POSSIBLE TAX MODELS TO STIMULATE THE ECONOMY IN LATGALE REGION

Since regaining the state independence, Latgale region in terms of its development has been lagging behind other regions of the country. It is demonstrated by the index of territorial development, which is drawn up annually and characterizes specific self-governments or regional socio-economic perspectives. The authors based on the identified shortcomings offer potential solutions for the problem. The aim of the article is to formulate and evaluate proposals for possible tax models in order to stimulate the economy of Latgale region. Consequently, the article defines problems, which hinder the economic development of Latgale region as well as analyzes and proposes possible tax models elaborated to solve the defined problems in the framework of the present study

Heterogeneous Learning from Demonstration

The development of human-robot systems able to leverage the strengths of both humans and their robotic counterparts has been greatly sought after because of the foreseen, broad-ranging impact across industry and research. We believe the true potential of these systems cannot be reached unless the robot is able to act with a high level of autonomy, reducing the burden of manual tasking or teleoperation. To achieve this level of autonomy, robots must be able to work fluidly with its human partners, inferring their needs without explicit commands. This inference requires the robot to be able to detect and classify the heterogeneity of its partners. We propose a framework for learning from heterogeneous demonstration based upon Bayesian inference and evaluate a suite of approaches on a real-world dataset of gameplay from StarCraft II. This evaluation provides evidence that our Bayesian approach can outperform conventional methods by up to 12.8$$

Coordinated Control of UAVs for Human-Centered Active Sensing of Wildfires

Fighting wildfires is a precarious task, imperiling the lives of engaging firefighters and those who reside in the fire's path. Firefighters need online and dynamic observation of the firefront to anticipate a wildfire's unknown characteristics, such as size, scale, and propagation velocity, and to plan accordingly. In this paper, we propose a distributed control framework to coordinate a team of unmanned aerial vehicles (UAVs) for a human-centered active sensing of wildfires. We develop a dual-criterion objective function based on Kalman uncertainty residual propagation and weighted multi-agent consensus protocol, which enables the UAVs to actively infer the wildfire dynamics and parameters, track and monitor the fire transition, and safely manage human firefighters on the ground using acquired information. We evaluate our approach relative to prior work, showing significant improvements by reducing the environment's cumulative uncertainty residual by more than $10^2$ and $10^5$ times in firefront coverage performance to support human-robot teaming for firefighting. We also demonstrate our method on physical robots in a mock firefighting exercise

PSYCHOLOGICAL AND MENTAL HEALTH BURDEN ON HEALTH CARE PROVIDERS IN A CANCER CENTRE DURING COVID-19 PANDEMIC OUTBREAK IN INDIA

Background: The outbreak of novel coronavirus (COVID-19) is severely affecting the public health and posing a challenge to health care providers, especially working as front-line medical staff. This study was aimed to understand the psychological impact and mental burden of the present outbreak on Indian health care providers who are working at cancer care centre. Subjects and methods: A self-reporting online questionnaire was given to the multidisciplinary staff (n=344) and their mental health was assessed using various scales via GAD-7 scale for anxiety, PHQ-9 scale for depression, ISI for insomnia, K-10 for distress, and STAI for stress along with five self-made Pandemic specific questions. Results: Response rate was 91% (n=344) among 190 (55%) were male and 154 (45%) were female. The frontline and secondline workers were 178 (52%) and 166 (48%), respectively. Symptoms of anxiety, depression, insomnia and distress was observed in 62 (18%), 75 (22%), 42 (12%), and 60 (17%) of the participants, respectively. They were predominantly influenced by variables such as gender (female), education (≥graduation), co-morbidities, and level of work (frontline). Followed by other less dominant variables such as contact with patients (frequent), and working in hospital (<3 years), respectively. Conclusion: A mild to moderate level of psychological burden was observed in the health care providers. Overall, there is a need to address the mental health issues by providing, timely training, counselling, rotation in shifts, lowering workload and intensify the awareness programmes of the staff during this COVID-19 pandemic for better outcomes and promoting resilience in the staff

Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations

Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics. However, current LfD frameworks are not capable of fast adaptation to heterogeneous human demonstrations nor the large-scale deployment in ubiquitous robotics applications. In this paper, we propose a novel LfD framework, Fast Lifelong Adaptive Inverse Reinforcement learning (FLAIR). Our approach (1) leverages learned strategies to construct policy mixtures for fast adaptation to new demonstrations, allowing for quick end-user personalization, (2) distills common knowledge across demonstrations, achieving accurate task inference; and (3) expands its model only when needed in lifelong deployments, maintaining a concise set of prototypical strategies that can approximate all behaviors via policy mixtures. We empirically validate that FLAIR achieves adaptability (i.e., the robot adapts to heterogeneous, user-specific task preferences), efficiency (i.e., the robot achieves sample-efficient adaptation), and scalability (i.e., the model grows sublinearly with the number of demonstrations while maintaining high performance). FLAIR surpasses benchmarks across three control tasks with an average 57% improvement in policy returns and an average 78% fewer episodes required for demonstration modeling using policy mixtures. Finally, we demonstrate the success of FLAIR in a table tennis task and find users rate FLAIR as having higher task (p<.05) and personalization (p<.05) performance

Learning Models of Adversarial Agent Behavior under Partial Observability

The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction. In this work, we present Graph based Adversarial Modeling with Mutal Information (GrAMMI) for modeling the behavior of an adversarial opponent agent. GrAMMI is a novel graph neural network (GNN) based approach that uses mutual information maximization as an auxiliary objective to predict the current and future states of an adversarial opponent with partial observability. To evaluate GrAMMI, we design two large-scale, pursuit-evasion domains inspired by real-world scenarios, where a team of heterogeneous agents is tasked with tracking and interdicting a single adversarial agent, and the adversarial agent must evade detection while achieving its own objectives. With the mutual information formulation, GrAMMI outperforms all baselines in both domains and achieves 31.68% higher log-likelihood on average for future adversarial state predictions across both domains.Comment: 8 pages, 3 figures, 2 table

Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment

We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial, evasive agent. The heterogeneous search team may only have access to a limited number of past adversary trajectories within a large search space. This problem is challenging for both model-based searching and reinforcement learning (RL) methods since the adversary exhibits reactionary and deceptive evasive behaviors in a large space leading to sparse detections for the search agents. To address this challenge, we propose a novel Multi-Agent RL (MARL) framework that leverages the estimated adversary location from our learnable filtering model. We show that our MARL architecture can outperform all baselines and achieves a 46% increase in detection rate.Comment: Accepted by IEEE International Symposium on Multi-Robot & Multi-Agent Systems (MRS) 202

Identification of a system for hydroxamate xenosiderophore-mediated iron transport in Burkholderia cenocepacia.

One of the mechanisms employed by the opportunistic pathogen Burkholderia cenocepacia to acquire the essential element iron is the production and release of two ferric iron chelating compounds (siderophores), ornibactin and pyochelin. Here we show that B. cenocepacia is also able to take advantage of a range of siderophores produced by other bacteria and fungi ('xenosiderophores') that chelate iron exclusively by means of hydroxamate groups. These include the tris-hydroxamate siderophores ferrioxamine B, ferrichrome, ferricrocin and triacetylfusarinine C, the bis-hydroxamates alcaligin and rhodotorulic acid, and the monohydroxamate siderophore cepabactin. We also show that of the 24 TonB-dependent transporters encoded by the B. cenocepacia genome, two (FhuA and FeuA) are involved in the uptake of hydroxamate xenosiderophores, with FhuA serving as the exclusive transporter of iron-loaded ferrioxamine B, triacetylfusarinine C, alcaligin and rhodotorulic acid, while both FhuA and FeuA are able to translocate ferrichrome-type siderophores across the outer membrane. Finally, we identified FhuB, a putative cytoplasmic membrane-anchored ferric-siderophore reductase, as being obligatory for utilization of all of the tested bis- and tris-hydroxamate xenosiderophores apart from alcaligin