15,793 research outputs found
Modeling Group Dynamics for Personalized Robot-Mediated Interactions
The field of human-human-robot interaction (HHRI) uses social robots to
positively influence how humans interact with each other. This objective
requires models of human understanding that consider multiple humans in an
interaction as a collective entity and represent the group dynamics that exist
within it. Understanding group dynamics is important because these can
influence the behaviors, attitudes, and opinions of each individual within the
group, as well as the group as a whole. Such an understanding is also useful
when personalizing an interaction between a robot and the humans in its
environment, where a group-level model can facilitate the design of robot
behaviors that are tailored to a given group, the dynamics that exist within
it, and the specific needs and preferences of the individual interactants. In
this paper, we highlight the need for group-level models of human understanding
in human-human-robot interaction research and how these can be useful in
developing personalization techniques. We survey existing models of group
dynamics and categorize them into models of social dominance, affect, social
cohesion, and conflict resolution. We highlight the important features these
models utilize, evaluate their potential to capture interpersonal aspects of a
social interaction, and highlight their value for personalization techniques.
Finally, we identify directions for future work, and make a case for models of
relational affect as an approach that can better capture group-level
understanding of human-human interactions and be useful in personalizing
human-human-robot interactions
CARLA+: An Evolution of the CARLA Simulator for Complex Environment Using a Probabilistic Graphical Model
In an urban and uncontrolled environment, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, and unprecedented dynamic events makes it challenging for the classical autonomous vehicle to navigate the traffic safely. Therefore, the realization of collaborative autonomous driving has the potential to improve road safety and traffic efficiency. However, an obvious challenge in this regard is how to define, model, and simulate the environment that captures the dynamics of a complex and urban environment. Therefore, in this research, we first define the dynamics of the envisioned environment, where we capture the dynamics relevant to the complex urban environment, specifically, highlighting the challenges that are unaddressed and are within the scope of collaborative autonomous driving. To this end, we model the dynamic urban environment leveraging a probabilistic graphical model (PGM). To develop the proposed solution, a realistic simulation environment is required. There are a number of simulatorsâCARLA (Car Learning to Act), one of the prominent ones, provides rich features and environment; however, it still fails on a few fronts, for example, it cannot fully capture the complexity of an urban environment. Moreover, the classical CARLA mainly relies on manual code and multiple conditional statements, and it provides no pre-defined way to do things automatically based on the dynamic simulation environment. Hence, there is an urgent need to extend the off-the-shelf CARLA with more sophisticated settings that can model the required dynamics. In this regard, we comprehensively design, develop, and implement an extension of a classical CARLA referred to as CARLA+ for the complex environment by integrating the PGM framework. It provides a unified framework to automate the behavior of different actors leveraging PGMs. Instead of manually catering to each condition, CARLA+ enables the user to automate the modeling of different dynamics of the environment. Therefore, to validate the proposed CARLA+, experiments with different settings are designed and conducted. The experimental results demonstrate that CARLA+ is flexible enough to allow users to model various scenarios, ranging from simple controlled models to complex models learned directly from real-world data. In the future, we plan to extend CARLA+ by allowing for more configurable parameters and more flexibility on the type of probabilistic networks and models one can choose. The open-source code of CARLA+ is made publicly available for researchers
Modular lifelong machine learning
Deep learning has drastically improved the state-of-the-art in many important fields, including computer vision and natural language processing (LeCun et al., 2015). However, it is expensive to train a deep neural network on a machine learning problem. The overall training cost further increases when one wants to solve additional problems. Lifelong machine learning (LML) develops algorithms that aim to efficiently learn to solve a sequence of problems, which become available one at a time. New problems are solved with less resources by transferring previously learned knowledge. At the same time, an LML algorithm needs to retain good performance on all encountered problems, thus avoiding catastrophic forgetting. Current approaches do not possess all the desired properties of an LML algorithm. First, they primarily focus on preventing catastrophic forgetting (Diaz-Rodriguez et al., 2018; Delange et al., 2021). As a result, they neglect some knowledge transfer properties. Furthermore, they assume that all problems in a sequence share the same input space. Finally, scaling these methods to a large sequence of problems remains a challenge.
