Multidimensional Action Research Articles

Simplified neural architecture for efficient human motion prediction in human-robot interaction

Human motion prediction is essential for safe and effective human-robot interaction, but modeling the intricate spatio-temporal dynamics inherent in human movement remains challenging. While recent methods have advanced motion prediction accuracy, they rely on complex neural architectures such as Graph Convolutional Networks and Recurrent Neural Networks, demanding extensive hyperparameter tuning. To overcome this limitation, we propose a streamlined multi-stage layer incremental perceptron (MSLP) architecture that achieves competitive results with far fewer parameters, improving efficiency. The MSLP eschews the complexity of prevalent networks and instead takes a stepped refinement approach to predict intricate motions using a lightweight model. This simplified yet effective design enables nuanced learning of spatio-temporal relationships without exhaustive tuning. Specifically, the MSLP incorporates two key components: a multi-channel feature extraction and enhancement block (MFE-block) and a temporal feature extraction module (TFE-block). The MFE-block strengthens the representation of each action node by integrating multi-dimensional action features. The TFE-block then captures the contextual relationships between actions over time. Together, the MFE-block and TFE-block allow the MSLP to model the complex spatial and temporal dynamics of human movement using a streamlined architecture and minimal parameter tuning. When evaluated on established datasets including Human3.6 M, CMU, 3DPW, and AMASS, the proposed MSLP method achieves improved accuracy gains of 16.7%-67.0% over existing state-of-the-art techniques. Additionally, the MSLP significantly reduces the number of parameters by 35.7%-99.5% compared to prior architectures. We find that the proposed MSLP significantly improves both long-term prediction and generalization capabilities of the model.

When Spectrum Sharing in Cognitive Networks Meets Deep Reinforcement Learning: Architecture, Fundamentals and Challenges

Next-generation wireless networks require the integration of cognitive networks (CNs) and decision-making techniques to improve the spectrum efficiency. The conventional spectrum sharing schemes require full channel state information of CNs and cannot satisfy the low-latency requirement of nextgeneration wireless networks. Artificial intelligence has shown its high potential to perform decision-making and improve resource utilization efficiency. Hence, we propose a multi-agent reinforcement learning (MARL)-based scheme for spectrum sharing, which is a promising self-decision technique in highly dynamic and complex wireless networks. We analyze several key challenges when MARL is applied to spectrum sharing, such as multi-objective function formulation, multi-dimensional action space, and partial channel state information. Then, we propose efficient solutions and apply the explainable DRL to improve the convergence efficiency in spectrum sharing. The proposed architecture, fundamentals, and challenges provide a clear vision for MARL in CNs.

Prevalence and determinants of school bullying in Qatar: a cross-sectional study

BackgroundSchool bullying is a wide-spread phenomenon that manifests in various forms. It has both short-term and long-term devastating consequences on physical, mental and social wellbeing. The Middle East and North Africa (MENA) region, including Qatar, has a relatively high prevalence of school bullying. This research aims at identifying the prevalence of bullying, particularly unsafe environments were bullying takes place, and its attributes at schools in Qatar.MethodsIn a cross-sectional study, 980 students from 10 schools in Qatar completed an anonymous self-completion standardized questionnaire to assess the different aspects of bullying from school students’ point of view.ResultsThe prevalence of bullying victimization and perpetration was found to be 41.0% and 31.7% among school students in Qatar, respectively. Classroom (67.5%) and hallways (64.8%) were the most frequently indicated environments of bullying whereas library was the least indicated one (28.3%). Verbal bullying was the most used type of bullying by students. Overall, students in Qatar believe that bullying is considerably a significant issue at their schools, yet schools are safe place for them to be in. Gender, age, ethnicity, school grade and years living in Qatar showed significant differences among the students.ConclusionSchool bullying is a serious, yet a manageable global problem. Our findings re-demonstrated the alarming high prevalence of school bullying in Qatar, highlighted student related and school related factors which have implications for future multidimensional action and research and recommended measures to foster safety at school.

