The Transfer Paradox in a One-Sector Overlapping Generations Model

This paper examines the effects of international income transfers on welfare and capital accumulation in a one-sector overlapping generations model. It is shown that a strong form of the transfer paradox-- in which the donor country experiences a welfare gain while the recipient country experiences a welfare loss—may occur both in and out of steady state. In addition, it is shown that a weak form of the transfer paradox—where either the donor or recipient (but not both) experience paradoxical welfare effects—may characterize all segments of the transition path not already characterized by the strong transfer paradox. The results are explained by the effects of transfers on world capital accumulation and the world interest rate, which imply secondary intertemporal welfare effects large enough to dominate the initial effects of the income transfer.Transfer problem, transfer paradox, dynamics, one-sector overlapping generations model

Multistage Zeeman deceleration of atomic and molecular oxygen

Multistage Zeeman deceleration is a technique used to reduce the velocity of neutral molecules with a magnetic dipole moment. Here we present a Zeeman decelerator that consists of 100 solenoids and 100 magnetic hexapoles, that is based on a short prototype design presented recently [Phys. Rev. A 95, 043415 (2017)]. The decelerator features a modular design with excellent thermal and vacuum properties, and is robustly operated at a 10 Hz repetition rate. This multistage Zeeman decelerator is particularly optimized to produce molecular beams for applications in crossed beam molecular scattering experiments. We characterize the decelerator using beams of atomic and molecular oxygen. For atomic oxygen, the magnetic fields produced by the solenoids are used to tune the final longitudinal velocity in the 500 - 125 m/s range, while for molecular oxygen the velocity is tunable in the 350 - 150 m/s range. This corresponds to a maximum kinetic energy reduction of 95% and 80% for atomic and molecular oxygen, respectively.Comment: Latest version as accepted by Physical Review

A deep level set method for image segmentation

This paper proposes a novel image segmentation approachthat integrates fully convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the integrated method can incorporatesmoothing and prior information to achieve an accurate segmentation.Furthermore, different than using the level set model as a post-processingtool, we integrate it into the training phase to fine-tune the FCN. Thisallows the use of unlabeled data during training in a semi-supervisedsetting. Using two types of medical imaging data (liver CT and left ven-tricle MRI data), we show that the integrated method achieves goodperformance even when little training data is available, outperformingthe FCN or the level set model alone

Iterative graph cuts for image segmentation with a nonlinear statistical shape prior

Shape-based regularization has proven to be a useful method for delineating objects within noisy images where one has prior knowledge of the shape of the targeted object. When a collection of possible shapes is available, the specification of a shape prior using kernel density estimation is a natural technique. Unfortunately, energy functionals arising from kernel density estimation are of a form that makes them impossible to directly minimize using efficient optimization algorithms such as graph cuts. Our main contribution is to show how one may recast the energy functional into a form that is minimizable iteratively and efficiently using graph cuts.Comment: Revision submitted to JMIV (02/24/13

HDSDF: Hybrid Directional and Signed Distance Functions for Fast Inverse Rendering

Implicit neural representations of 3D shapes form strong priors that areuseful for various applications, such as single and multiple view 3Dreconstruction. A downside of existing neural representations is that theyrequire multiple network evaluations for rendering, which leads to highcomputational costs. This limitation forms a bottleneck particularly in thecontext of inverse problems, such as image-based 3D reconstruction. To addressthis issue, in this paper (i) we propose a novel hybrid 3D objectrepresentation based on a signed distance function (SDF) that we augment with adirectional distance function (DDF), so that we can predict distances to theobject surface from any point on a sphere enclosing the object. Moreover, (ii)using the proposed hybrid representation we address the multi-view consistencyproblem common in existing DDF representations. We evaluate our novel hybridrepresentation on the task of single-view depth reconstruction and show thatour method is several times faster compared to competing methods, while at thesame time achieving better reconstruction accuracy.<br