Modular approaches to deep learning decompose a deep neural network into sub-networks, referred to as modules. Each module can then be trained to perform an atomic transformation, specialised in processing a distinct subset of inputs. This modular approach to storing knowledge makes it easy to only reuse the subset of modules which are useful for the task at hand.
This thesis introduces a line of research which demonstrates the merits of a modular approach to lifelong machine learning, and its ability to address the aforementioned shortcomings of other methods. Compared to previous work, we show that a modular approach can be used to achieve more LML properties than previously demonstrated. Furthermore, we develop tools which allow modular LML algorithms to scale in order to retain said properties on longer sequences of problems.
First, we introduce HOUDINI, a neurosymbolic framework for modular LML. HOUDINI represents modular deep neural networks as functional programs and accumulates a library of pre-trained modules over a sequence of problems. Given a new problem, we use program synthesis to select a suitable neural architecture, as well as a high-performing combination of pre-trained and new modules. We show that our approach has most of the properties desired from an LML algorithm. Notably, it can perform forward transfer, avoid negative transfer and prevent catastrophic forgetting, even across problems with disparate input domains and problems which require different neural architectures.
Second, we produce a modular LML algorithm which retains the properties of HOUDINI but can also scale to longer sequences of problems. To this end, we fix the choice of a neural architecture and introduce a probabilistic search framework, PICLE, for searching through different module combinations. To apply PICLE, we introduce two probabilistic models over neural modules which allows us to efficiently identify promising module combinations.
Third, we phrase the search over module combinations in modular LML as black-box optimisation, which allows one to make use of methods from the setting of hyperparameter optimisation (HPO). We then develop a new HPO method which marries a multi-fidelity approach with model-based optimisation. We demonstrate that this leads to improvement in anytime performance in the HPO setting and discuss how this can in turn be used to augment modular LML methods.
Overall, this thesis identifies a number of important LML properties, which have not all been attained in past methods, and presents an LML algorithm which can achieve all of them, apart from backward transfer
Japanese Expert Teachers' Understanding of the Application of Rhythm in Judo: a New Pedagogy
Aim
The aim of this research is to understand the application of rhythm in judo through the experience of expert Japanese coaches.
Background
Scientists and experienced coaches agree rhythm is an important skill in peopleâs everyday life. There is currently no research that investigates the importance of rhythm in judo. People with a highly developed sense of rhythm, move properly, breathe properly, or begin and finish work at the right time. Where sport is concerned, motion and dance can play an important role not only in the improvement of performance, but also in the reduction, or even prevention of, injuries. Those who are naturally musically inclined (have a musical ear) may find they can improve their technique faster than others, and this is something that, by investigating the way expert coaches understand the application of rhythm in judo, this research seeks to understand.
As Lange, (1970) stated, factors of movement are âweight, space, time, and flow on the background of the general flux of movement in proportional arrangementsâ (Bradley, 2008; Selioni, 2013; Youngerman, 1976), therefore, this research will investigate the interaction of body and mind. Dance training as well as judo are somatic experiences that have as their ultimate goal the attainment of a skilled body. With quality training an athlete gains an increased awareness of their body which leads to better control of movement and is very important for judo athletes. This training is found in Japanese kabuki dance (Hahn, 2007), the Greek syrtaki dance (Zografou & Pateraki, 2007), and in walking techniques used in the traditional and Olympic sports of Japanese judo and Greek wrestling.
Methods
Interpretative phenomenological analysis (IPA) was the most suitable data analysis approach for this study for a number of reasons, mainly because it was considered to most closely reflect the author's realist epistemological view. The idiographic approach and framework, particularly on IPA, was regarded as a useful framework in which the current topic could meaningfully be explored.
As this study is one of the first to explore this new thematic area, IPA was the preferred approach to address the goal of providing a detailed account of the expertâs experience. Therefore, semi-structured interviews were used as a data source. This is the most conventional form of data collection using IPA and most closely reflects the researcher-participant relationship. Semi-structured interviews provide considerable flexibility by allowing the researcher to be guided by the phenomena of interest to the participant.
In this study, purposive sampling was achieved using inclusion criteria pertaining to the research question.
Using the ranking system criteria based on the belt in combination with age employed by the International Judo Federation (IJF) and Kodokan Judo Institute, six expert coaches of forty years old and over with a minimum belt rank of 6th dan were selected as a sample.