In search of an international multidimensional action plan for second victim support: a narrative review

BackgroundInsights around second victims (SV) and patient safety has been growing over time. An overview of the available evidence is lacking. This review aims to describe (i) the impact a patient safety incident can have and (ii) how healthcare professionals can be supported in the aftermath of a patient safety incident.MethodsA literature search in Medline, EMBASE and CINAHL was performed between 1 and 2010 and 26 November 2020 with studies on SV as inclusion criteria. To be included in this review the studies must include healthcare professionals involved in the aftermath of a patient safety incident.ResultsIn total 104 studies were included. SVs can suffer from both psychosocial (negative and positive), professional and physical reactions. Support can be provided at five levels. The first level is prevention (on individual and organizational level) referring to measures taken before a patient safety incident happens. The other four levels focus on providing support in the aftermath of a patient safety incident, such as self-care of individuals and/or team, support by peers and triage, structured support by an expert in the field (professional support) and structured clinical support.ConclusionThe impact of a patient safety incident on healthcare professionals is broad and diverse. Support programs should be organized at five levels, starting with preventive actions followed by self-care, support by peers, structured professional support and clinical support. This multilevel approach can now be translated in different countries, networks and organizations based on their own culture, support history, structure and legal context. Next to this, they should also include the stage of recovery in which the healthcare professional is located in.

The 1450-nm Diode Laser Reduces Redness and Porphyrin Density: An Image-Based, Patient-Oriented Appraisal.

Acne vulgaris remains the leading dermatological condition. The efficacy of laser treatment has been supported by many clinical studies, but studies investigating its multidimensional action are lacking. To comprehensively investigate the efficacy of 1450-nm diode laser treatment in patients with inflammatory acne and provide objective and subjective data for doctors in clinical practice. This retrospective study included patients with inflammatory acne lesions who underwent three courses of 1450-nm diode laser treatment between October 2019 and August 2020. Facial surface analysis was performed via objective computer assessments using the Canfield VISIA imaging system. Post-treatment subjective assessments were retrieved and analyzed using the clinical global impression-improvement index (CGI-I) and patient global impression of improvement scales (PGI-I). The final analysis included 20 patients. The changes in the porphyrin VISIA system scores demonstrated significant improvement, with median scores being 35.83, 48.83, and 54.83, respectively. The changes in the red area VISIA scores also showed improvement, with the median scores being 48, 50.33, and 58.83, respectively. The average CGI-I scale scores were 2.2 ± 1.01, 1.70 ± 0.80, and 1.50 ± 0.76, respectively (p = 0.001), and the average PGI-I scale scores were 3.10 ± 0.85, 3.10 ± 0.97, and 3.05 ± 0.95, respectively (p = 0.727), with no significant changes observed in sebum production. The present study is the first to provide objective and subjective evidence proving that the 1450-nm diode laser can reduce inflammatory acne lesions.

Comparing information in general monotone decision problems

I study the value of information in monotone decision problems with potentially multidimensional action spaces. As a criterion for comparing information structures, I develop a condition called monotone quasi-garbling, which involves adding reversely monotone noise to an existing information structure. Specifically, this noise is more likely to return a higher signal in a lower state and a lower signal in a higher state. I show that monotone quasi-garbling is a necessary and sufficient condition for decision makers to obtain a higher ex-ante expected payoff. This new criterion refines the garbling condition by Blackwell (1951, 1953) and is equivalent to the accuracy condition by Lehmann (1988) under the monotone likelihood ratio property. To illustrate, I apply the result to problems in nonlinear monopoly pricing and optimal insurance.

Evidence-Informed, Interdisciplinary, Multidimensional Action Plan to Advance Overactive Bladder Research and Treatment Initiatives: Erratum.

Evidence-Informed, Interdisciplinary, Multidimensional Action Plan to Advance Overactive Bladder Research and Treatment Initiatives: Erratum.

Medicinal plants in nutricosmetics

Nutricosmetics are nutritional supplements that affect the condition of the skin and its appendages. They are available as ampoules, capsules, or tablets which improve skin elasticity and tone, as well as increase its hydration. Supplements, that prevent excessive hair loss and support the proper growth of nails, are also classified as nutricosmetics. Many supplements contain compounds of plant origin that are rich in active ingredients and exhibit a beneficial effect on the physiology of the skin and its appendages. The aim of this study was to assess the health-promoting effects of selected species of medicinal plants used in nutricosmetics based on the latest literature reports. During the analysis of specific species, the herbal raw material, main biologically active substances, their action, and application were taken into account. Active ingredients are obtained from many parts of plants, including fruits, leaves, and herbs. They are a source of flavonoids, mineral salts, vitamins, and antioxidants of antiaging, and anti-inflammatory properties. The wide use of plant raw materials of popular species and the richness of active ingredients of multidimensional action obtained from them, creates great opportunities for the dietary supplement industry.

Evidence-Informed, Interdisciplinary, Multidimensional Action Plan to Advance Overactive Bladder Research and Treatment Initiatives: Directives From State-of-the-Science Conference on Overactive Bladder and Cognitive Impairment.

