22,518 research outputs found
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
An iterative warping and clustering algorithm to estimate multiple wave-shape functions from a nonstationary oscillatory signal
Nonsinusoidal oscillatory signals are everywhere. In practice, the
nonsinusoidal oscillatory pattern, modeled as a 1-periodic wave-shape function
(WSF), might vary from cycle to cycle. When there are finite different WSFs,
, so that the WSF jumps from one to another suddenly, the
different WSFs and jumps encode useful information. We present an iterative
warping and clustering algorithm to estimate from a
nonstationary oscillatory signal with time-varying amplitude and frequency, and
hence the change points of the WSFs. The algorithm is a novel combination of
time-frequency analysis, singular value decomposition entropy and vector
spectral clustering. We demonstrate the efficiency of the proposed algorithm
with simulated and real signals, including the voice signal, arterial blood
pressure, electrocardiogram and accelerometer signal. Moreover, we provide a
mathematical justification of the algorithm under the assumption that the
amplitude and frequency of the signal are slowly time-varying and there are
finite change points that model sudden changes from one wave-shape function to
another one.Comment: 39 pages, 11 figure
NF-Atlas: Multi-Volume Neural Feature Fields for Large Scale LiDAR Mapping
LiDAR Mapping has been a long-standing problem in robotics. Recent progress
in neural implicit representation has brought new opportunities to robotic
mapping. In this paper, we propose the multi-volume neural feature fields,
called NF-Atlas, which bridge the neural feature volumes with pose graph
optimization. By regarding the neural feature volume as pose graph nodes and
the relative pose between volumes as pose graph edges, the entire neural
feature field becomes both locally rigid and globally elastic. Locally, the
neural feature volume employs a sparse feature Octree and a small MLP to encode
the submap SDF with an option of semantics. Learning the map using this
structure allows for end-to-end solving of maximum a posteriori (MAP) based
probabilistic mapping. Globally, the map is built volume by volume
independently, avoiding catastrophic forgetting when mapping incrementally.
Furthermore, when a loop closure occurs, with the elastic pose graph based
representation, only updating the origin of neural volumes is required without
remapping. Finally, these functionalities of NF-Atlas are validated. Thanks to
the sparsity and the optimization based formulation, NF-Atlas shows competitive
performance in terms of accuracy, efficiency and memory usage on both
simulation and real-world datasets
ShakingBot: Dynamic Manipulation for Bagging
Bag manipulation through robots is complex and challenging due to the
deformability of the bag. Based on dynamic manipulation strategy, we propose a
new framework, ShakingBot, for the bagging tasks. ShakingBot utilizes a
perception module to identify the key region of the plastic bag from arbitrary
initial configurations. According to the segmentation, ShakingBot iteratively
executes a novel set of actions, including Bag Adjustment, Dual-arm Shaking,
and One-arm Holding, to open the bag. The dynamic action, Dual-arm Shaking, can
effectively open the bag without the need to account for the crumpled
configuration.Then, we insert the items and lift the bag for transport. We
perform our method on a dual-arm robot and achieve a success rate of 21/33 for
inserting at least one item across various initial bag configurations. In this
work, we demonstrate the performance of dynamic shaking actions compared to the
quasi-static manipulation in the bagging task. We also show that our method
generalizes to variations despite the bag's size, pattern, and color.Comment: Manipulating bag through robots to baggin
Fair Grading Algorithms for Randomized Exams
This paper studies grading algorithms for randomized exams. In a randomized
exam, each student is asked a small number of random questions from a large
question bank. The predominant grading rule is simple averaging, i.e.,
calculating grades by averaging scores on the questions each student is asked,
which is fair ex-ante, over the randomized questions, but not fair ex-post, on
the realized questions. The fair grading problem is to estimate the average
grade of each student on the full question bank. The maximum-likelihood
estimator for the Bradley-Terry-Luce model on the bipartite student-question
graph is shown to be consistent with high probability when the number of
questions asked to each student is at least the cubed-logarithm of the number
of students. In an empirical study on exam data and in simulations, our
algorithm based on the maximum-likelihood estimator significantly outperforms
simple averaging in prediction accuracy and ex-post fairness even with a small
class and exam size
Tauberian identities and the connection to Wile E. Coyote physics
The application of the motion of a vertically suspended mass-spring system
released under tension is studied focusing upon the delay timescale for the
bottom mass as a function of the spring constants and masses. This
``hang-time", reminiscent of the Coyote and Road Runner cartoons, is quantified
using the far-field asymptotic expansion of the bottom mass' Laplace transform.
