1,599 research outputs found
MVFAN: Multi-View Feature Assisted Network for 4D Radar Object Detection
4D radar is recognized for its resilience and cost-effectiveness under
adverse weather conditions, thus playing a pivotal role in autonomous driving.
While cameras and LiDAR are typically the primary sensors used in perception
modules for autonomous vehicles, radar serves as a valuable supplementary
sensor. Unlike LiDAR and cameras, radar remains unimpaired by harsh weather
conditions, thereby offering a dependable alternative in challenging
environments. Developing radar-based 3D object detection not only augments the
competency of autonomous vehicles but also provides economic benefits. In
response, we propose the Multi-View Feature Assisted Network (\textit{MVFAN}),
an end-to-end, anchor-free, and single-stage framework for 4D-radar-based 3D
object detection for autonomous vehicles. We tackle the issue of insufficient
feature utilization by introducing a novel Position Map Generation module to
enhance feature learning by reweighing foreground and background points, and
their features, considering the irregular distribution of radar point clouds.
Additionally, we propose a pioneering backbone, the Radar Feature Assisted
backbone, explicitly crafted to fully exploit the valuable Doppler velocity and
reflectivity data provided by the 4D radar sensor. Comprehensive experiments
and ablation studies carried out on Astyx and VoD datasets attest to the
efficacy of our framework. The incorporation of Doppler velocity and RCS
reflectivity dramatically improves the detection performance for small moving
objects such as pedestrians and cyclists. Consequently, our approach culminates
in a highly optimized 4D-radar-based 3D object detection capability for
autonomous driving systems, setting a new standard in the field.Comment: 19 Pages, 7 figures, Accepted by ICONIP 202
Redemption from Range-view for Accurate 3D Object Detection
Most recent approaches for 3D object detection predominantly rely on
point-view or bird's-eye view representations, with limited exploration of
range-view-based methods. The range-view representation suffers from scale
variation and surface texture deficiency, both of which pose significant
limitations for developing corresponding methods. Notably, the surface texture
loss problem has been largely ignored by all existing methods, despite its
significant impact on the accuracy of range-view-based 3D object detection. In
this study, we propose Redemption from Range-view R-CNN (R2 R-CNN), a novel and
accurate approach that comprehensively explores the range-view representation.
Our proposed method addresses scale variation through the HD Meta Kernel, which
captures range-view geometry information in multiple scales. Additionally, we
introduce Feature Points Redemption (FPR) to recover the lost 3D surface
texture information from the range view, and Synchronous-Grid RoI Pooling
(S-Grid RoI Pooling), a multi-scaled approach with multiple receptive fields
for accurate box refinement. Our R2 R-CNN outperforms existing range-view-based
methods, achieving state-of-the-art performance on both the KITTI benchmark and
the Waymo Open Dataset. Our study highlights the critical importance of
addressing the surface texture loss problem for accurate 3D object detection in
range-view-based methods. Codes will be made publicly available
ThermRad: A Multi-modal Dataset for Robust 3D Object Detection under Challenging Conditions
Robust 3D object detection in extreme weather and illumination conditions is
a challenging task. While radars and thermal cameras are known for their
resilience to these conditions, few studies have been conducted on
radar-thermal fusion due to the lack of corresponding datasets. To address this
gap, we first present a new multi-modal dataset called ThermRad, which includes
a 3D LiDAR, a 4D radar, an RGB camera and a thermal camera. This dataset is
unique because it includes data from all four sensors in extreme weather
conditions, providing a valuable resource for future research in this area. To
validate the robustness of 4D radars and thermal cameras for 3D object
detection in challenging weather conditions, we propose a new multi-modal
fusion method called RTDF-RCNN, which leverages the complementary strengths of
4D radars and thermal cameras to boost object detection performance. To further
prove the effectiveness of our proposed framework, we re-implement
state-of-the-art (SOTA) 3D detectors on our dataset as benchmarks for
evaluation. Our method achieves significant enhancements in detecting cars,
pedestrians, and cyclists, with improvements of over 7.98%, 24.27%, and 27.15%,
respectively, while achieving comparable results to LiDAR-based approaches. Our
contributions in both the ThermRad dataset and the new multi-modal fusion
method provide a new approach to robust 3D object detection in adverse weather
and illumination conditions. The ThermRad dataset will be released.Comment: 12 pages, 5 figures, Proceedings of the IEEE/CVF International
Conference on Computer Visio
Floating binary planets from ejections during close stellar encounters
The discovery of planetary systems beyond our solar system has posed
challenges to established theories of planetary formation. Planetary orbits
display a variety of architectures not predicted by first principles, and
