3,388 research outputs found
X-ICP: Localizability-Aware LiDAR Registration for Robust Localization in Extreme Environments
Modern robotic systems are required to operate in challenging environments,
which demand reliable localization under challenging conditions. LiDAR-based
localization methods, such as the Iterative Closest Point (ICP) algorithm, can
suffer in geometrically uninformative environments that are known to
deteriorate point cloud registration performance and push optimization toward
divergence along weakly constrained directions. To overcome this issue, this
work proposes i) a robust fine-grained localizability detection module, and ii)
a localizability-aware constrained ICP optimization module, which couples with
the localizability detection module in a unified manner. The proposed
localizability detection is achieved by utilizing the correspondences between
the scan and the map to analyze the alignment strength against the principal
directions of the optimization as part of its fine-grained LiDAR localizability
analysis. In the second part, this localizability analysis is then integrated
into the scan-to-map point cloud registration to generate drift-free pose
updates by enforcing controlled updates or leaving the degenerate directions of
the optimization unchanged. The proposed method is thoroughly evaluated and
compared to state-of-the-art methods in simulated and real-world experiments,
demonstrating the performance and reliability improvement in LiDAR-challenging
environments. In all experiments, the proposed framework demonstrates accurate
and generalizable localizability detection and robust pose estimation without
environment-specific parameter tuning.Comment: 20 Pages, 20 Figures Submitted to IEEE Transactions On Robotics.
Supplementary Video: https://youtu.be/SviLl7q69aA Project Website:
https://sites.google.com/leggedrobotics.com/x-ic
Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging
No abstract available
DeepMem: ML Models as storage channels and their (mis-)applications
Machine learning (ML) models are overparameterized to support generality and
avoid overfitting. Prior works have shown that these additional parameters can
be used for both malicious (e.g., hiding a model covertly within a trained
model) and beneficial purposes (e.g., watermarking a model). In this paper, we
propose a novel information theoretic perspective of the problem; we consider
the ML model as a storage channel with a capacity that increases with
overparameterization. Specifically, we consider a sender that embeds arbitrary
information in the model at training time, which can be extracted by a receiver
with a black-box access to the deployed model. We derive an upper bound on the
capacity of the channel based on the number of available parameters. We then
explore black-box write and read primitives that allow the attacker to: (i)
store data in an optimized way within the model by augmenting the training data
at the transmitter side, and (ii) to read it by querying the model after it is
deployed. We also analyze the detectability of the writing primitive and
consider a new version of the problem which takes information storage
covertness into account. Specifically, to obtain storage covertness, we
introduce a new constraint such that the data augmentation used for the write
primitives minimizes the distribution shift with the initial (baseline task)
distribution. This constraint introduces a level of "interference" with the
initial task, thereby limiting the channel's effective capacity. Therefore, we
develop optimizations to improve the capacity in this case, including a novel
ML-specific substitution based error correction protocol. We believe that the
proposed modeling of the problem offers new tools to better understand and
mitigate potential vulnerabilities of ML, especially in the context of
increasingly large models
An Investigation into Radiative Property Variations across Pre-Annealed Advanced High Strength Steel Coils
In recent years, increasingly stringent crashworthiness and emissions regulations have driven automakers to consider novel materials for automotive lightweighting. Advanced high-strength steels (AHSS) used in automotive chassis construction has increased considerably. The most widely used grades of AHSS are dual-phase (DP) ferrite-martensite (α + α') grades.
Advanced high strength steel coils must be annealed with a precise heating schedule to achieve the required mechanical properties. However, temperature excursions during intercritical annealing cause erratic changes in the steel's microstructure, resulting in variations in post-annealed mechanical properties across coils. These variations lead to high scrap rates and cost manufacturers millions of dollars annually. Past research has attributed these temperature excursions to non-uniform thermal irradiation. The present work shows variations in radiative properties across a single AHSS coil may cause temperature excursions through pyrometer errors and nonuniform heating. Radiative property variations across a coil may also arise before annealing due to non-homogeneities in surface topography, influencing how the radiative properties subsequently evolve during annealing.
This thesis documents experimental and theoretical work characterising radiative property variations across a single AHSS coil processed on an industrial cold-rolling line. The ex-situ radiative properties of samples extracted from various coil locations are analysed using a Fourier Transform Infra-Red (FTIR) spectrometer equipped with an integrating sphere, revealing large swings in radiative properties along its length and width. The effect of these variations on pyrometric temperature measurements, strip temperature evolution, and in turn, the as-formed mechanical properties are discussed.
