780 research outputs found
Cosmological Density and Power Spectrum from Peculiar Velocities: Nonlinear Corrections and PCA
We allow for nonlinear effects in the likelihood analysis of galaxy peculiar
velocities, and obtain ~35%-lower values for the cosmological density parameter
Om and the amplitude of mass-density fluctuations. The power spectrum in the
linear regime is assumed to be a flat LCDM model (h=0.65, n=1, COBE) with only
Om as a free parameter. Since the likelihood is driven by the nonlinear regime,
we "break" the power spectrum at k_b=0.2 h/Mpc and fit a power law at k>k_b.
This allows for independent matching of the nonlinear behavior and an unbiased
fit in the linear regime. The analysis assumes Gaussian fluctuations and
errors, and a linear relation between velocity and density. Tests using proper
mock catalogs demonstrate a reduced bias and a better fit. We find for the
Mark3 and SFI data Om_m=0.32+-0.06 and 0.37+-0.09 respectively, with
sigma_8*Om^0.6 = 0.49+-0.06 and 0.63+-0.08, in agreement with constraints from
other data. The quoted 90% errors include cosmic variance. The improvement in
likelihood due to the nonlinear correction is very significant for Mark3 and
moderately so for SFI. When allowing deviations from LCDM, we find an
indication for a wiggle in the power spectrum: an excess near k=0.05 and a
deficiency at k=0.1 (cold flow). This may be related to the wiggle seen in the
power spectrum from redshift surveys and the second peak in the CMB anisotropy.
A chi^2 test applied to modes of a Principal Component Analysis (PCA) shows
that the nonlinear procedure improves the goodness of fit and reduces a spatial
gradient of concern in the linear analysis. The PCA allows addressing spatial
features of the data and fine-tuning the theoretical and error models. It shows
that the models used are appropriate for the cosmological parameter estimation
performed. We address the potential for optimal data compression using PCA.Comment: 18 pages, LaTex, uses emulateapj.sty, ApJ in press (August 10, 2001),
improvements to text and figures, updated reference
Accurate and linear time pose estimation from points and lines
The final publication is available at link.springer.comThe Perspective-n-Point (PnP) problem seeks to estimate the pose of a calibrated camera from n 3Dto-2D point correspondences. There are situations, though, where PnP solutions are prone to fail because feature point correspondences cannot be reliably estimated (e.g. scenes with repetitive patterns or with low texture). In such
scenarios, one can still exploit alternative geometric entities, such as lines, yielding the so-called Perspective-n-Line (PnL) algorithms. Unfortunately, existing PnL solutions are not as accurate and efficient as their point-based
counterparts. In this paper we propose a novel approach to introduce 3D-to-2D line correspondences into a PnP formulation, allowing to simultaneously process points and lines. For this purpose we introduce an algebraic line error
that can be formulated as linear constraints on the line endpoints, even when these are not directly observable. These constraints can then be naturally integrated within the linear formulations of two state-of-the-art point-based algorithms,
the OPnP and the EPnP, allowing them to indistinctly handle points, lines, or a combination of them. Exhaustive experiments show that the proposed formulation brings remarkable boost in performance compared to only point or
only line based solutions, with a negligible computational overhead compared to the original OPnP and EPnP.Peer ReviewedPostprint (author's final draft
Some open questions in "wave chaos"
The subject area referred to as "wave chaos", "quantum chaos" or "quantum
chaology" has been investigated mostly by the theoretical physics community in
the last 30 years. The questions it raises have more recently also attracted
the attention of mathematicians and mathematical physicists, due to connections
with number theory, graph theory, Riemannian, hyperbolic or complex geometry,
classical dynamical systems, probability etc. After giving a rough account on
"what is quantum chaos?", I intend to list some pending questions, some of them
having been raised a long time ago, some others more recent
Alpha-N: Shortest Path Finder Automated Delivery Robot with Obstacle Detection and Avoiding System
Alpha N A self-powered, wheel driven Automated Delivery Robot is presented in
this paper. The ADR is capable of navigating autonomously by detecting and
avoiding objects or obstacles in its path. It uses a vector map of the path and
calculates the shortest path by Grid Count Method of Dijkstra Algorithm.