This article outlines an evidence-informed, interdisciplinary, multidimensional, comprehensive action plan for the American Urogynecologic Society to improve care of women with overactive bladder (OAB) while minimizing treatment-related adverse events, including cognitive impairment. It is a "call to action" to advance basic, translational, and clinical research and summarizes initiatives developed at the State-of-the-Science Conference on OAB and Cognitive Impairment to (1) develop framework for a new OAB treatment approach in women, (2) define research gaps and future research priorities, (3) champion health equity and diversity considerations in OAB treatment, (4) foster community and promote education to remove stigma surrounding OAB and urinary incontinence, and (5) elevate visibility and impact of OAB, by creating partnerships through education and engagement with health care professionals, industry, private and public payers, funding agencies, and policymakers.

George W. Bush Yönetimi Kamu Diplomasisi: 11 Eylül ve Teröre Karşı Savaş

The terrorist attacks that occurred on September 11, 2001 are events that profoundly affected the national security of the United States, changed the political landscape of the world, and put terrorism on the primary agenda of United States security policy. President George W. Bush’s declaration of the War on Terror after 9/11 is an approach that changed the course of the world in global political relations. The War on Terror, a multidimensional action, took the form of a conceptual framework for national security and was an effort to establish and maintain a global coalition on the axis of expanding cooperation. In the motives required by the discursive reactions, the War on Terror has revealed a cause-effect relationship in terms of shaping the ideas and being understood by the masses, and has been described as a step that legitimizes international policies. In public diplomacy regarding the security interests of the United States, the War on Terror has become a comprehensive public diplomacy campaign against anti-Americanism. In this context, the War on Terror is a rhetorical basis for Bush administration public diplomacy. During the Bush administration, the War on Terror, which was an act of reminder for the values of the country and used to distinguish themselves from their enemies, gained importance for the creation of unity and strength across the country. In this study, the reflection of the 9/11 attacks on public diplomacy, the War on Terror strategy and the Bush Doctrine are addressed. At the same time, the rhetoric of the War on Terror in the formation of the value judgments of the masses and the implementation of the policies is examined from the perspective of public diplomacy.

The Multi-Dimensional Actions Control Approach for Obstacle Avoidance Based on Reinforcement Learning

In robotics, obstacle avoidance is an essential ability for distance sensor-based robots. This type of robot has axisymmetrically distributed distance sensors to acquire obstacle distance, so the state is symmetrical. Training the control policy with a reinforcement learning method is a trend. Considering the complexity of environments, such as narrow paths and right-angle turns, robots will have a better ability if the control policy can control the steering direction and speed simultaneously. This paper proposes the multi-dimensional action control (MDAC) approach based on a reinforcement learning technique, which can be used in multiple continuous action space tasks. It adopts a hierarchical structure, which has high and low-level modules. Low-level policies output concrete actions and the high-level policy determines when to invoke low-level modules according to the environment’s features. We design robot navigation experiments with continuous action spaces to test the method’s performance. It is an end-to-end approach and can solve complex obstacle avoidance tasks in navigation.

States Interests Legal Ensuring in the Field of Cross-Border Digital Services Taxation

The article continues the series of studies in the field of international economic (tax) law relating to the barriers and prospects of multidimensional action plan BEPS initiated by OECD and G-20 implementation. It’s dedicated to the issue of the states’ economic interest ensuring in the field of digital services taxation of non-residents exporting such kind of services to the business entities and physical persons without paying any direct corporate income tax. Above mentioned issue is considered on the multilateral level initially taking into account the most significant concept and legal drafting within Action 1 «Tax Challenges Arising from Digitalization» of multidimensional action plan BEPS initiated by OECD and G-20. Attention is focused, particularly, on the basic value creation concepts as well as intentions to modify long-standing approaches and to introduce novelties regarding identification of nonresidents-importers’ nexus to the territories of the states under absence of their permanent establishments. Regional level became the context of issue researching, on the one hand, UE institutions legislative initiatives relating to directive drafting aiming at new tax on gross income of digital services on the common market big companies-providers introduction, on the other hand, negative reaction of several member states towards such initiative on behalf of their companies which are digital services leading exports. Special attention has been given to the national legislation level with respect to unilateral actions of the power bodies of France, being one of the primary European integration apologist project, resulted in special law adoption. Its rules introduced new corporate income tax on digital services to be paid both residents and non-residents. Such legislative approach is contrary to the interests of such kind of services leading exporters — multinational corporations from the USA and China creating risks of both symmetric and asymmetric international economic and law countermeasures, particularly, within World Trade Organization implementation.

Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning

With the roll-out of smart meters and the increasing prevalence of distributed energy resources (DERs) at the residential level, end-users rely on home energy management systems (HEMSs) that can harness real-time data and employ artificial intelligence techniques to optimally manage the operation of different DERs, which are targeted toward minimizing the end-user’s energy bill. In this respect, the performance of the conventional model-based demand response (DR) management approach may deteriorate due to the inaccuracy of the employed DER operating models and the probabilistic modeling of uncertain parameters. To overcome the above drawbacks, this paper develops a novel real-time DR management strategy for a residential household based on the twin delayed deep deterministic policy gradient (TD3) learning approach. This approach is model-free, and thus does not rely on knowledge of the distribution of uncertainties or the operating models and parameters of the DERs. It also enables learning of neural-network-based and fine-grained DR management policies in a multi-dimensional action space by exploiting high-dimensional sensory data that encapsulate the uncertainties associated with the renewable generation, appliances’ operating states, utility prices, and outdoor temperature. The proposed method is applied to the energy management problem for a household with a portfolio of the most prominent types of DERs. Case studies involving a real-world scenario are used to validate the superior performance of the proposed method in reducing the household’s energy costs while coping with the multi-source uncertainties through comprehensive comparisons with the state-of-the-art deep reinforcement learning (DRL) methods.

Upotreba kariprazina u lečenju unipolarne depresije - prikaz slučaja

The integrative treatment of affective disorders, especially various forms of depressive disorder, still represents a significant challenge in clinical work. The current case report aims to present the challenges in everyday clinical practice in the case of complex diagnosis, differential diagnosis, and treatment of recurrent, severe depressive episode, as well as to consider the role of adjunctive therapy in its treatment. The case report presents a middle-aged, female patient, treated at the Clinic of Psychiatry, University Clinical Center of Serbia, for a severe recurrent depressive episode, with previous multiple suicide attempts. She had been initially treated with an antidepressant pertaining to the group of serotonin and norepinephrine reuptake inhibitors as well as mood stabilizer Lithium carbonate. Due to lack of adequate response, the atypical antipsychotic cariprazine was introduced as adjunctive therapy to her treatment, which led to significant clinical improvement and patient satisfaction. The use of atypical antipsychotics, including cariprazine, as primarily psychopharmaceuticals with multidimensional action is thus of specific importance from the aspect of everyday clinical practice. Only a detailed, integrative and individually tailored view of therapeutic possibilities, based on scientific knowledge, and practical experience, can lead to their adequate, timely and well-founded clinical application. Furthermore, their primary goal should be better functioning of patients with affective disorders based on the objective criteria and subjective/personal experience of functioning.

Vagueness in multidimensional proposals

This paper studies how agents choose to be vague in their proposals in a delegation environment. Two agents compete for the approval of a decision maker to implement a multidimensional action. Based on their knowledge of the consequences of actions, agents propose future actions but can be vague about any dimension. The decision maker, uncertain about the consequences of actions, chooses one agent to act. I show that vagueness on the dimension where one stands closer to the decision maker than his opponent preserves such an advantage, while preciseness undermines it. Vagueness therefore tends to occur on agents' advantageous dimensions.

Role of Unani Medicines in Oro-Dental Diseases: A Comprehensive Review and Scientific Evidence

Background: Unani system of medicine originated in ancient Greece, with the teachings of Buqrat (Hippocrates, 460-377 BC); and was introduced in India by the Mughal emperors. In Unani medicine, diseases and drugs are classified on the basis of mizaj (temperament) which is one of the fundamental concepts of Unani medicine. Oral health is given special emphasis in Unani medicine due to the understanding that diseases of the mouth affect eating and nutrition which may affect all systems of the body. Oro-dental diseases are described extensively in Unani literature in a systematic manner and many drugs such as Aqar Qarha (Anacyclus pyrethrum DC.), Kaat safed (Acacia catechu Willd.) etc are described which have shown promising results in recent scientific studies. Methodology: First, a review of Unani classical books was completed and the drugs to be further researched were selected. Then, a search was made on major scientific search engines regarding scientific evidence of their efficacy and safety. Conclusion: The Unani drugs mentioned in classical literature have shown promising results as anti-inflammatory, antipyretic, antiseptic, anti-bacterial, anti-fungal and anti-ulcerative agents in many oral disorders. Also, most of the drugs have a multi-dimensional action which is both curative and preventive.

