385 research outputs found
Bribes to Miners: Evidence from Ethereum
Though blockchain aims to alleviate bribing attacks, users can collude with
miners by directly sending bribes. This paper focuses on empirical evidence of
bribes to miners, and the detected behaviour implies that mining power could be
exploited. By scanning transactions on Ethereum, transactions for potential
direct bribes are filtered, and we find that the potential bribers and bribees
are centralized in a small group. After constructing proxies of active level of
potential bribing, we find that potential bribes can affect the status of
Ethereum and other mainstream blockchains, and network adoption of blockchain
can be influenced as well. Besides, direct bribes can be related to stock
markets, e.g., S&P 500 and Nasdaq
Facilitating Cooperative Truck Platooning for Energy Savings: Path Planning, Platoon Formation and Benefit Redistribution
Enabled by the connected and automated vehicle (CAV) technology, cooperative truck platooning that offers promising energy savings is likely to be implemented soon.
However, as the trucking industry operates in a highly granular manner so that the trucks usually vary in their operation schedules, vehicle types and configurations, it is inevitable that 1) the spontaneous platooning over a spatial network is rare, 2) the total fuel savings vary from platoon to platoon, and 3) the benefit achieved within a platoon differs from position to position, e.g., the lead vehicle always achieves the least fuel-saving. Consequently, trucks from different owners may not have the opportunities to platoon with others if no path coordination is performed. Even if they happen to do so, they may tend to change positions in the formed platoons to achieve greater benefits, yielding behaviorally unstable platoons with less energy savings and more disruptions to traffic flows.
This thesis proposes a hierarchical modeling framework to explicate the necessitated strategies that facilitate cooperative truck platooning. An empirical study is first conducted to scrutinize the energy-saving potentials of the U.S. national freight network. By comparing the performance under scheduled platooning and ad-hoc platooning, the author shows that the platooning opportunities can be greatly improved by careful path planning, thereby yielding substantial energy savings. For trucks assembled on the same path and can to platoon together, the second part of the thesis investigates the optimal platoon formation that maximizes total platooning utility and benefits redistribution mechanisms that address the behavioral instability issue. Both centralized and decentralized approaches are proposed. In particular, the decentralized approach employs a dynamic process where individual trucks or formed platoons are assumed to act as rational agents. The agents decide whether to form a larger, better platoon considering their own utilities under the pre-defined benefit reallocation mechanisms. Depending on whether the trucks are single-brand or multi-brand, whether there is a complete information setting or incomplete information setting, three mechanisms, auction, bilateral trade model, and one-sided matching are proposed. The centralized approach yields a near-optimal solution for the whole system and is more computationally efficient than conventional algorithms. The decentralized approach is stable, more flexible, and computational efficient while maintaining acceptable degrees of optimality. The mechanisms proposed can apply to not only under the truck platooning scenario but also other forms of shared mobility.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163047/1/xtsun_1.pd
On the Robotic Uncertainty of Fully Autonomous Traffic
Recent transportation research suggests that autonomous vehicles (AVs) have
the potential to improve traffic flow efficiency as they are able to maintain
smaller car-following distances. Nevertheless, being a unique class of ground
robots, AVs are susceptible to robotic errors, particularly in their perception
module, leading to uncertainties in their movements and an increased risk of
collisions. Consequently, conservative operational strategies, such as larger
headway and slower speeds, are implemented to prioritize safety over traffic
capacity in real-world operations. To reconcile the inconsistency, this paper
proposes an analytical model framework that delineates the endogenous
reciprocity between traffic safety and efficiency that arises from robotic
uncertainty in AVs. Car-following scenarios are extensively examined, with
uncertain headway as the key parameter for bridging the single-lane capacity
and the collision probability. A Markov chain is then introduced to describe
the dynamics of the lane capacity, and the resulting expected
collision-inclusive capacity is adopted as the ultimate performance measure for
fully autonomous traffic. With the help of this analytical model, it is
possible to support the settings of critical parameters in AV operations and
incorporate optimization techniques to assist traffic management strategies for
autonomous traffic
NSOTree: Neural Survival Oblique Tree
Survival analysis is a statistical method employed to scrutinize the duration
until a specific event of interest transpires, known as time-to-event
information characterized by censorship. Recently, deep learning-based methods
have dominated this field due to their representational capacity and
state-of-the-art performance. However, the black-box nature of the deep neural
network hinders its interpretability, which is desired in real-world survival
applications but has been largely neglected by previous works. In contrast,
conventional tree-based methods are advantageous with respect to
interpretability, while consistently grappling with an inability to approximate
the global optima due to greedy expansion. In this paper, we leverage the
strengths of both neural networks and tree-based methods, capitalizing on their
ability to approximate intricate functions while maintaining interpretability.
