6 research outputs found
Spectral Representation Learning for Conditional Moment Models
Many problems in causal inference and economics can be formulated in the
framework of conditional moment models, which characterize the target function
through a collection of conditional moment restrictions. For nonparametric
conditional moment models, efficient estimation often relies on preimposed
conditions on various measures of ill-posedness of the hypothesis space, which
are hard to validate when flexible models are used. In this work, we address
this issue by proposing a procedure that automatically learns representations
with controlled measures of ill-posedness. Our method approximates a linear
representation defined by the spectral decomposition of a conditional
expectation operator, which can be used for kernelized estimators and is known
to facilitate minimax optimal estimation in certain settings. We show this
representation can be efficiently estimated from data, and establish L2
consistency for the resulting estimator. We evaluate the proposed method on
proximal causal inference tasks, exhibiting promising performance on
high-dimensional, semi-synthetic data
LATR: 3D Lane Detection from Monocular Images with Transformer
3D lane detection from monocular images is a fundamental yet challenging task
in autonomous driving. Recent advances primarily rely on structural 3D
surrogates (e.g., bird's eye view) built from front-view image features and
camera parameters. However, the depth ambiguity in monocular images inevitably
causes misalignment between the constructed surrogate feature map and the
original image, posing a great challenge for accurate lane detection. To
address the above issue, we present a novel LATR model, an end-to-end 3D lane
detector that uses 3D-aware front-view features without transformed view
representation. Specifically, LATR detects 3D lanes via cross-attention based
on query and key-value pairs, constructed using our lane-aware query generator
and dynamic 3D ground positional embedding. On the one hand, each query is
generated based on 2D lane-aware features and adopts a hybrid embedding to
enhance lane information. On the other hand, 3D space information is injected
as positional embedding from an iteratively-updated 3D ground plane. LATR
outperforms previous state-of-the-art methods on both synthetic Apollo,
realistic OpenLane and ONCE-3DLanes by large margins (e.g., 11.4 gain in terms
of F1 score on OpenLane). Code will be released at
https://github.com/JMoonr/LATR .Comment: Accepted by ICCV2023 (Oral
M^2-3DLaneNet: Multi-Modal 3D Lane Detection
Estimating accurate lane lines in 3D space remains challenging due to their
sparse and slim nature. In this work, we propose the M^2-3DLaneNet, a
Multi-Modal framework for effective 3D lane detection. Aiming at integrating
complementary information from multi-sensors, M^2-3DLaneNet first extracts
multi-modal features with modal-specific backbones, then fuses them in a
unified Bird's-Eye View (BEV) space. Specifically, our method consists of two
core components. 1) To achieve accurate 2D-3D mapping, we propose the top-down
BEV generation. Within it, a Line-Restricted Deform-Attention (LRDA) module is
utilized to effectively enhance image features in a top-down manner, fully
capturing the slenderness features of lanes. After that, it casts the 2D
pyramidal features into 3D space using depth-aware lifting and generates BEV
features through pillarization. 2) We further propose the bottom-up BEV fusion,
which aggregates multi-modal features through multi-scale cascaded attention,
integrating complementary information from camera and LiDAR sensors. Sufficient
experiments demonstrate the effectiveness of M^2-3DLaneNet, which outperforms
previous state-of-the-art methods by a large margin, i.e., 12.1% F1-score
improvement on OpenLane dataset
Dual-Drug Loaded Separable Microneedles for Efficient Rheumatoid Arthritis Therapy
Although the inhibitors of the interleukin-6 receptor (IL-6R) and tumor necrosis factor-α (TNF-α) have achieved a certain success in the clinical treatment of rheumatoid arthritis (RA), great effort should be made to overcome side effects and to improve patient compliance. The present research aimed to address these problems by the co-delivery of tocilizumab (TCZ)—an inhibitor of IL-6R—and an aptamer Apt1-67, which specifically inhibits TNF receptor 1 via separable microneedles (MN). MN were featured with a sustained release of TCZ from needle tips and a rapid release of Apt1-67 from needle bodies by using methacrylate groups grafted hyaluronic acid as the fillings of needle tips and polyvinyl alcohol/polyvinyl pyrrolidone as the fillings of needle bodies. Our results demonstrated that TCZ and Apt1-67 were distributed in MN as expected, and they could be released to the surroundings in the skin. In vivo studies revealed that combined medication via MN (TCZ/Apt1-67@MN) was superior to MN loaded with a single drug. Compared with subcutaneous injection, TCZ/Apt1-67@MN was of great advantage in inhibiting bone erosion and alleviating symptoms of CIA mice. This study not only provides a novel approach for combined medication with different release properties but also supplies a strategy for improving drug efficacy