Radiative property variations are strongly correlated to differences in surface topography (particularly surface cavities) through optical profilometry, optical microscopy, and scanning electron microscopy (SEM). The work uses 3D depth mapping of optical imagery to generate surface height maps and theoretically models the radiative properties using a geometric optics approximation (GOA) ray-tracing algorithm. The GOA approach provides accurate spectral emissivity predictions within its validity regime.
The study then explores reasons for surface cavity formation, hypothesising that cavities form due to the dissolution of selective grain boundary oxides (formed during hot rolling) during acid pickling, which leads to micro-topographical changes to the strip surface. Furthermore, non-homogeneous cold-rolling parameters subsequently lead to non-uniform cavity flattening. The thesis then explores the combined effect of acid pickling time and cold-rolling reduction percentage by studying different AHSS alloys, cold-rolled and acid-pickled to different extents, through a factorial design-of-experiments procedure.
An artificial neural network (ANN) regression model for near-instantaneous spectral emissivity predictions of AHSS was developed using surface roughness parameters and optical imagery as inputs. Manufacturers can implement this model with emerging in-situ strip imaging technologies to provide real-time spectral emissivity predictions before a coil section enters an annealing furnace. Galvanisers can also use these on-line spectral emissivity predictions to update pyrometry and furnace temperature control algorithms in real time.
This thesis expands our knowledge base on the possible causes for temperature excursions across an AHSS coil during annealing. Findings of this research will benefit steel manufacturers in identifying and reducing non-homogeneities in mechanical properties across AHSS coils, reducing high scrap rates in the industry
Investigating the impact of space weather on the polar atmosphere using rigorous statistical methods
I de senere år har det vært en økning i observasjonsbaserte, re-analytiske og modellbaserte studier som viser korrelasjoner mellom dag-til-dag og år-til-år solaktivitet og klima-/vær-mønstre. Det overordnede målet med avhandlingen er å undersøke to solklima-mekanismer, den Kjemisk-Dynamiske koblingen og Mansurov-effekten. Den Kjemisk-Dynamiske koblingen er knyttet til ioniseringen av den øvre atmosfæren (¡50 km) som skjer ved energisk partikkelnedbør (EPP). Dette resulterer i produksjon av nitrogen- og hydrogenoksider (NOx og HOx). Disse molekylene bryter effektivt ned ozon, og kan derfor endre strålingsbalansen i atmosfæren, noe som igjen potensielt kan føre til en kaskadeeffekt av dynamisk induserte atmosfæriske værendringer i polaratmosfæren. Mansurov-effekten er knyttet til det interplanetariske magnetfeltet (IMF) og dets evne til å modulere den globale elektriske kretsen (GEC). Dette antas å videre påvirke den polare troposfæren gjennom å endre de fysiske prosessene bak dannelse og vekst av skyer. Effekten antas å være nesten umiddelbar, noe som gir en fysisk forbindelse mellom verdensrommet og den nedre del av Jordens atmosfære. Begge mekanismene har blitt studert ved hjelp av sofistikerte statistiske analysemetoder.
For den Kjemisk-Dynamiske koblingen, bruker vi SOCOL3-MPIOM-modellen for å sammenligne temperaturforskjeller i den nordlige atmosfæren i modellkjøringen med og uten EPP. Analysen bygger på en nylig studie som viser at EPP hovedsakelig påvirker den nordlige atmosfæriske temperaturen rett før og under forstyrrede forhold i den stratosfæriske polare jetstrøm. Vi finner svært signifikante temperaturresponser rett før hendelser karakterisert som små stratosfæriske oppvarminger, forhold assosiert med en svekket polar jetstrøm og økt bølgeaktivitet. De største temperaturforskjellene er synlig i februar, men bare for den siste halvdel (1955–2008) av simuleringsperioden (1900–2008). Funnene antyder at den Kjemisk-Dynamiske koblingen kan spille en avgjørende rolle i stratosfæriske forhold om vinteren og bekrefter eksistensen av den Kjemisk-Dynamiske koblingen i modellen.