Landmark determination with Radio Frequency Identification tags are placed in
the path for identification and verification of source and destination, and
also for the recalibration of the current position. On the other hand, an
Object Detection Module is built by Faster RCNN with VGGNet16 architecture for
supporting path planning by detecting and recognizing obstacles. The Path
Planning System is combined with the output of the GCM, the RFID Reading System
and also by the binary results of ODM. This PPS requires a minimum speed of 200
RPM and 75 seconds duration for the robot to successfully relocate its position
by reading an RFID tag. In the result analysis phase, the ODM exhibits an
accuracy of 83.75 percent, RRS shows 92.3 percent accuracy and the PPS
maintains an accuracy of 85.3 percent. Stacking all these 3 modules, the ADR is
built, tested and validated which shows significant improvement in terms of
performance and usability comparing with other service robots.Comment: 12 pages, 7 figures, To be appear in the proceedings of 12th Asian
Conference on Intelligent Information and Database Systems 23-26 March 2020
Phuket, Thailan
Associative3D: Volumetric Reconstruction from Sparse Views
This paper studies the problem of 3D volumetric reconstruction from two views
of a scene with an unknown camera. While seemingly easy for humans, this
problem poses many challenges for computers since it requires simultaneously
reconstructing objects in the two views while also figuring out their
relationship. We propose a new approach that estimates reconstructions,
distributions over the camera/object and camera/camera transformations, as well
as an inter-view object affinity matrix. This information is then jointly
reasoned over to produce the most likely explanation of the scene. We train and
test our approach on a dataset of indoor scenes, and rigorously evaluate the
merits of our joint reasoning approach. Our experiments show that it is able to
recover reasonable scenes from sparse views, while the problem is still
challenging. Project site: https://jasonqsy.github.io/Associative3DComment: ECCV 202
Generic 3D Representation via Pose Estimation and Matching
Though a large body of computer vision research has investigated developing
generic semantic representations, efforts towards developing a similar
representation for 3D has been limited. In this paper, we learn a generic 3D
representation through solving a set of foundational proxy 3D tasks:
object-centric camera pose estimation and wide baseline feature matching. Our
method is based upon the premise that by providing supervision over a set of
carefully selected foundational tasks, generalization to novel tasks and
abstraction capabilities can be achieved. We empirically show that the internal
representation of a multi-task ConvNet trained to solve the above core problems
generalizes to novel 3D tasks (e.g., scene layout estimation, object pose
estimation, surface normal estimation) without the need for fine-tuning and
shows traits of abstraction abilities (e.g., cross-modality pose estimation).
In the context of the core supervised tasks, we demonstrate our representation
achieves state-of-the-art wide baseline feature matching results without
requiring apriori rectification (unlike SIFT and the majority of learned
features). We also show 6DOF camera pose estimation given a pair local image
patches. The accuracy of both supervised tasks come comparable to humans.
Finally, we contribute a large-scale dataset composed of object-centric street
view scenes along with point correspondences and camera pose information, and
conclude with a discussion on the learned representation and open research
questions.Comment: Published in ECCV16. See the project website
http://3drepresentation.stanford.edu/ and dataset website
https://github.com/amir32002/3D_Street_Vie
A finite model of two-dimensional ideal hydrodynamics
A finite-dimensional su() Lie algebra equation is discussed that in the
infinite limit (giving the area preserving diffeomorphism group) tends to
the two-dimensional, inviscid vorticity equation on the torus. The equation is
numerically integrated, for various values of , and the time evolution of an
(interpolated) stream function is compared with that obtained from a simple
mode truncation of the continuum equation. The time averaged vorticity moments
and correlation functions are compared with canonical ensemble averages.Comment: (25 p., 7 figures, not included. MUTP/92/1
Scene Segmentation Driven by Deep Learning and Surface Fitting
This paper proposes a joint color and depth segmentation scheme exploiting together geometrical clues and a learning stage. The approach starts from an initial over-segmentation based on spectral clustering. The input data is also fed to a Convolutional Neural Network (CNN) thus producing a per-pixel descriptor vector for each scene sample. An iterative merging procedure is then used to recombine the segments into the regions corresponding to the various objects and surfaces. The proposed algorithm starts by considering all the adjacent segments and computing a similarity metric according to the CNN features. The couples of segments with higher similarity are considered for merging. Finally the algorithm uses a NURBS surface fitting scheme on the segments in order to understand if the selected couples correspond to a single surface. The comparison with state-of-the-art methods shows how the proposed method provides an accurate and reliable scene segmentation
Mass equidistribution of Hilbert modular eigenforms
Let F be a totally real number field, and let f traverse a sequence of
non-dihedral holomorphic eigencuspforms on GL(2)/F of weight (k_1,...,k_n),
trivial central character and full level. We show that the mass of f
equidistributes on the Hilbert modular variety as max(k_1,...,k_n) tends to
infinity.
Our result answers affirmatively a natural analogue of a conjecture of
Rudnick and Sarnak (1994). Our proof generalizes the argument of
Holowinsky-Soundararajan (2008) who established the case F = Q. The essential
difficulty in doing so is to adapt Holowinsky's bounds for the Weyl periods of
the equidistribution problem in terms of manageable shifted convolution sums of
Fourier coefficients to the case of a number field with nontrivial unit group.Comment: 40 pages; typos corrected, nearly accepted for
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Debiasing Decisions. Improved Decision Making With A Single Training Intervention
From failures of intelligence analysis to misguided beliefs about vaccinations, biased judgment and decision making contributes to problems in policy, business, medicine, law, and private life. Early attempts to reduce decision biases with training met with little success, leading scientists and policy makers to focus on debiasing by using incentives and changes in the presentation and elicitation of decisions. We report the results of two longitudinal experiments that found medium to large effects of one-shot debiasing training interventions. Participants received a single training intervention, played a computer game or watched an instructional video, which addressed biases critical to intelligence analysis (in Experiment 1: bias blind spot, confirmation bias, and fundamental attribution error; in Experiment 2: anchoring, representativeness, and social projection). Both kinds of interventions produced medium to large debiasing effects immediately (games ≥ -31.94% and videos ≥ -18.60%) that persisted at least 2 months later (games ≥ -23.57% and videos ≥ -19.20%). Games, which provided personalized feedback and practice, produced larger effects than did videos. Debiasing effects were domain-general: bias reduction occurred across problems in different contexts, and problem formats that were taught and not taught in the interventions. The results suggest that a single training intervention can improve decision making. We suggest its use alongside improved incentives, information presentation, and nudges to reduce costly errors associated with biased judgments and decisions
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