Accelerating decentralized reinforcement learning of complex individual behaviors

Many Reinforcement Learning (RL) real-world applications have multi-dimensional action spaces which suffer from the combinatorial explosion of complexity. Then, it may turn infeasible to implement Centralized RL (CRL) systems due to the exponential increasing of dimensionality in both the state space and the action space, and the large number of training trials. In order to address this, this paper proposes to deal with these issues by using Decentralized Reinforcement Learning (DRL) to alleviate the effects of the curse of dimensionality on the action space, and by transferring knowledge to reduce the training episodes so that asymptotic converge can be achieved. Three DRL schemes are compared: DRL with independent learners and no prior-coordination (DRL-Ind); DRL accelerated-coordinated by using the Control Sharing (DRL+CoSh) Knowledge Transfer approach; and a proposed DRL scheme using the CoSh-based variant Nearby Action Sharing to include a measure of the uncertainty into the CoSh procedure (DRL+NeASh). These three schemes are analyzed through an extensive experimental study and validated through two complex real-world problems, namely the inwalk-kicking and the ball-dribbling behaviors, both performed with humanoid biped robots. Obtained results show (empirically): (i) the effectiveness of DRL systems which even without prior-coordination are able to achieve asymptotic convergence throughout indirect coordination; (ii) that by using the proposed knowledge transfer methods, it is possible to reduce the training episodes and to coordinate the DRL process; and (iii) obtained learning times are between 36% and 62% faster than the DRL-Ind schemes in the case studies.

Toward Breaking the Curse of Dimensionality: An FPTAS for Stochastic Dynamic Programs with Multidimensional Actions and Scalar States

We propose a Fully Polynomial-Time Approximation Scheme (FPTAS) for stochastic dynamic programs with multidimensional action, scalar state, convex costs and linear state transition function. The action spaces are polyhedral and described by parametric linear programs. This type of problems finds applications in the area of optimal planning under uncertainty, and can be thought of as the problem of optimally managing a single non-discrete resource over a finite time horizon. We show that under a value oracle model for the cost functions this result for one-dimensional state space is "best possible", because a similar dynamic programming model with two-dimensional state space does not admit a PTAS. The FPTAS relies on the solution of polynomial-sized linear programs to recursively compute an approximation of the value function at each stage. Our paper enlarges the class of dynamic programs that admit an FPTAS by showing, under suitable conditions, how to deal with multidimensional action spaces and with vectors of continuous random variables with bounded support. These results bring us one step closer to overcoming the curse of dimensionality of dynamic programming.

Adaptive Object Tracking via Multi-Angle Analysis Collaboration

Although tracking research has achieved excellent performance in mathematical angles, it is still meaningful to analyze tracking problems from multiple perspectives. This motivation not only promotes the independence of tracking research but also increases the flexibility of practical applications. This paper presents a significant tracking framework based on the multi-dimensional state–action space reinforcement learning, termed as multi-angle analysis collaboration tracking (MACT). MACT is comprised of a basic tracking framework and a strategic framework which assists the former. Especially, the strategic framework is extensible and currently includes feature selection strategy (FSS) and movement trend strategy (MTS). These strategies are abstracted from the multi-angle analysis of tracking problems (observer’s attention and object’s motion). The content of the analysis corresponds to the specific actions in the multidimensional action space. Concretely, the tracker, regarded as an agent, is trained with Q-learning algorithm and -greedy exploration strategy, where we adopt a customized rewarding function to encourage robust object tracking. Numerous contrast experimental evaluations on the OTB50 benchmark demonstrate the effectiveness of the strategies and improvement in speed and accuracy of MACT tracker.

Decentralized Reinforcement Learning of Robot Behaviors

A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individual behaviors in problems where multi-dimensional action spaces are involved. When using this methodology, sub-tasks are learned in parallel by individual agents working toward a common goal. In addition to proposing this methodology, three specific multi agent DRL approaches are considered: DRL-Independent, DRL Cooperative-Adaptive (CA), and DRL-Lenient. These approaches are validated and analyzed with an extensive empirical study using four different problems: 3D Mountain Car, SCARA Real-Time Trajectory Generation, Ball-Dribbling in humanoid soccer robotics, and Ball-Pushing using differential drive robots. The experimental validation provides evidence that DRL implementations show better performances and faster learning times than their centralized counterparts, while using less computational resources. DRL-Lenient and DRL-CA algorithms achieve the best final performances for the four tested problems, outperforming their DRL-Independent counterparts. Furthermore, the benefits of the DRL-Lenient and DRL-CA are more noticeable when the problem complexity increases and the centralized scheme becomes intractable given the available computational resources and training time.