To this end, we propose a Neural Survival Oblique Tree (NSOTree) for survival
analysis. Specifically, the NSOTree was derived from the ReLU network and can
be easily incorporated into existing survival models in a plug-and-play
fashion. Evaluations on both simulated and real survival datasets demonstrated
the effectiveness of the proposed method in terms of performance and
interpretability.Comment: 12 page
Robust Low-Rank Subspace Segmentation with Semidefinite Guarantees
Recently there is a line of research work proposing to employ Spectral
Clustering (SC) to segment (group){Throughout the paper, we use segmentation,
clustering, and grouping, and their verb forms, interchangeably.}
high-dimensional structural data such as those (approximately) lying on
subspaces {We follow {liu2010robust} and use the term "subspace" to denote both
linear subspaces and affine subspaces. There is a trivial conversion between
linear subspaces and affine subspaces as mentioned therein.} or low-dimensional
manifolds. By learning the affinity matrix in the form of sparse
reconstruction, techniques proposed in this vein often considerably boost the
performance in subspace settings where traditional SC can fail. Despite the
success, there are fundamental problems that have been left unsolved: the
spectrum property of the learned affinity matrix cannot be gauged in advance,
and there is often one ugly symmetrization step that post-processes the
affinity for SC input. Hence we advocate to enforce the symmetric positive
semidefinite constraint explicitly during learning (Low-Rank Representation
with Positive SemiDefinite constraint, or LRR-PSD), and show that factually it
can be solved in an exquisite scheme efficiently instead of general-purpose SDP
solvers that usually scale up poorly. We provide rigorous mathematical
derivations to show that, in its canonical form, LRR-PSD is equivalent to the
recently proposed Low-Rank Representation (LRR) scheme {liu2010robust}, and
hence offer theoretic and practical insights to both LRR-PSD and LRR, inviting
future research. As per the computational cost, our proposal is at most
comparable to that of LRR, if not less. We validate our theoretic analysis and
optimization scheme by experiments on both synthetic and real data sets.Comment: 10 pages, 4 figures. Accepted by ICDM Workshop on Optimization Based
Methods for Emerging Data Mining Problems (OEDM), 2010. Main proof simplified
and typos corrected. Experimental data slightly adde
Blow-up phenomena for a family of Burgers-like equations
AbstractBy introducing a stress multiplier we derive a family of Burgers-like equations. We investigate the blow-up phenomena of the equations both on the real line R and on the circle S to get a comparison with the Degasperis–Procesi equation. On the line R, we first establish the local well-posedness and the blow-up scenario. Then we use conservation laws of the equations to get the estimate for the L∞-norm of the strong solutions, by which we prove that the solutions to the equations may blow up in the form of wave breaking for certain initial profiles. Analogous results are provided in the periodic case. Especially, we find differences between the Burgers-like equations and the Degasperis–Procesi equation, see Remark 4.1
Spatial-temporal prediction of air quality based on recurrent neural networks
To predict air quality (PM2.5 concentrations, et al), many parametric regression models have been developed, while deep learning algorithms are used less often. And few of them takes the air pollution emission or spatial information into consideration or predict them in hour scale. In this paper, we proposed a spatial-temporal GRU-based prediction framework incorporating ground pollution monitoring (GPM), factory emissions (FE), surface meteorology monitoring (SMM) variables to predict hourly PM2.5 concentrations. The dataset for empirical experiments was built based on air quality monitoring in Shenyang, China. Experimental results indicate that our method enables more accurate predictions than all baseline models and by applying the convolutional processing to the GPM and FE variables notable improvement can be achieved in prediction accuracy
Voter Coalitions in Decentralized Autonomous Organization (DAO): Evidence from MakerDAO
Decentralized Autonomous Organization (DAO) provides a decentralized
governance solution through blockchain, where decision-making process relies on
on-chain voting and follows majority rule. This paper focuses on MakerDAO, and
we find five voter coalitions after applying clustering algorithm to voting
history. The emergence of a dominant voter coalition is a signal of governance
centralization in DAO, and voter coalitions have complicated influence on Maker
protocol, which is governed by MakerDAO. This paper presents empirical evidence
of multicoalition democracy in DAO and further contributes to the contemporary
debate on whether decentralized governance is possible
A facile molecularly imprinted polymer-based fluorometric assay for detection of histamine
Histamine is a biogenic amine naturally present in many body cells. It is also a contaminant that is mostly found in spoiled food. The consumption of foods containing high levels of histamine may lead to an allergy-like food poisoning. Analytical methods that can routinely screen histamine are thus urgently needed. In this paper, we developed a facile and cost-effective molecularly imprinted polymer (MIP)-based fluorometric assay to directly quantify histamine. Histamine-specific MIP nanoparticles (nanoMIPs) were synthesized using a modified solid-phase synthesis method. They were then immobilized in the wells of a microplate to bind the histamine in aqueous samples. After binding, o-phthaldialdehyde (OPA) was used to label the bound histamine, which converted the binding events into fluorescent signals. The obtained calibration curve of histamine showed a linear correlation ranging from 1.80 to 44.98 μM with the limit of detection of 1.80 μM. This method was successfully used to detect histamine in spiked diary milk with a recovery rate of more than 85%
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