free-floating planets appear ubiquitous. The recent discovery of candidate
Jupiter Mass Binary Objects (JuMBOs) by the James Webb Space Telescope (JWST)
further expanded this enigma. Here, by means of high-accuracy, direct -body
simulations, we evaluate the possibility that JuMBOs may form as a result of
ejection after a close stellar flyby. We consider a system of two Jupiter-like
planets moving in circular orbits with velocities and at distances
and around a Sun-like star. The interloper is another Sun-like star
approaching with asymptotic velocity . We find that JuMBOs can indeed
be formed upon ejection if the two planets are nearly aligned as the interloper
reaches the closest approach. The ratio of the cross section of JuMBOs
production to that of single ejected free-floating planets can approach for and . JuMBOs
formed via this channel are expected to have an average semi-major axis
comparable to and high eccentricity, with a distinctive
superthermal distribution which can help to observationally identify this
formation channel and distinguish it from primordial formation. If the ejection
channel is confirmed for these or future JWST observations, these JuMBOs will
directly inform us of the conditions where these giant planets formed in
protoplanetary disks, putting stringent constraints on the giant planet
formation theory.Comment: 18 pages, 5 figures. Videos are available at
https://yihanwangastro.github.io/#posts
Technical and Economic Feasibility Analysis of a Conceptual Subsea Freight Glider for CO2 Transportation
This study analyses the technical and economic aspects of a novel subsea freight glider (SFG). The SFG is an excellent replacement for tanker ships and submarine pipelines transporting liquefied CO2. The main aim of the SFG is to ship CO2 from an offshore facility to an underwater well where the gas can be injected; as an advantage, the SFG vehicle may be used to transport all kinds of cargo. The SFG travels below the sea surface, making the vessel weather-independent. The research is divided into two steps. Firstly, the technical feasibility analysis is performed by designing a baseline design with a length of 56.5 m, a beam of 5.5 m, and a cargo volume of 1194 m3. The SFG is developed using DNVGL-RU-NAVAL-Pt4Ch1, which was initially created for military submarine designs. Two additional half-scaled 469 m3 and double-scaled 2430 m3 models are created when the baseline design fulfils the technical requirements. Secondly, the economic analysis is carried out using the freely accessible MUNIN D9.3 and ZEP reports. The economic feasibility analysis is illustrated through a case study with a CO2 transport capacity range of 0.5 to 2.5 mtpa (million tons per annum) and a transport length range of 180 km to 1500 km. The prices of CO2 per ton for the SFG, crew and autonomous tankers, and offshore pipelines are comprehensively compared. According to the results, SFGs with capacities of 469 m3, 1194 m3, and 2430 m3 are technically possible to manufacture. Moreover, the SFGs are competitive with a smaller CO2 capacity of 0.5 mtpa at distances of 180 and 500 km and a capacity of 1 mtpa at a distance of 180 km.publishedVersio
Universality and Limitations of Prompt Tuning
Despite the demonstrated empirical efficacy of prompt tuning to adapt a
pretrained language model for a new task, the theoretical underpinnings of the
difference between "tuning parameters before the input" against "the tuning of
model weights" are limited. We thus take one of the first steps to understand
the role of soft-prompt tuning for transformer-based architectures. By
considering a general purpose architecture, we analyze prompt tuning from the
lens of both: universal approximation and limitations with finite-depth
fixed-weight pretrained transformers for continuous-valued functions. Our
universality result guarantees the existence of a strong transformer with a
prompt to approximate any sequence-to-sequence function in the set of Lipschitz
functions. The limitations of prompt tuning for limited-depth transformers are
first proved by constructing a set of datasets, that cannot be memorized by a
prompt of any length for a given single encoder layer. We also provide a lower
bound on the required number of tunable prompt parameters and compare the
result with the number of parameters required for a low-rank update (based on
LoRA) for a single-layer setting. We finally extend our analysis to multi-layer
settings by providing sufficient conditions under which the transformer can at
best learn datasets from invertible functions only. Our theoretical claims are
also corroborated by empirical results
Community Perspectives of Tourism Benefits - The Link to Conservation Attitudes and Livelihoods
Tourism has often been seen as an approach to link conservation and development. Its
potential rests in providing economic benefits while maintaining environmental integrity
(Stem et al, 2003). In protected areas, nature-based tourism offers the economic
justification for establishing protected area (Brandon, K. 1996) through park entry fees
and other tourism related activities. The economic benefits provided through tourism
development also help generate conservation support from local communities and can be
used to improve conservation efforts.