Results
Both interviews and the codification process contributed to new findings regarding the application of rhythm to judo, and judo itself as a pedagogical tool.
The diagrammatic model can be considered a 'guideline' to the phenomena deemed most significant. The personal significance of rhythm in judo was evidenced by the frequency with which the interviewees naturally referred to it during the interviews. A number of interviewees said that it was important for rhythm to be second nature. Rhythm was also described as an integrated and representative
element in the context of training. This framework was seen as essential in providing the reader with a contextualised understanding of the phenomena considered most important for the current research. Interviewees reported various motives for employing training in rhythm such as faster technical development, better attack/defence, fitness, speed, skills acquisition, personal and spiritual growth, competition results.
Conclusions
This study offers first-hand accounts from professional coaches of a previously unknown phenomena, namely the use of rhythm in judo, and sheds insight on how judo experts understand rhythm in terms of training, competition, and personal growth. These findings suggest that outside of training, coaches play an important role in teaching, mentoring, and leading students. In conclusion, the research revealed four important points which form the basis of a new method of teaching judo: pedagogy, skills, rhythm and movement
A review of combined neuromodulation and physical therapy interventions for enhanced neurorehabilitation
Rehabilitation approaches for individuals with neurologic conditions have increasingly shifted toward promoting neuroplasticity for enhanced recovery and restoration of function. This review focuses on exercise strategies and non-invasive neuromodulation techniques that target neuroplasticity, including transcranial magnetic stimulation (TMS), vagus nerve stimulation (VNS), and peripheral nerve stimulation (PNS). We have chosen to focus on non-invasive neuromodulation techniques due to their greater potential for integration into routine clinical practice. We explore and discuss the application of these interventional strategies in four neurological conditions that are frequently encountered in rehabilitation settings: Parkinsonâs Disease (PD), Traumatic Brain Injury (TBI), stroke, and Spinal Cord Injury (SCI). Additionally, we discuss the potential benefits of combining non-invasive neuromodulation with rehabilitation, which has shown promise in accelerating recovery. Our review identifies studies that demonstrate enhanced recovery through combined exercise and non-invasive neuromodulation in the selected patient populations. We primarily focus on the motor aspects of rehabilitation, but also briefly address non-motor impacts of these conditions. Additionally, we identify the gaps in current literature and barriers to implementation of combined approaches into clinical practice. We highlight areas needing further research and suggest avenues for future investigation, aiming to enhance the personalization of the unique neuroplastic responses associated with each condition. This review serves as a resource for rehabilitation professionals and researchers seeking a comprehensive understanding of neuroplastic exercise interventions and non-invasive neuromodulation techniques tailored for specific diseases and diagnoses
Contextual Pre-Planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning
Recent studies show that deep reinforcement learning (DRL) agents tend to
overfit to the task on which they were trained and fail to adapt to minor
environment changes. To expedite learning when transferring to unseen tasks, we
propose a novel approach to representing the current task using reward machines
(RM), state machine abstractions that induce subtasks based on the current
task's rewards and dynamics. Our method provides agents with symbolic
representations of optimal transitions from their current abstract state and
rewards them for achieving these transitions. These representations are shared
across tasks, allowing agents to exploit knowledge of previously encountered
symbols and transitions, thus enhancing transfer. Our empirical evaluation
shows that our representations improve sample efficiency and few-shot transfer
in a variety of domains.Comment: IJCAI Workshop on Planning and Reinforcement Learning, 202
Research progress on deep learning in magnetic resonance imagingâbased diagnosis and treatment of prostate cancer: a review on the current status and perspectives
Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Virtual Stiffness: A Novel Biomechanical Approach to Estimate Limb Stiffness of a Multi-Muscle and Multi-Joint System
