8,086 research outputs found
Tuning the Chern number and Berry curvature with spin-orbit coupling and magnetic textures
We obtain the band structure of a particle moving in a magnetic spin texture,
classified by its chirality and structure factor, in the presence of spin-orbit
coupling. This rich interplay leads to a variety of novel topological phases
characterized by the Berry curvature and their associated Chern numbers. We
suggest methods of experimentally exploring these topological phases by Hall
drift measurements of the Chern number and Berry phase interferometry to map
the Berry curvature.Comment: 8 pages, 5 figure
Scene Induced Multi-Modal Trajectory Forecasting via Planning
We address multi-modal trajectory forecasting of agents in unknown scenes by
formulating it as a planning problem. We present an approach consisting of
three models; a goal prediction model to identify potential goals of the agent,
an inverse reinforcement learning model to plan optimal paths to each goal, and
a trajectory generator to obtain future trajectories along the planned paths.
Analysis of predictions on the Stanford drone dataset, shows generalizability
of our approach to novel scenes.Comment: ICRA Workshop on Long Term Human Motion Prediction (extended
abstract
Multi-scale Volumes for Deep Object Detection and Localization
This study aims to analyze the benefits of improved multi-scale reasoning for
object detection and localization with deep convolutional neural networks. To
that end, an efficient and general object detection framework which operates on
scale volumes of a deep feature pyramid is proposed. In contrast to the
proposed approach, most current state-of-the-art object detectors operate on a
single-scale in training, while testing involves independent evaluation across
scales. One benefit of the proposed approach is in better capturing of
multi-scale contextual information, resulting in significant gains in both
detection performance and localization quality of objects on the PASCAL VOC
dataset and a multi-view highway vehicles dataset. The joint detection and
localization scale-specific models are shown to especially benefit detection of
challenging object categories which exhibit large scale variation as well as
detection of small objects.Comment: To appear in Pattern Recognition 201
Temperature-driven BCS-BEC crossover in a coupled boson-fermion system
We propose a simple bose-fermi model in two dimensions, with a coupling that
converts pairs of opposite spin fermions into localized bosons and vice versa.
We show that tracing out one of the degrees, either the bosons or fermions,
generates temperature-dependent long range effective interactions between
bosons as well as effective attractive interactions between fermions. Using
Monte Carlo techniques we obtain the thermodynamic properties and phase
stiffness as a function of temperature, dominated by vortex-antivortex
unbinding of the bosons. Remarkably in the fermion sector we observe a
temperature-induced BCS-BEC crossover signaled by a distinct change of their
spectral properties: the minimum gap locus moves from the Fermi wave vector to
the point. Such a model is relevant for describing aspects of high
superconductivity in cuprates and pnictides, superconducting islands on
graphene, and bose-fermi mixtures in cold atomic systems.Comment: 10 pages, 10 figure
Looking at Hands in Autonomous Vehicles: A ConvNet Approach using Part Affinity Fields
In the context of autonomous driving, where humans may need to take over in
the event where the computer may issue a takeover request, a key step towards
driving safety is the monitoring of the hands to ensure the driver is ready for
such a request. This work, focuses on the first step of this process, which is
to locate the hands. Such a system must work in real-time and under varying
harsh lighting conditions. This paper introduces a fast ConvNet approach, based
on the work of original work of OpenPose for full body joint estimation. The
network is modified with fewer parameters and retrained using our own day-time
naturalistic autonomous driving dataset to estimate joint and affinity heatmaps
for driver & passenger's wrist and elbows, for a total of 8 joint classes and
part affinity fields between each wrist-elbow pair. The approach runs real-time
on real-world data at 40 fps on multiple drivers and passengers. The system is
extensively evaluated both quantitatively and qualitatively, showing at least
95% detection performance on joint localization and arm-angle estimation.Comment: 11 pages, 8 figures, 1 table. Submitted to "IEEE Transactions on
Intelligent Vehicles" (under review
Convolutional Social Pooling for Vehicle Trajectory Prediction
Forecasting the motion of surrounding vehicles is a critical ability for an
autonomous vehicle deployed in complex traffic. Motion of all vehicles in a
scene is governed by the traffic context, i.e., the motion and relative spatial
configuration of neighboring vehicles. In this paper we propose an LSTM
encoder-decoder model that uses convolutional social pooling as an improvement
to social pooling layers for robustly learning interdependencies in vehicle
