52 research outputs found
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 and 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
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