627 research outputs found
Oceanic Games: Centralization Risks and Incentives in Blockchain Mining
To participate in the distributed consensus of permissionless blockchains,
prospective nodes -- or miners -- provide proof of designated, costly
resources. However, in contrast to the intended decentralization, current data
on blockchain mining unveils increased concentration of these resources in a
few major entities, typically mining pools. To study strategic considerations
in this setting, we employ the concept of Oceanic Games, Milnor and Shapley
(1978). Oceanic Games have been used to analyze decision making in corporate
settings with small numbers of dominant players (shareholders) and large
numbers of individually insignificant players, the ocean. Unlike standard
equilibrium models, they focus on measuring the value (or power) per entity and
per unit of resource} in a given distribution of resources. These values are
viewed as strategic components in coalition formations, mergers and resource
acquisitions. Considering such issues relevant to blockchain governance and
long-term sustainability, we adapt oceanic games to blockchain mining and
illustrate the defined concepts via examples. The application of existing
results reveals incentives for individual miners to merge in order to increase
the value of their resources. This offers an alternative perspective to the
observed centralization and concentration of mining power. Beyond numerical
simulations, we use the model to identify issues relevant to the design of
future cryptocurrencies and formulate prospective research questions.Comment: [Best Paper Award] at the International Conference on Mathematical
Research for Blockchain Economy (MARBLE 2019
Sustainable farming with native rocks: the transition without revolution.
The development process which humanity passed through favored a series of conquests, reflected in the better quality of life and longevity, however, it also provoked upsets and severe transformation in the environment and in the human food security. Such process is driving the ecosystems to be homogeneous, and, therefore,the nutrients� supply, via nourishment. To change this panorama, the present work discusses the gains of incorporating the stonemeal technique as a strategic alternative to give back the essential fertile characteristics to the soils. This technology has the function of facilitating the rejuvenation of the soils and increasing the availability of the necessary nutrients to the full development of the plants which is a basic input for the proliferation of life in all its dimensions
Monopoly Pricing in a Vertical Market with Demand Uncertainty
We study a vertical market with an upsteam supplier and multiple downstream
retailers. Demand uncertainty falls to the supplier who acts first and sets a
uniform wholesale price before the retailers observe the realized demand and
engage in retail competition. Our focus is on the supplier's optimal pricing
decision. We express the price elasticity of expected demand in terms of the
mean residual demand (MRD) function of the demand distribution. This allows for
a closed form characterization of the points of unitary elasticity that
maximize the supplier's profits and the derivation of a mild unimodality
condition for the supplier's objective function that generalizes the widely
used increasing generalized failure rate (IGFR) condition. A direct implication
is that optimal prices between different markets can be ordered if the markets
can be stochastically ordered according to their MRD functions or equivalently
to their elasticities. Based on this, we apply the theory of stochastic orders
to study the response of the supplier's optimal price to various features of
the demand distribution. Our findings challenge previously established economic
insights about the effects of market size, demand transformations and demand
variability on wholesale prices and indicate that the conclusions largely
depend on the exact notion that will be employed. We then turn to measure
market performance and derive a distribution free and tight bound on the
probability of no trade between the supplier and the retailers. If trade takes
place, our findings indicate that ovarall performance depends on the interplay
between demand uncertainty and level of retail competition
Stable Matching with Evolving Preferences
We consider the problem of stable matching with dynamic preference lists. At
each time step, the preference list of some player may change by swapping
random adjacent members. The goal of a central agency (algorithm) is to
maintain an approximately stable matching (in terms of number of blocking
pairs) at all times. The changes in the preference lists are not reported to
the algorithm, but must instead be probed explicitly by the algorithm. We
design an algorithm that in expectation and with high probability maintains a
matching that has at most blocking pairs.Comment: 13 page
3D Shape Estimation from 2D Landmarks: A Convex Relaxation Approach
We investigate the problem of estimating the 3D shape of an object, given a
set of 2D landmarks in a single image. To alleviate the reconstruction
ambiguity, a widely-used approach is to confine the unknown 3D shape within a
shape space built upon existing shapes. While this approach has proven to be
successful in various applications, a challenging issue remains, i.e., the
joint estimation of shape parameters and camera-pose parameters requires to
solve a nonconvex optimization problem. The existing methods often adopt an
alternating minimization scheme to locally update the parameters, and
consequently the solution is sensitive to initialization. In this paper, we
propose a convex formulation to address this problem and develop an efficient
algorithm to solve the proposed convex program. We demonstrate the exact
recovery property of the proposed method, its merits compared to alternative
methods, and the applicability in human pose and car shape estimation.Comment: In Proceedings of CVPR 201
Collaborative Perception From Data Association To Localization
During the last decade, visual sensors have become ubiquitous. One or more cameras
can be found in devices ranging from smartphones to unmanned aerial vehicles and
autonomous cars. During the same time, we have witnessed the emergence of large
scale networks ranging from sensor networks to robotic swarms.
Assume multiple visual sensors perceive the same scene from different viewpoints. In
order to achieve consistent perception, the problem of correspondences between ob-
served features must be first solved. Then, it is often necessary to perform distributed
localization, i.e. to estimate the pose of each agent with respect to a global reference
frame. Having everything set in the same coordinate system and everything having
the same meaning for all agents, operation of the agents and interpretation of the
jointly observed scene become possible.
The questions we address in this thesis are the following: first, can a group of visual
sensors agree on what they see, in a decentralized fashion? This is the problem of
collaborative data association. Then, based on what they see, can the visual sensors
agree on where they are, in a decentralized fashion as well? This is the problem of
cooperative localization.
The contributions of this work are five-fold. We are the first to address the problem
of consistent multiway matching in a decentralized setting. Secondly, we propose
an efficient decentralized dynamical systems approach for computing any number of
smallest eigenvalues and the associated eigenvectors of a weighted graph with global
convergence guarantees with direct applications in group synchronization problems,
e.g. permutations or rotations synchronization. Thirdly, we propose a state-of-the
art framework for decentralized collaborative localization for mobile agents under
the presence of unknown cross-correlations by solving a minimax optimization prob-
lem to account for the missing information. Fourthly, we are the first to present an
approach to the 3-D rotation localization of a camera sensor network from relative
bearing measurements. Lastly, we focus on the case of a group of three visual sensors.
We propose a novel Riemannian geometric representation of the trifocal tensor which
relates projections of points and lines in three overlapping views. The aforemen-
tioned representation enables the use of the state-of-the-art optimization methods on
Riemannian manifolds and the use of robust averaging techniques for estimating the
trifocal tensor
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