Ved å bruke data fra OMNIweb og ERA5 re-analyse over tidsperioden 1968–2020, undersøkes forbindelsen mellom IMF By og polart atmosfærisk trykk på havnivå. I motsetning til tidligere publiserte studier om Mansurov-effekten, finner vi ingen signifikant respons etter å ha tatt hensyn til autokorrelasjon og kontrollert for falsk deteksjonsandel (false discovery rate). Tidligere studier har også fremhevet en 27-dagers syklisk trykkrespons i sine resultater som indirekte bevis for en fysisk forbindelse. Vi demonstrerer at denne periodiske trykkresponsen oppstår som et resultat av de statistiske metodene som er brukt, og kan derfor ikke brukes som en indikator på en fysisk sammenheng. Videre oppdages en hittil ukjent robust og statistisk signifikant korrelasjon mellom IMF By og polart atmosfærisk trykk ved havnivå. Korrelasjonen er tydelig i perioden mars-april-mai på begge halvkuler, men med en tilsynelatende ufysisk timing med hensyn til Mansurov-effekten. I alt fremhever resultatene det generelle behovet for grundig statistisk testing, samt behovet for varsomhet når man bruker spesifikke metoder sammen med periodiske og autokorrelerte variabler.Recent years have seen a surge in observational, re-analysis, and model-based studies providing evidence of statistical correlations between day-to-day to interannual solar activity and climate/weather patterns. The overarching objective of this thesis is to delve into the theory of two solar-climate mechanisms, the Chemical-Dynamical coupling and the Mansurov effect. The Chemical-Dynamical coupling is linked to the ionization of the upper atmosphere (¡50 km) by energetic particle precipitation (EPP), resulting in the production of odd nitrogen and hydrogen oxides (NOx and HOx). These compounds are effective ozone depleters, and can alter the radiative balance of the atmosphere, potentially leading to a cascading effect in dynamically induced atmospheric weather changes observable in the polar atmosphere. The Mansurov effect is related to the interplanetary magnetic field (IMF) and its ability to modulate the global electric circuit (GEC), which is further assumed to impact the polar troposphere through cloud generation processes. It is hypothesised to occur nearly instantaneously, providing a physical link between near-Earth-space and the lower atmosphere. These topics will be studied with sophisticated statistical analysis methods.
For the Chemical-Dynamical coupling, we use the SOCOL3-MPIOM model to compare the northern polar atmospheric temperature differences in ensemble members with and without EPP. The analyses builds on recent re-analysis evidence showing that EPP mostly impacts the northern polar atmospheric temperature right before and during disturbed Polar Vortex (PV) conditions. We find highly significant temperature responses during conditions set up by minor Sudden Stratospheric Warmings (SSW), associated with disturbed polar vortex and enhanced planetary wave activity. The largest anomalies are seen in February, and only for the latter half (1955–2008) of the simulation period (1900–2008). The findings suggest that during winter, the Chemical-Dynamical coupling could play a crucial role in stratospheric conditions and confirms the existence of the chemical-dynamical link in the model.
By using ERA5 atmospheric re-analysis data and OMNIweb IMF data spanning 1968–2020, the connection between the IMF By and polar surface pressure is investigated. Contrary to prior published studies on the Mansurov effect, no significant response is found after accounting for autocorrelation and multiple hypothesis testing. In addition, prior studies highlight a 27-day cyclic pressure response as indirect evidence of a physical link. However, we show that this periodic pressure behaviour occurs as a statistical artefact of the methods, and is not a reliable indicator of a causal connection. Furthermore, a new robust and statistically significant correlation is determined between the IMF By and polar surface pressure. It is found in the time-period March-April-May for both hemispheres, but with an unphysical timing with respect to the Mansurov hypothesis. The analyses highlight the general need for rigorous statistical testing, as well as the need for caution when deploying certain methodologies with periodic and highly autocorrelated variables.Doktorgradsavhandlin
Fast emulation of anisotropies induced in the cosmic microwave background by cosmic strings
Cosmic strings are linear topological defects that may have been produced
during symmetry-breaking phase transitions in the very early Universe. In an
expanding Universe the existence of causally separate regions prevents such
symmetries from being broken uniformly, with a network of cosmic string
inevitably forming as a result. To faithfully generate observables of such
processes requires computationally expensive numerical simulations, which
prohibits many types of analyses. We propose a technique to instead rapidly
emulate observables, thus circumventing simulation. Emulation is a form of
generative modelling, often built upon a machine learning backbone. End-to-end
emulation often fails due to high dimensionality and insufficient training
data. Consequently, it is common to instead emulate a latent representation
from which observables may readily be synthesised. Wavelet phase harmonics are
an excellent latent representations for cosmological fields, both as a summary
statistic and for emulation, since they do not require training and are highly
sensitive to non-Gaussian information. Leveraging wavelet phase harmonics as a
latent representation, we develop techniques to emulate string induced CMB
anisotropies over a 7.2 degree field of view, with sub-arcminute resolution, in
under a minute on a single GPU. Beyond generating high fidelity emulations, we
provide a technique to ensure these observables are distributed correctly,
providing a more representative ensemble of samples. The statistics of our
emulations are commensurate with those calculated on comprehensive Nambu-Goto
simulations. Our findings indicate these fast emulation approaches may be
suitable for wide use in, e.g., simulation based inference pipelines. We make
our code available to the community so that researchers may rapidly emulate
cosmic string induced CMB anisotropies for their own analysis
Catastrophic overfitting can be induced with discriminative non-robust features
Adversarial training (AT) is the de facto method for building robust neural
networks, but it can be computationally expensive. To mitigate this, fast
single-step attacks can be used, but this may lead to catastrophic overfitting
(CO). This phenomenon appears when networks gain non-trivial robustness during
the first stages of AT, but then reach a breaking point where they become
vulnerable in just a few iterations. The mechanisms that lead to this failure
mode are still poorly understood. In this work, we study the onset of CO in
single-step AT methods through controlled modifications of typical datasets of
natural images. In particular, we show that CO can be induced at much smaller
values than it was observed before just by injecting images with
seemingly innocuous features. These features aid non-robust classification but
are not enough to achieve robustness on their own. Through extensive
experiments we analyze this novel phenomenon and discover that the presence of
these easy features induces a learning shortcut that leads to CO. Our findings
provide new insights into the mechanisms of CO and improve our understanding of
the dynamics of AT. The code to reproduce our experiments can be found at
https://github.com/gortizji/co_features.Comment: Published in Transactions on Machine Learning Research (TMLR
Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos
The success of the Neural Radiance Fields (NeRFs) for modeling and free-view
rendering static objects has inspired numerous attempts on dynamic scenes.