Numerous studies have shown that the incentives for local people to support
conservation are recipient of tangible economic benefits. However, despite this popular
notion, it is remains to be seen what role tourism benefits play in conservation attitudes,
and its interactions with other social economic factors that are in play. In face of a lack of
information on the role tourism plays in livelihoods improvement and conservation
attitudes, this study attempts to explore how tourism in protected area help link
biodiversity conservation with community development. It evaluates the positive and
negative effects tourism has had on local residents, and explores whether those factors
lead to positive conservation attitudes. The study took place in buffer zone community in
Chitwan National Park, Nepal’s first protected area and one of the most popular tourist
destinations in the nation. By providing the context of this study, the paper introduces the
study method and analyzes the results it revealed. Such information will provide policy
implications for governments and future tourism operators to prioritize factors that
generate supportive attitudes towards tourism and greater support for conservation. The
study also helps to develop a better understanding of community needs for designing
future development projects that meet community expectations.Master of ScienceSchool for Environment and SustainabilityUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/137969/1/Wang_Yihan_Practicum_2017.pd
Game-Theoretic Unlearnable Example Generator
Unlearnable example attacks are data poisoning attacks aiming to degrade the
clean test accuracy of deep learning by adding imperceptible perturbations to
the training samples, which can be formulated as a bi-level optimization
problem. However, directly solving this optimization problem is intractable for
deep neural networks. In this paper, we investigate unlearnable example attacks
from a game-theoretic perspective, by formulating the attack as a nonzero sum
Stackelberg game. First, the existence of game equilibria is proved under the
normal setting and the adversarial training setting. It is shown that the game
equilibrium gives the most powerful poison attack in that the victim has the
lowest test accuracy among all networks within the same hypothesis space, when
certain loss functions are used. Second, we propose a novel attack method,
called the Game Unlearnable Example (GUE), which has three main gradients. (1)
The poisons are obtained by directly solving the equilibrium of the Stackelberg
game with a first-order algorithm. (2) We employ an autoencoder-like generative
network model as the poison attacker. (3) A novel payoff function is introduced
to evaluate the performance of the poison. Comprehensive experiments
demonstrate that GUE can effectively poison the model in various scenarios.
Furthermore, the GUE still works by using a relatively small percentage of the
training data to train the generator, and the poison generator can generalize
to unseen data well. Our implementation code can be found at
https://github.com/hong-xian/gue
Data-Dependent Stability Analysis of Adversarial Training
Stability analysis is an essential aspect of studying the generalization
ability of deep learning, as it involves deriving generalization bounds for
stochastic gradient descent-based training algorithms. Adversarial training is
the most widely used defense against adversarial example attacks. However,
previous generalization bounds for adversarial training have not included
information regarding the data distribution. In this paper, we fill this gap by
providing generalization bounds for stochastic gradient descent-based
adversarial training that incorporate data distribution information. We utilize
the concepts of on-average stability and high-order approximate Lipschitz
conditions to examine how changes in data distribution and adversarial budget
can affect robust generalization gaps. Our derived generalization bounds for
both convex and non-convex losses are at least as good as the uniform
stability-based counterparts which do not include data distribution
information. Furthermore, our findings demonstrate how distribution shifts from
data poisoning attacks can impact robust generalization
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