motion. Additionally, our model outputs a multi-modal predictive distribution
over future trajectories based on maneuver classes. We evaluate our model using
the publicly available NGSIM US-101 and I-80 datasets. Our results show
improvement over the state of the art in terms of RMS values of prediction
error and negative log-likelihoods of true future trajectories under the
model's predictive distribution. We also present a qualitative analysis of the
model's predicted distributions for various traffic scenarios.Comment: Accepted for publication at CVPR TrajNet Workshop, 2018. arXiv admin
note: text overlap with arXiv:1805.0549
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs
Online multi-object tracking (MOT) is extremely important for high-level
spatial reasoning and path planning for autonomous and highly-automated
vehicles. In this paper, we present a modular framework for tracking multiple
objects (vehicles), capable of accepting object proposals from different sensor
modalities (vision and range) and a variable number of sensors, to produce
continuous object tracks. This work is a generalization of the MDP framework
for MOT, with some key extensions - First, we track objects across multiple
cameras and across different sensor modalities. This is done by fusing object
proposals across sensors accurately and efficiently. Second, the objects of
interest (targets) are tracked directly in the real world. This is a departure
from traditional techniques where objects are simply tracked in the image
plane. Doing so allows the tracks to be readily used by an autonomous agent for
navigation and related tasks.
To verify the effectiveness of our approach, we test it on real world highway
data collected from a heavily sensorized testbed capable of capturing
full-surround information. We demonstrate that our framework is well-suited to
track objects through entire maneuvers around the ego-vehicle, some of which
take more than a few minutes to complete. We also leverage the modularity of
our approach by comparing the effects of including/excluding different sensors,
changing the total number of sensors, and the quality of object proposals on
the final tracking result
Aspects of Entanglement Entropy for Gauge Theories
A definition for the entanglement entropy in a gauge theory was given
recently in arXiv:1501.02593. Working on a spatial lattice, it involves
embedding the physical state in an extended Hilbert space obtained by taking
the tensor product of the Hilbert space of states on each link of the lattice.
This extended Hilbert space admits a tensor product decomposition by definition
and allows a density matrix and entanglement entropy for the set of links of
interest to be defined. Here, we continue the study of this extended Hilbert
space definition with particular emphasis on the case of Non-Abelian gauge
theories.
We extend the electric centre definition of Casini, Huerta and Rosabal to the
Non-Abelian case and find that it differs in an important term. We also find
that the entanglement entropy does not agree with the maximum number of Bell
pairs that can be extracted by the processes of entanglement distillation or
dilution, and give protocols which achieve the maximum bound. Finally, we
compute the topological entanglement entropy which follows from the extended
Hilbert space definition and show that it correctly reproduces the total
quantum dimension in a class of Toric code models based on Non-Abelian discrete
groups.Comment: Discussion of Non-Abelian Toric code corrected to agree with
arXiv:1511.04369; some related comments revised and typos corrected; 45
pages, 2 figure
Learning to Detect Vehicles by Clustering Appearance Patterns
This paper studies efficient means for dealing with intra-category diversity
in object detection. Strategies for occlusion and orientation handling are
explored by learning an ensemble of detection models from visual and
geometrical clusters of object instances. An AdaBoost detection scheme is
employed with pixel lookup features for fast detection. The analysis provides
insight into the design of a robust vehicle detection system, showing promise
in terms of detection performance and orientation estimation accuracy.Comment: Preprint version of our T-ITS 2015 pape
Simulation of Flux Lines with Columnar Pins: Bose Glass and Entangled Liquids
Using path integral Monte Carlo we simulate a 3D system of up to 1000
magnetic flux lines by mapping it onto a system of interacting bosons in
(2+1)D. With increasing temperature we find a first order melting of flux lines
from an ordered solid to an entangled liquid signalled by a finite entropy jump
and sharp discontinuities in the defect density and the structure factor
at the first reciprocal lattice vector. In the presence of a small
number of strong columnar pins, we find that the crystal is transformed into a
Bose glass phase with patches of crystalline order nucleated around the trapped
vortices but with no overall positional or orientational order. This glassy
phase melts into a defected entangled liquid through a continuous transition.Comment: 4 pages, 5 figures, one figure in .gif format; use xv to convert to
.ep
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