778 research outputs found
Quantum criticality in disordered bosonic optical lattices
Using the exact Bose-Fermi mapping, we study universal properties of
ground-state density distributions and finite-temperature quantum critical
behavior of one-dimensional hard-core bosons in trapped incommensurate optical
lattices. Through the analysis of universal scaling relations in the quantum
critical regime, we demonstrate that the superfluid to Bose glass transition
and the general phase diagram of disordered hard-core bosons can be uniquely
determined from finite-temperature density distributions of the trapped
disordered system.Comment: 4 pages, 5 figure
Phase diagram of frustrated mixed-spin ladders in the strong-coupling limit
We study the ground-state properties of frustrated Heisenberg ferrimagnetic
ladders with antiferromagnetic exchange interactions and two types of
alternating sublattice spins. In the limit of strong rung couplings, we show
that the mixed spin-1/2 and spin-1 ladders can be systematically mapped onto a
spin-1/2 Heisenberg model with additional next-nearest-neighbor exchanges. The
system is either in a ferrimagnetic state or in a critical spin-liquid state
depending on the competition between the spin exchanges along the legs and the
diagonal exchanges.Comment: 6 pages, 2 figur
Essays On Referral Programs And Preference Estimation
In this dissertation, we study referral programs and preference estimation in two essays. In the first essay, we propose that a firm can enhance the effectiveness of its referral program by promoting better matching between referred customers and the firm. We develop three treatments aimed at promoting better matching, including (1) offering current customers a gift before inviting them to refer friends, (2) notifying current customers about the value that they have received from the firm before inviting them to refer friends, and (3) rewarding referring customers based on the value of their referred customers. We test these three treatments by conducting two field experiments in collaboration with a Chinese online financial services firm. We find that all three treatments substantially enhanced the effectiveness of the focal referral program, measured for each current customer as the total value of his referred customers. We also find that the enhancement was primarily driven by the acquisition of higher-value new customers rather than the acquisition of more new customers. In addition, we investigate customer heterogeneity in treatment effects and explore the mechanisms through which these treatments impacted customer referrals. In the second essay, we develop a new model for effective modeling of consumer heterogeneity in choice-based conjoint estimation. Assuming that most variations in consumers\u27 partworth vectors are along a small number of orthogonal directions, we propose that shrinking the individual-level partworth vectors toward a low-dimensional affine subspace that is also inferred from data can be an effective approach to pooling information across consumers and modeling consumer heterogeneity. We develop a low-dimension learning model to implement this information pooling mechanism that builds on recent advances in rank minimization and machine learning. We evaluate the empirical performance of the low-dimension learning model using both simulation experiments and field choice-based conjoint data sets. We find that the low-dimension learning model overall outperforms multiple benchmark models in terms of both parameter recovery and predictive accuracy. While addressing two different marketing topics, both essays share a common theme - careful modeling of consumer heterogeneity plays a key role in understanding consumer behavior and developing effective marketing strategies
Finite Element Method Modeling Of Advanced Electronic Devices
In this dissertation, we use finite element method together with other numerical techniques to study advanced electron devices. We study the radiation properties in electron waveguide structure with multi-step discontinuities and soft wall lateral confinement. Radiation mechanism and conditions are examined by numerical simulation of dispersion relations and transport properties. The study of geometry variations shows its significant impact on the radiation intensity and direction. In particular, the periodic corrugation structure exhibits strong directional radiation. This interesting feature may be useful to design a nano-scale transmitter, a communication device for future nano-scale system. Non-quasi-static effects in AC characteristics of carbon nanotube field-effect transistors are examined by solving a full time-dependent, open-boundary Schrödinger equation. The non-quasi-static characteristics, such as the finite channel charging time, and the dependence of small signal transconductance and gate capacitance on the frequency, are explored. The validity of the widely used quasi-static approximation is examined. The results show that the quasi-static approximation overestimates the transconductance and gate capacitance at high frequencies, but gives a more accurate value for the intrinsic cut-off frequency over a wide range of bias conditions. The influence of metal interconnect resistance on the performance of vertical and lateral power MOSFETs is studied. Vertical MOSFETs in a D2PAK and DirectFET package, and lateral MOSFETs in power IC and flip chip are investigated as the case studies. The impact of various layout patterns and material properties on RDS(on) will provide useful guidelines for practical vertical and lateral power MOSFETs design
A Vector Matroid-Theoretic Approach in the Study of Structural Controllability Over F(z)
In this paper, the structural controllability of the systems over F(z) is
studied using a new mathematical method-matroids. Firstly, a vector matroid is
defined over F(z). Secondly, the full rank conditions of [sI-A|B] are derived
in terms of the concept related to matroid theory, such as rank, base and
union. Then the sufficient condition for the linear system and composite system
over F(z) to be structurally controllable is obtained. Finally, this paper
gives several examples to demonstrate that the married-theoretic approach is
simpler than other existing approaches
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models
The prediction accuracy has been the long-lasting and sole standard for
comparing the performance of different image classification models, including
the ImageNet competition. However, recent studies have highlighted the lack of
robustness in well-trained deep neural networks to adversarial examples.
Visually imperceptible perturbations to natural images can easily be crafted
and mislead the image classifiers towards misclassification. To demystify the
trade-offs between robustness and accuracy, in this paper we thoroughly
benchmark 18 ImageNet models using multiple robustness metrics, including the
distortion, success rate and transferability of adversarial examples between
306 pairs of models. Our extensive experimental results reveal several new
insights: (1) linear scaling law - the empirical and
distortion metrics scale linearly with the logarithm of classification error;
(2) model architecture is a more critical factor to robustness than model size,
and the disclosed accuracy-robustness Pareto frontier can be used as an
evaluation criterion for ImageNet model designers; (3) for a similar network
architecture, increasing network depth slightly improves robustness in
distortion; (4) there exist models (in VGG family) that exhibit
high adversarial transferability, while most adversarial examples crafted from
one model can only be transferred within the same family. Experiment code is
publicly available at \url{https://github.com/huanzhang12/Adversarial_Survey}.Comment: Accepted by the European Conference on Computer Vision (ECCV) 201
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