 56-66

Author(s):

E. V. Ryabtseva ◽

Keyword(s):

Russian Federation ◽

Judicial System ◽

Structural Elements ◽

Consistent System ◽

Optimal Behavior ◽

Regulatory Impact ◽

The Russian Federation ◽

The Impact ◽

Individual Regulation

The growing role of the judicial community in reforming the judicial system actualizes the scientific problems of law enforcement associated with understanding the essence of the regulatory impact of the Councils of Judges of the Russian Federation as a body of the judicial community to prevent the emergence of conflicts of legal interests in judicial activity. The purpose of the research is to theoretically substantiate the essence of individual regulation of conflicts of legal interests by the Council of Judges of the Russian Federation, aimed at optimizing its activities to combat corruption. The worldview and methodological basis were the works of theoretical scholars and their methods of integrative understanding of law to substantiate the impact of the Council of Judges of the Russian Federation on judicial activity through individual regulation. The conclusion is substantiated that the activities of the Commission of the Council of Judges of the Russian Federation on Ethics, related to the drawing up of opinions on the assessment of conflicts of legal interests and other corruption risks for both acting judges and retired judges, is an individual regulation of legal relations through: interpretation of law; overcoming gaps and conflicts in the law; individuali zation of rights, etc. The content of the interpretation of law by the Commission of the Council of Judges of the Russian Federation on Ethics is: the application of certain norms of both international and national law in a specific legal relationship when assessing conflicts of legal interests among judges through a systematic interpretation of the norms of law as a system of elements, defining its role in law, identifying other norms, as well as the principles of law; interpretation of the principles and norms of law, through the legal-logical interpretation of a normative act as logically interconnected structural elements of a single, internally agreed and consistent system of principles and norms of law, when deciding on the presence of conflicts of legal interests in the activities of judges, etc. The paper substantiates that in relation to conflicts of legal interests, individualization should be aimed at determining by the Council of Judges of the Russian Federation typical situations of such conflicts for their correct assessment and development of recommendations related to the optimal behavior of judges, when circumstances arise that lead to conflicts of legal interests.

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