1,295 research outputs found
HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting
Purpose: Magnetic resonance fingerprinting (MRF) methods typically rely on
dictio-nary matching to map the temporal MRF signals to quantitative tissue
parameters. Such approaches suffer from inherent discretization errors, as well
as high computational complexity as the dictionary size grows. To alleviate
these issues, we propose a HYbrid Deep magnetic ResonAnce fingerprinting
approach, referred to as HYDRA.
Methods: HYDRA involves two stages: a model-based signature restoration phase
and a learning-based parameter restoration phase. Signal restoration is
implemented using low-rank based de-aliasing techniques while parameter
restoration is performed using a deep nonlocal residual convolutional neural
network. The designed network is trained on synthesized MRF data simulated with
the Bloch equations and fast imaging with steady state precession (FISP)
sequences. In test mode, it takes a temporal MRF signal as input and produces
the corresponding tissue parameters.
Results: We validated our approach on both synthetic data and anatomical data
generated from a healthy subject. The results demonstrate that, in contrast to
conventional dictionary-matching based MRF techniques, our approach
significantly improves inference speed by eliminating the time-consuming
dictionary matching operation, and alleviates discretization errors by
outputting continuous-valued parameters. We further avoid the need to store a
large dictionary, thus reducing memory requirements.
Conclusions: Our approach demonstrates advantages in terms of inference
speed, accuracy and storage requirements over competing MRF method
Optimal quantum detectors for unambiguous detection of mixed states
We consider the problem of designing an optimal quantum detector that
distinguishes unambiguously between a collection of mixed quantum states. Using
arguments of duality in vector space optimization, we derive necessary and
sufficient conditions for an optimal measurement that maximizes the probability
of correct detection. We show that the previous optimal measurements that were
derived for certain special cases satisfy these optimality conditions. We then
consider state sets with strong symmetry properties, and show that the optimal
measurement operators for distinguishing between these states share the same
symmetries, and can be computed very efficiently by solving a reduced size
semidefinite program.Comment: Submitted to Phys. Rev.
What's Psychology Worth? A Field Experiment in the Consumer Credit Market
Numerous laboratory studies report on behaviors inconsistent with rational economic models. How much do these inconsistencies matter in natural settings, when consumers make large, real decisions and have the opportunity to learn from experiences? We report on a field experiment designed to address this question. Incumbent clients of a lender in South Africa were sent letters offering them large, short-term loans at randomly chosen interest rates. Psychological “features” on the letter, which did not affect offer terms or economic content, were also independently randomized. Consistent with standard economics, the interest rate significantly affected loan take-up. Inconsistent with standard economics, the psychological features also significantly affected take-up. The independent randomizations allow us to quantify the relative importance of psychological features and prices. Our core finding is the sheer magnitude of the psychological effects. On average, any one psychological manipulation has the same effect as a one half percentage point change in the monthly interest rate. Interestingly, the psychological features appear to have greater impact in the context of less advantageous offers. Moreover, the psychological features do not appear to draw in marginally worse clients, nor does the magnitude of the psychological effects vary systematically with income or education. In short, even in a market setting with large stakes and experienced customers, subtle psychological features that normatively ought to have no impact appear to be extremely powerful drivers of behavior.Behavioral economics, psychology, microfinance, marketing, field experiment, credit markets
On Model-Based RIP-1 Matrices
The Restricted Isometry Property (RIP) is a fundamental property of a matrix
enabling sparse recovery. Informally, an m x n matrix satisfies RIP of order k
in the l_p norm if ||Ax||_p \approx ||x||_p for any vector x that is k-sparse,
i.e., that has at most k non-zeros. The minimal number of rows m necessary for
the property to hold has been extensively investigated, and tight bounds are
known. Motivated by signal processing models, a recent work of Baraniuk et al
has generalized this notion to the case where the support of x must belong to a
given model, i.e., a given family of supports. This more general notion is much
less understood, especially for norms other than l_2. In this paper we present
tight bounds for the model-based RIP property in the l_1 norm. Our bounds hold
for the two most frequently investigated models: tree-sparsity and
block-sparsity. We also show implications of our results to sparse recovery
problems.Comment: Version 3 corrects a few errors present in the earlier version. In
particular, it states and proves correct upper and lower bounds for the
number of rows in RIP-1 matrices for the block-sparse model. The bounds are
of the form k log_b n, not k log_k n as stated in the earlier versio
Undersampled Phase Retrieval with Outliers
We propose a general framework for reconstructing transform-sparse images
from undersampled (squared)-magnitude data corrupted with outliers. This
framework is implemented using a multi-layered approach, combining multiple
initializations (to address the nonconvexity of the phase retrieval problem),
