393 research outputs found
ADNet: Lane Shape Prediction via Anchor Decomposition
In this paper, we revisit the limitations of anchor-based lane detection
methods, which have predominantly focused on fixed anchors that stem from the
edges of the image, disregarding their versatility and quality. To overcome the
inflexibility of anchors, we decompose them into learning the heat map of
starting points and their associated directions. This decomposition removes the
limitations on the starting point of anchors, making our algorithm adaptable to
different lane types in various datasets. To enhance the quality of anchors, we
introduce the Large Kernel Attention (LKA) for Feature Pyramid Network (FPN).
This significantly increases the receptive field, which is crucial in capturing
the sufficient context as lane lines typically run throughout the entire image.
We have named our proposed system the Anchor Decomposition Network (ADNet).
Additionally, we propose the General Lane IoU (GLIoU) loss, which significantly
improves the performance of ADNet in complex scenarios. Experimental results on
three widely used lane detection benchmarks, VIL-100, CULane, and TuSimple,
demonstrate that our approach outperforms the state-of-the-art methods on
VIL-100 and exhibits competitive accuracy on CULane and TuSimple. Code and
models will be released on https://github.com/ Sephirex-X/ADNet.Comment: ICCV2023 accepte
Driven Majorana Modes: A Route to Synthetic Superconductivity
We propose a protocol to realize synthetic superconductors in
one-dimensional topological systems that host Majorana fermions. By
periodically driving a localized Majorana mode across the system, our protocol
realizes a topological pumping of Majorana fermions, analogous to the adiabatic
Thouless pumping of electrical charges. Importantly, similar to the realization
of a Chern insulator through Thouless pumping, we show that pumping of Majorana
zero modes could lead to a superconductor in the two dimensions of
space and synthetic time. The Floquet theory is employed to map the driven
one-dimensional system to a two-dimensional synthetic system by considering
frequency as a new dimension. We demonstrate such Floquet
superconductors using the Kitaev -wave superconductor chain, a prototypical
1D topological system, as well as its more realistic realization in the 1D
Kondo lattice model as examples. We further show the appearance of a new
Majorana mode at the Floquet zone boundary in an intermediate drive frequency
region. Our work suggests a driven magnetic spiral coupled to a superconductor
as a promising platform for the realization of novel topological
superconductors
Neural Machine Translation with Dynamic Graph Convolutional Decoder
Existing wisdom demonstrates the significance of syntactic knowledge for the
improvement of neural machine translation models. However, most previous works
merely focus on leveraging the source syntax in the well-known encoder-decoder
framework. In sharp contrast, this paper proposes an end-to-end translation
architecture from the (graph \& sequence) structural inputs to the (graph \&
sequence) outputs, where the target translation and its corresponding syntactic
graph are jointly modeled and generated. We propose a customized Dynamic
Spatial-Temporal Graph Convolutional Decoder (Dyn-STGCD), which is designed for
consuming source feature representations and their syntactic graph, and
auto-regressively generating the target syntactic graph and tokens
simultaneously. We conduct extensive experiments on five widely acknowledged
translation benchmarks, verifying that our proposal achieves consistent
improvements over baselines and other syntax-aware variants
Development of a hardware-In-the-Loop (HIL) testbed for cyber-physical security in smart buildings
As smart buildings move towards open communication technologies, providing
access to the Building Automation System (BAS) through the intranet, or even
remotely through the Internet, has become a common practice. However, BAS was
historically developed as a closed environment and designed with limited
cyber-security considerations. Thus, smart buildings are vulnerable to
cyber-attacks with the increased accessibility. This study introduces the
development and capability of a Hardware-in-the-Loop (HIL) testbed for testing
and evaluating the cyber-physical security of typical BASs in smart buildings.
The testbed consists of three subsystems: (1) a real-time HIL emulator
simulating the behavior of a virtual building as well as the Heating,
Ventilation, and Air Conditioning (HVAC) equipment via a dynamic simulation in
Modelica; (2) a set of real HVAC controllers monitoring the virtual building
operation and providing local control signals to control HVAC equipment in the
HIL emulator; and (3) a BAS server along with a web-based service for users to
fully access the schedule, setpoints, trends, alarms, and other control
functions of the HVAC controllers remotely through the BACnet network. The
server generates rule-based setpoints to local HVAC controllers. Based on these
three subsystems, the HIL testbed supports attack/fault-free and
attack/fault-injection experiments at various levels of the building system.
The resulting test data can be used to inform the building community and
support the cyber-physical security technology transfer to the building
industry.Comment: Presented at the 2023 ASHRAE Winter Conferenc
Robust Perception through Equivariance
Deep networks for computer vision are not reliable when they encounter
adversarial examples. In this paper, we introduce a framework that uses the
dense intrinsic constraints in natural images to robustify inference. By
introducing constraints at inference time, we can shift the burden of
robustness from training to the inference algorithm, thereby allowing the model
to adjust dynamically to each individual image's unique and potentially novel
characteristics at inference time. Among different constraints, we find that
equivariance-based constraints are most effective, because they allow dense
constraints in the feature space without overly constraining the representation
at a fine-grained level. Our theoretical results validate the importance of
having such dense constraints at inference time. Our empirical experiments show
that restoring feature equivariance at inference time defends against
worst-case adversarial perturbations. The method obtains improved adversarial
robustness on four datasets (ImageNet, Cityscapes, PASCAL VOC, and MS-COCO) on
image recognition, semantic segmentation, and instance segmentation tasks.
Project page is available at equi4robust.cs.columbia.edu
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