Why multi-tracer surveys beat cosmic variance

Galaxy surveys that map multiple species of tracers of large-scale structure can improve the constraints on some cosmological parameters far beyond the limits imposed by a simplistic interpretation of cosmic variance. This enhancement derives from comparing the relative clustering between different tracers of large-scale structure. We present a simple but fully generic expression for the Fisher information matrix of surveys with any (discrete) number of tracers, and show that the enhancement of the constraints on bias-sensitive parameters are a straightforward consequence of this multi-tracer Fisher matrix. In fact, the relative clustering amplitudes between tracers are eigenvectors of this multi-tracer Fisher matrix. The diagonalized multi-tracer Fisher matrix clearly shows that while the effective volume is bounded by the physical volume of the survey, the relational information between species is unbounded. As an application, we study the expected enhancements in the constraints of realistic surveys that aim at mapping several different types of tracers of large-scale structure. The gain obtained by combining multiple tracers is highest at low redshifts, and in one particular scenario we analyzed, the enhancement can be as large as a factor of ~3 for the accuracy in the determination of the redshift distortion parameter, and a factor ~5 for the local non-Gaussianity parameter. Radial and angular distance determinations from the baryonic features in the power spectrum may also benefit from the multi-tracer approach.Comment: New references included; 9 pages, 9 figure

LDSO: Direct Sparse Odometry with Loop Closure

In this paper we present an extension of Direct Sparse Odometry (DSO) to a monocular visual SLAM system with loop closure detection and pose-graph optimization (LDSO). As a direct technique, DSO can utilize any image pixel with sufficient intensity gradient, which makes it robust even in featureless areas. LDSO retains this robustness, while at the same time ensuring repeatability of some of these points by favoring corner features in the tracking frontend. This repeatability allows to reliably detect loop closure candidates with a conventional feature-based bag-of-words (BoW) approach. Loop closure candidates are verified geometrically and Sim(3) relative pose constraints are estimated by jointly minimizing 2D and 3D geometric error terms. These constraints are fused with a co-visibility graph of relative poses extracted from DSO's sliding window optimization. Our evaluation on publicly available datasets demonstrates that the modified point selection strategy retains the tracking accuracy and robustness, and the integrated pose-graph optimization significantly reduces the accumulated rotation-, translation- and scale-drift, resulting in an overall performance comparable to state-of-the-art feature-based systems, even without global bundle adjustment

When is Market Incompleteness Irrelevant for the Price of Aggregate Risk (and when is it not)?

In a standard incomplete markets model with a continuum of households that have constant relative risk aversion (CRRA) preferences, the absence of insurance markets for idiosyncratic labor income risk has no effect on the premium for aggregate risk if the distribution of idiosyncratic risk is independent of aggregate shocks and aggregate consumption growth is independent over time. In the equilibrium, which features trade and binding solvency constraints, as opposed to Constantinides and Duffie (1996), households only use the stock market to smooth consumption; the bond market is inoperative. Furthermore we show that the cross-sectional wealth and consumption distributions are not affected by aggregate shocks. These results hold regardless of the persistence of idiosyncratic shocks, and arise even when households face tight solvency constraints, but only a weaker irrelevance result survives when we allow for predictability in aggregate consumption growth.

Geo6D: Geometric Constraints Learning for 6D Pose Estimation

Numerous 6D pose estimation methods have been proposed that employ end-to-end regression to directly estimate the target pose parameters. Since the visible features of objects are implicitly influenced by their poses, the network allows inferring the pose by analyzing the differences in features in the visible region. However, due to the unpredictable and unrestricted range of pose variations, the implicitly learned visible feature-pose constraints are insufficiently covered by the training samples, making the network vulnerable to unseen object poses. To tackle these challenges, we proposed a novel geometric constraints learning approach called Geo6D for direct regression 6D pose estimation methods. It introduces a pose transformation formula expressed in relative offset representation, which is leveraged as geometric constraints to reconstruct the input and output targets of the network. These reconstructed data enable the network to estimate the pose based on explicit geometric constraints and relative offset representation mitigates the issue of the pose distribution gap. Extensive experimental results show that when equipped with Geo6D, the direct 6D methods achieve state-of-the-art performance on multiple datasets and demonstrate significant effectiveness, even with only 10% amount of data

The Composition of International Capital Flows: Risk Sharing Through Foreign Direct Investment

Evidence on international capital flows suggests that foreign direct investment (FDI) is less volatile than other financial flows. To explain this finding, I model international capital flows under the assumptions of imperfect enforcement of financial contracts and inalienability of FDI. Imperfect enforcement of contracts leads to endogenous financing constraints and the pricing of default risk. Inalienability implies that it is not as advantageous to expropriate FDI relative to other flows. These features combine to give a risk sharing advantage to FDI over other capital flows. This risk sharing advantage of FDI translates into a lower default premium and lower sensitivity to changes in a country's financing constraint. The model offers the new implication that financially constrained countries should borrow relatively more through FDI. This is because FDI is harder to expropriate and not because FDI is more productive or less volatile. Using several creditworthiness and country risk ratings to measure financing constraints, I present new evidence linking FDI and financing constraints. Moreover, numerical simulations of the model generate stronger serial correlation for FDI than for other flows into developing countries. This corroborates the view that non-FDI flows are more short-term and more likely to change direction.Foreign direct investment, intangible assets, volatility, risk sharing, imperfect enforcement, financing constraints, default risk, country risk