repeated minimization of a convex majorizer (surrogate for a nonconvex
objective function), and iterative optimization using the alternating
directions method of multipliers. Exploiting the generality of this framework,
we investigate using a Laplace measurement noise model better adapted to
outliers present in the data than the conventional Gaussian noise model. Using
simulations, we explore the sensitivity of the method to both the
regularization and penalty parameters. We include 1D Monte Carlo and 2D image
reconstruction comparisons with alternative phase retrieval algorithms. The
results suggest the proposed method, with the Laplace noise model, both
increases the likelihood of correct support recovery and reduces the mean
squared error from measurements containing outliers. We also describe exciting
extensions made possible by the generality of the proposed framework, including
regularization using analysis-form sparsity priors that are incompatible with
many existing approaches.Comment: 11 pages, 9 figure
The Case for Behaviorally Informed Regulation
Policymakers approach human behavior largely through the perspective of the “rational agent” model, which relies on normative, a priori analyses of the making of rational decisions. This perspective is promoted in the social sciences and in professional schools, and has come to dominate much of the formulation and conduct of policy. An alternative view, developed mostly through empirical behavioral research, provides a substantially different perspective on individual behavior and its policy implications. Behavior, according to the empirical perspective, is the outcome of perceptions, impulses, and other processes that characterize the impressive machinery that we carry behind the eyes and between the ears. These proclivities, research has shown, intrude upon and shape behavior, often quite independently of deliberative intent, and in contrast with normative ideals that people endorse upon reflection. The results are systematic behaviors that are unforeseen and misunderstood by classical policy thinking. A more nuanced behavioral perspective, such research suggests, can yield deeper understanding and improved regulatory insight
Behaviorally Informed Financial Services Regulation
Financial services decisions can have enourmous consequences for household well-being. Households need a range of financial services - to conduct basic transactions, such as receiving their income, storing it, and paying bills; to save for emergency needs and long-term goals; to access credit; and to insure against life\u27s key risks. But the financial services system is exceedingly complicated and often not well-designed to optimize house-hold behavior. In response to the complexity of out financial system, there has been a long running debate about the appropriate role and form of regulation. Regulation is largely stuck in two competing models - disclosure, and usury or product restrictions. This paper explores a different approach, based on insights from behavioral economics on one hand, and an understanding of industrial organization on the other. At the core of the analysis is the interaction between individual psychology and market competition. This is in contrast to the classic model, which relies on the interaction between rational choice and market competition. The introduction of richer psychology complicates the impact of competition. It helps us understand that firms compete based on how individuals will respond to products in the marketplace, and competitive outcomes may not always and in all contexts closely align with improved decisional choice and increased consumer welfare. This paper adopts a behavioral economic framework that considers firm incentives to respond to regulation. Under this framework, outcomes are an equilibrium interaction between individuals with specific psychologies and firms that responds to those psychologies within specific market contexts. Regulation must then address failures in this equilibrium. The model suggests, for example, that in some contexts market participants seek to overcome common human failings (as for example, with under-saving) while in other contexts market participants seek to exploit those failings (as for example, with over-borrowing). Behaviorally informed regulation needs to take account of these different contexts. The paper discusses the specific application of these forces to the case of mortage, credit card, and banking markets. The purpose of this paper is not to champion politics, but to illustrate how a behaviorally informed regulatory analysis would lead to a deeper understanding of the costs and benefits of specific policies
Quantum Detection with Unknown States
We address the problem of distinguishing among a finite collection of quantum
states, when the states are not entirely known. For completely specified
states, necessary and sufficient conditions on a quantum measurement minimizing
the probability of a detection error have been derived. In this work, we assume
that each of the states in our collection is a mixture of a known state and an
unknown state. We investigate two criteria for optimality. The first is
minimization of the worst-case probability of a detection error. For the second
we assume a probability distribution on the unknown states, and minimize of the
expected probability of a detection error.
We find that under both criteria, the optimal detectors are equivalent to the
optimal detectors of an ``effective ensemble''. In the worst-case, the
effective ensemble is comprised of the known states with altered prior
probabilities, and in the average case it is made up of altered states with the
original prior probabilities.Comment: Refereed version. Improved numerical examples and figures. A few
typos fixe
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