These asymptotics are connected to the short time mass dynamics through
Tauberian identities and explicit residue calculations. It is shown, perhaps
paradoxically, that this delay timescale is maximized in the large mass limit
of the top ``boulder". Experiments are presented and compared with the
theoretical predictions. This system is an exciting example for the teaching of
mass-spring dynamics in classes on Ordinary Differential Equations, and does
not require any normal mode calculations for these predictions
Transfer learning for operator selection: A reinforcement learning approach
In the past two decades, metaheuristic optimisation algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a parameter or a strategy. Some online and offline strategies are employed in order to obtain optimal configurations of the algorithms. Adaptive operator selection is one of them, and it determines whether or not to update a strategy from the strategy pool during the search process. In the field of machine learning, Reinforcement Learning (RL) refers to goal-oriented algorithms, which learn from the environment how to achieve a goal. On MOAs, reinforcement learning has been utilised to control the operator selection process. However, existing research fails to show that learned information may be transferred from one problem-solving procedure to another. The primary goal of the proposed research is to determine the impact of transfer learning on RL and MOAs. As a test problem, a set union knapsack problem with 30 separate benchmark problem instances is used. The results are statistically compared in depth. The learning process, according to the findings, improved the convergence speed while significantly reducing the CPU time
Towards a more just refuge regime: quotas, markets and a fair share
The international refugee regime is beset by two problems: Responsibility for refuge falls
disproportionately on a few states and many owed refuge do not get it. In this work, I explore
remedies to these problems. One is a quota distribution wherein states are distributed
responsibilities via allotment. Another is a marketized quota system wherein states are free to buy
and sell their allotments with others. I explore these in three parts. In Part 1, I develop the prime
principles upon which a just regime is built and with which alternatives can be adjudicated. The
first and most important principle – ‘Justice for Refugees’ – stipulates that a just regime provides
refuge for all who have a basic interest in it. The second principle – ‘Justice for States’ – stipulates
that a just distribution of refuge responsibilities among states is one that is capacity considerate. In
Part 2, I take up several vexing questions regarding the distribution of refuge responsibilities
among states in a collective effort. First, what is a state’s ‘fair share’? The answer requires the
determination of some logic – some metric – with which a distribution is determined. I argue that
one popular method in the political theory literature – a GDP-based distribution – is normatively
unsatisfactory. In its place, I posit several alternative metrics that are more attuned with the
principles of justice but absent in the political theory literature: GDP adjusted for Purchasing
Power Parity and the Human Development Index. I offer an exploration of both these. Second,
are states required to ‘take up the slack’ left by defaulting peers? Here, I argue that duties of help
remain intact in cases of partial compliance among states in the refuge regime, but that political
concerns may require that such duties be applied with caution. I submit that a market instrument
offers one practical solution to this problem, as well as other advantages. In Part 3, I take aim at
marketization and grapple with its many pitfalls: That marketization is commodifying, that it is
corrupting, and that it offers little advantage in providing quality protection for refugees. In
addition to these, I apply a framework of moral markets developed by Debra Satz. I argue that a
refuge market may satisfy Justice Among States, but that it is violative of the refugees’ welfare
interest in remaining free of degrading and discriminatory treatment
Underwater optical wireless communications in turbulent conditions: from simulation to experimentation
Underwater optical wireless communication (UOWC) is a technology that aims to apply high speed optical wireless communication (OWC) techniques to the underwater channel. UOWC has the potential to provide high speed links over relatively short distances as part of a hybrid underwater network, along with radio frequency (RF) and underwater acoustic communications (UAC) technologies. However, there are some difficulties involved in developing a reliable UOWC link, namely, the complexity of the channel. The main focus throughout this thesis is to develop a greater understanding of the effects of the UOWC channel, especially underwater turbulence. This understanding is developed from basic theory through to simulation and experimental studies in order to gain a holistic understanding of turbulence in the UOWC channel.
This thesis first presents a method of modelling optical underwater turbulence through simulation that allows it to be examined in conjunction with absorption and scattering. In a stationary channel, this turbulence induced scattering is shown to cause and increase both spatial and temporal spreading at the receiver plane. It is also demonstrated using the technique presented that the relative impact of turbulence on a received signal is lower in a highly scattering channel, showing an in-built resilience of these channels. Received intensity distributions are presented confirming that fluctuations in received power from this method follow the commonly used Log-Normal fading model. The impact of turbulence - as measured using this new modelling framework - on link performance, in terms of maximum achievable data rate and bit error rate is equally investigated.
Following that, experimental studies comparing both the relative impact of turbulence induced scattering on coherent and non-coherent light propagating through water and the relative impact of turbulence in different water conditions are presented. It is shown that the scintillation index increases with increasing temperature inhomogeneity in the underwater channel. These results indicate that a light beam from a non-coherent source has a greater resilience to temperature inhomogeneity induced turbulence effect in an underwater channel. These results will help researchers in simulating realistic channel conditions when modelling a light emitting diode (LED) based intensity modulation with direct detection (IM/DD) UOWC link.
Finally, a comparison of different modulation schemes in still and turbulent water conditions is presented. Using an underwater channel emulator, it is shown that pulse position modulation (PPM) and subcarrier intensity modulation (SIM) have an inherent resilience to turbulence induced fading with SIM achieving higher data rates under all conditions. The signal processing technique termed pair-wise coding (PWC) is applied to SIM in underwater optical wireless communications for the first time. The performance of PWC is compared with the, state-of-the-art, bit and power loading optimisation algorithm. Using PWC, a maximum data rate of 5.2 Gbps is achieved in still water conditions
- …