860 research outputs found
Entanglement-guided architectures of machine learning by quantum tensor network
It is a fundamental, but still elusive question whether the schemes based on
quantum mechanics, in particular on quantum entanglement, can be used for
classical information processing and machine learning. Even partial answer to
this question would bring important insights to both fields of machine learning
and quantum mechanics. In this work, we implement simple numerical experiments,
related to pattern/images classification, in which we represent the classifiers
by many-qubit quantum states written in the matrix product states (MPS).
Classical machine learning algorithm is applied to these quantum states to
learn the classical data. We explicitly show how quantum entanglement (i.e.,
single-site and bipartite entanglement) can emerge in such represented images.
Entanglement characterizes here the importance of data, and such information
are practically used to guide the architecture of MPS, and improve the
efficiency. The number of needed qubits can be reduced to less than 1/10 of the
original number, which is within the access of the state-of-the-art quantum
computers. We expect such numerical experiments could open new paths in
charactering classical machine learning algorithms, and at the same time shed
lights on the generic quantum simulations/computations of machine learning
tasks.Comment: 10 pages, 5 figure
The Analysis of the Causes of Willy’s Death in Arthur Miller’s Death of a Salesman
Death of a Salesman is a classic tragic work in contemporary America. It discusses some social factors in Willy Loman’s death, such as the influence of the American Dream and the Great Depression. It also makes a detailed study on the flaws in the character of Willy Loman, some of which contribute to his own death, such as his misguided social values and his twisted relationship with his family. The paper aims at a further study on Willy Loman’s death and to put forward the author’s view on various causes of his death. Then it concludes that Willy’s death is the result of American society and his own character defect
Mira Variable Stars From LAMOST DR4 Data: Emission Features, Temperature Types, and Candidate Selection
Based on an extensive spectral study of a photometrically confirmed sample of
Mira variables, we find a relationship between relative Balmer emission-line
strength and spectral temperature of O-rich Mira stars. The flux ratio increases from less than unity to five as
stars cool down from M0 to M10, which is likely driven by increasing TiO
absorption above the deepest shock-emitting regions. We also discuss the
relationship between the equivalent widths of the Balmer emission lines and the
photometric luminosity phase of our Mira sample stars. Using our 291 Mira
spectra as templates for reference, 191 Mira candidates are newly identified
from the LAMOST DR4 catalog. We summarize the criteria adopted to select Mira
candidates based on emission-line indices and molecular absorption bands. This
enlarged spectral sample of Mira variables has the potential to contribute
significantly to our knowledge of the optical properties of Mira stars and will
facilitate further studies of these late-type, long-period variables.Comment: 21 pages; ApJS, in pres
Discrete Distribution Estimation under User-level Local Differential Privacy
We study discrete distribution estimation under user-level local differential
privacy (LDP). In user-level -LDP, each user has samples
and the privacy of all samples must be preserved simultaneously. We resolve
the following dilemma: While on the one hand having more samples per user
should provide more information about the underlying distribution, on the other
hand, guaranteeing the privacy of all samples should make the estimation
task more difficult. We obtain tight bounds for this problem under almost all
parameter regimes. Perhaps surprisingly, we show that in suitable parameter
regimes, having samples per user is equivalent to having times more
users, each with only one sample. Our results demonstrate interesting phase
transitions for and the privacy parameter in the estimation
risk. Finally, connecting with recent results on shuffled DP, we show that
combined with random shuffling, our algorithm leads to optimal error guarantees
(up to logarithmic factors) under the central model of user-level DP in certain
parameter regimes. We provide several simulations to verify our theoretical
findings.Comment: 26 pages, 4 figure
Learning For Predictive Control: A Dual Gaussian Process Approach
An important issue in model-based control design is that an accurate dynamic
model of the system is generally nonlinear, complex, and costly to obtain. This
limits achievable control performance in practice. Gaussian process (GP) based
estimation of system models is an effective tool to learn unknown dynamics
directly from input/output data. However, conventional GP-based control methods
often ignore the computational cost associated with accumulating data during
the operation of the system and how to handle forgetting in continuous
adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based
model predictive control (MPC) strategy that enables efficient use of online
learning based predictive control without the danger of catastrophic
forgetting. The bio-inspired DGP structure is a combination of a long-term GP
and a short-term GP, where the long-term GP is used to keep the learned
knowledge in memory and the short-term GP is employed to rapidly compensate
unknown dynamics during online operation. Furthermore, a novel recursive online
update strategy for the short-term GP is proposed to successively improve the
learnt model during online operation. Effectiveness of the proposed strategy is
demonstrated via numerical simulations.Comment: arXiv admin note: substantial text overlap with arXiv:2112.1166
Learning For Predictive Control:A Dual Gaussian Process Approach
An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control (MPC) strategy that enables efficient use of online learning based predictive control without the danger of catastrophic forgetting. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation. Effectiveness of the proposed strategy is demonstrated via numerical simulations
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