In recent years, different groups have developed algorithms to control the stiffness of a robotic device through the electromyographic activity collected from a human operator. However, the approaches proposed so far require an initial calibration, have a complex subject-specific muscle model, or consider the activity of only a few pairs of antagonist muscles. This study described and tested an approach based on a biomechanical model to estimate the limb stiffness of a multi-joint, multi-muscle system from muscle activations. The âvirtual stiffnessâ method approximates the generated stiffness as the stiffness due to the component of the muscle-activation vector that does not generate any endpoint force. Such a component is calculated by projecting the vector of muscle activations, estimated from the electromyographic signals, onto the null space of the linear mapping of muscle activations onto the endpoint force. The proposed method was tested by using an upper-limb model made of two joints and six Hill-type muscles and data collected during an isometric force-generation task performed with the upper limb. The null-space projection of the muscle-activation vector approximated the major axis of the stiffness ellipse or ellipsoid. The model provides a good approximation of the voluntary stiffening performed by participants that could be directly implemented in wearable myoelectric controlled devices that estimate, in real-time, the endpoint forces, or endpoint movement, from the mapping between muscle activation and force, without any additional calibrations
Bibliometric study of immunotherapy for hepatocellular carcinoma
BackgroundHepatocellular carcinoma (HCC), recognized as a significant global health concern, ranks as the sixth most prevalent form of cancer and is the third leading cause of cancer-associated mortality. Over half of HCC patients are diagnosed at advanced stages, an unfortunate phenomenon primarily attributed to the liverâs robust compensatory mechanisms. Given the limited availability of donor livers, existing clinical surgical approaches have yet to provide universally applicable treatment strategies offering substantial prognostic improvement for late-stage cancer. Although the past few decades have witnessed significant advancements in chemotherapy and targeted therapy for HCC, the emergence of drug resistance poses a substantial impediment to their successful execution. Furthermore, issues such as diminished quality of life post-treatment and high treatment costs warrant critical attention. Consequently, the imperative for an effective treatment strategy for advanced liver cancer is unequivocal. In recent years, notable progress in the development and application of immunotherapy has sparked a revolution in advanced liver cancer treatment. This study aims to elucidate a more comprehensive understanding of the current landscape, knowledge framework, research focal points, and nascent breakthrough trends in the domain of immunotherapy for hepatocellular carcinoma via bibliometric analysis.MethodOur study involved conducting a comprehensive literature search spanning from 1999 through December 31, 2022, by utilizing the Science Citation Index Expanded (SCI-Expanded) database. Our aim was to amass all the papers and reviews related to immunotherapy for hepatocellular carcinoma. Our search strategy yielded a total of 4,486 papers. After exclusion of self-citations, we focused our analysis on 68,925 references. These references were cited 119,523 times (excluding 97,941 self-citations), boasting an average citation frequency of 26.64 times per paper, and achieved an h-index of 135. We employed analytical software tools like Citespace and VOSviewer to perform an intricate analysis of the amassed literature, covering various aspects, including geographical location, research institutions, publishing journals, authors, references, and keywords. Our method incorporated timeline analysis, burst detection, and co-occurrence analysis. The application of these tools facilitated a thorough evaluation of research hotspots, knowledge structure, and emerging advancements within the sphere of immunotherapy for hepatocellular carcinoma.ResultsOur bibliometric analysis disclosed a noteworthy escalation in the number of publications in the realm of hepatocellular carcinoma immunotherapy during the years 2021-2022, surpassing the aggregate number of papers published in the preceding decade (2011â2020). This surge underscores a sharp upturn in research interest within this field. Additionally, the research hotspot in hepatocellular carcinoma immunotherapy has perceptibly deviated from the preceding decadeâs trends. In terms of geographical distribution, China emerged as the leading country, producing 50.08% of the total publications. This was followed by the United States, with 963 papers, and Japan, contributing 335 papers. Among research institutions, Sun Yat-sen University was the most prolific, while Tim F. Greten stood out as the most published author with 42 papers to his credit. A co-reference network examination uncovered a shift in research emphasis within the field of hepatocellular carcinoma immunotherapy, highlighting the evolving nature of this important area of studyConclusionOur bibliometric study highlights the significant evolution and growth in HCC immunotherapy research over the past two decades. Looking ahead, research will focus on improving the microenvironment post-drug resistance from immune combination therapy, harnessing adoptive cellular immunity (as CAR-T), subclassify the population and developing new tumor markers. Incorporation of technologies such as nanotechnology, microbiology, and gene editing will further advance HCC treatments. This progressive trajectory in the field promises a brighter future for individuals suffering from HCC
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