Current techniques that utilize neural rendering for facilitating free-view
videos (FVVs) are restricted to either offline rendering or are capable of
processing only brief sequences with minimal motion. In this paper, we present
a novel technique, Residual Radiance Field or ReRF, as a highly compact neural
representation to achieve real-time FVV rendering on long-duration dynamic
scenes. ReRF explicitly models the residual information between adjacent
timestamps in the spatial-temporal feature space, with a global
coordinate-based tiny MLP as the feature decoder. Specifically, ReRF employs a
compact motion grid along with a residual feature grid to exploit inter-frame
feature similarities. We show such a strategy can handle large motions without
sacrificing quality. We further present a sequential training scheme to
maintain the smoothness and the sparsity of the motion/residual grids. Based on
ReRF, we design a special FVV codec that achieves three orders of magnitudes
compression rate and provides a companion ReRF player to support online
streaming of long-duration FVVs of dynamic scenes. Extensive experiments
demonstrate the effectiveness of ReRF for compactly representing dynamic
radiance fields, enabling an unprecedented free-viewpoint viewing experience in
speed and quality.Comment: Accepted by CVPR 2023. Project page, see
https://aoliao12138.github.io/ReRF
ICEBEAR-3D: An Advanced Low Elevation Angle Auroral E region Imaging Radar
The Ionospheric Continuous-wave E region Bistatic Experimental Auroral Radar (ICEBEAR) is an auroral E~region radar which has operated from 7 December 2017 until the September 2019. During the first two years of operation, ICEBEAR was only capable of spatially locating E~region scatter and meteor trail targets in range and azimuth. Elevation angles were not determinable due to its East-West uniform linear receiving antenna array. Measuring elevation angles of targets when viewing from low elevation angles with radar interferometers has been a long standing problem. Past high latitude radars have attempted to obtain elevation angles of E~region targets using North-South baselines, but have always resulted in erroneous elevation angles being measured in the low elevation regime (0° to ≈30° above the horizon), leaving interesting scientific questions about scatter altitudes in the auroral E~region unanswered. The work entailed in this thesis encompasses the design of the ICEBEAR-3D system for the acquisition of these important elevation angles.
The receiver antenna array was redesigned using a custom phase error minimization and stochastic antenna location perturbation technique, which produces phase tolerant receiver antenna arrays. The resulting 45-baseline sparse non-uniform coplanar T-shaped array was designed for aperture synthesis radar imaging. Conventional aperture synthesis radar imaging techniques assume point-like incoherent targets and image using a Cartesian basis over a narrow field of view. These methods are incompatible with horizon pointing E~region radars such as ICEBEAR. Instead, radar targets were imaged using the Suppressed Spherical Wave Harmonic Transform (Suppressed-SWHT) technique. This imaging method uses precalculated spherical harmonic coefficient matrices to transform the visibilities to brightness maps by direct matrix multiplication. The under sampled image domain artefacts (dirty beam) were suppressed by the products of differing harmonic order brightness maps. From the images, elevation and azimuth angles of arrival were obtained. Due to the excellent phase tolerance of ICEBEAR new light was shed on the long standing low elevation angle problem. This led to the development of the proper phase reference vertical interferometry geometry, which allowed horizon pointing radar interferometers to unambiguously measure elevation angles near the horizon. Ultimately resulting in accurate elevation angles from zenith to horizon
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