294 research outputs found
RaPlace: Place Recognition for Imaging Radar using Radon Transform and Mutable Threshold
Due to the robustness in sensing, radar has been highlighted, overcoming
harsh weather conditions such as fog and heavy snow. In this paper, we present
a novel radar-only place recognition that measures the similarity score by
utilizing Radon-transformed sinogram images and cross-correlation in frequency
domain. Doing so achieves rigid transform invariance during place recognition,
while ignoring the effects of radar multipath and ring noises. In addition, we
compute the radar similarity distance using mutable threshold to mitigate
variability of the similarity score, and reduce the time complexity of
processing a copious radar data with hierarchical retrieval. We demonstrate the
matching performance for both intra-session loop-closure detection and global
place recognition using a publicly available imaging radar datasets. We verify
reliable performance compared to existing stable radar place recognition
method. Furthermore, codes for the proposed imaging radar place recognition is
released for community
Differentially Private Multivariate Statistics with an Application to Contingency Table Analysis
Differential privacy (DP) has become a rigorous central concept in privacy
protection for the past decade. Among various notions of DP, -DP is an
easily interpretable and informative concept that tightly captures privacy
level by comparing trade-off functions obtained from the hypothetical test of
how well the mechanism recognizes individual information in the dataset. We
adopt the Gaussian differential privacy (GDP), a canonical parametric family of
-DP. The Gaussian mechanism is a natural and fundamental mechanism that
tightly achieves GDP. However, the ordinary multivariate Gaussian mechanism is
not optimal with respect to statistical utility. To improve the utility, we
develop the rank-deficient and James-Stein Gaussian mechanisms for releasing
private multivariate statistics based on the geometry of multivariate Gaussian
distribution. We show that our proposals satisfy GDP and dominate the ordinary
Gaussian mechanism with respect to -cost. We also show that the Laplace
mechanism, a prime mechanism in -DP framework, is sub-optimal than
Gaussian-type mechanisms under the framework of GDP. For a fair comparison, we
calibrate the Laplace mechanism to the global sensitivity of the statistic with
the exact approach to the trade-off function. We also develop the optimal
parameter for the Laplace mechanism when applied to contingency tables. Indeed,
we show that the Gaussian-type mechanisms dominate the Laplace mechanism in
contingency table analysis. In addition, we apply our findings to propose
differentially private -tests on contingency tables. Numerical results
demonstrate that differentially private parametric bootstrap tests control the
type I error rates and show higher power than other natural competitors
TRansPose: Large-Scale Multispectral Dataset for Transparent Object
Transparent objects are encountered frequently in our daily lives, yet
recognizing them poses challenges for conventional vision sensors due to their
unique material properties, not being well perceived from RGB or depth cameras.
Overcoming this limitation, thermal infrared cameras have emerged as a
solution, offering improved visibility and shape information for transparent
objects. In this paper, we present TRansPose, the first large-scale
multispectral dataset that combines stereo RGB-D, thermal infrared (TIR)
images, and object poses to promote transparent object research. The dataset
includes 99 transparent objects, encompassing 43 household items, 27 recyclable
trashes, 29 chemical laboratory equivalents, and 12 non-transparent objects. It
comprises a vast collection of 333,819 images and 4,000,056 annotations,
providing instance-level segmentation masks, ground-truth poses, and completed
depth information. The data was acquired using a FLIR A65 thermal infrared
(TIR) camera, two Intel RealSense L515 RGB-D cameras, and a Franka Emika Panda
robot manipulator. Spanning 87 sequences, TRansPose covers various challenging
real-life scenarios, including objects filled with water, diverse lighting
conditions, heavy clutter, non-transparent or translucent containers, objects
in plastic bags, and multi-stacked objects. TRansPose dataset can be accessed
from the following link: https://sites.google.com/view/transpose-datasetComment: Under revie
HeLiPR: Heterogeneous LiDAR Dataset for inter-LiDAR Place Recognition under Spatial and Temporal Variations
Place recognition is crucial for robotic localization and loop closure in
simultaneous localization and mapping (SLAM). Recently, LiDARs have gained
popularity due to their robust sensing capability and measurement consistency,
even in the illumination-variant environment, offering an advantage over
traditional imaging sensors. Spinning LiDARs are widely accepted among many
types, while non-repetitive scanning patterns have recently been utilized in
robotic applications. Beyond the range measurements, some LiDARs offer
additional measurements, such as reflectivity, Near Infrared (NIR), and
velocity (e.g., FMCW LiDARs). Despite these advancements, a noticeable dearth
of datasets comprehensively reflects the broad spectrum of LiDAR configurations
optimized for place recognition. To tackle this issue, our paper proposes the
HeLiPR dataset, curated especially for place recognition with heterogeneous
LiDAR systems, embodying spatial-temporal variations. To the best of our
knowledge, the HeLiPR dataset is the first heterogeneous LiDAR dataset designed
to support inter-LiDAR place recognition with both non-repetitive and spinning
LiDARs, accommodating different field of view (FOV) and varying numbers of
rays. Encompassing the distinct LiDAR configurations, it captures varied
environments ranging from urban cityscapes to high-dynamic freeways over a
month, designed to enhance the adaptability and robustness of place recognition
across diverse scenarios. Notably, the HeLiPR dataset also includes
trajectories that parallel sequences from MulRan, underscoring its utility for
research in heterogeneous LiDAR place recognition and long-term studies. The
dataset is accessible at https: //sites.google.com/view/heliprdataset.Comment: 9 pages, 9 figures, 5 table
Topological Directional Coupler
Interferometers and beam splitters are fundamental building blocks for
photonic neuromorphic and quantum computing machinery. In waveguide-based
photonic integrated circuits, beam-splitting is achieved with directional
couplers that rely on transition regions where the waveguides are adiabatically
bent to suppress back-reflection. We present a novel, compact approach to
introducing guided mode coupling. By leveraging multimodal domain walls between
microwave topological photonic crystals, we use the photonic-spin-conservation
to suppress back-reflection while relaxing the topological protection of the
valley degree of freedom to implement tunable beam splitting. Rapid
advancements in chip-scale topological photonics suggest that the proposed
simultaneous utilization of multiple topological degrees of freedom could
benefit the development of novel photonic computing platforms
Target-Agnostic Gender-Aware Contrastive Learning for Mitigating Bias in Multilingual Machine Translation
Gender bias is a significant issue in machine translation, leading to ongoing
research efforts in developing bias mitigation techniques. However, most works
focus on debiasing bilingual models without much consideration for multilingual
systems. In this paper, we specifically target the gender bias issue of
multilingual machine translation models for unambiguous cases where there is a
single correct translation, and propose a bias mitigation method based on a
novel approach. Specifically, we propose Gender-Aware Contrastive Learning,
GACL, which encodes contextual gender information into the representations of
non-explicit gender words. Our method is target language-agnostic and is
applicable to pre-trained multilingual machine translation models via
fine-tuning. Through multilingual evaluation, we show that our approach
improves gender accuracy by a wide margin without hampering translation
performance. We also observe that incorporated gender information transfers and
benefits other target languages regarding gender accuracy. Finally, we
demonstrate that our method is applicable and beneficial to models of various
sizes.Comment: Accepted to EMNLP 2023 Main Conferenc
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