144 research outputs found
Enhanced Low-resolution LiDAR-Camera Calibration Via Depth Interpolation and Supervised Contrastive Learning
Motivated by the increasing application of low-resolution LiDAR recently, we
target the problem of low-resolution LiDAR-camera calibration in this work. The
main challenges are two-fold: sparsity and noise in point clouds. To address
the problem, we propose to apply depth interpolation to increase the point
density and supervised contrastive learning to learn noise-resistant features.
The experiments on RELLIS-3D demonstrate that our approach achieves an average
mean absolute rotation/translation errors of 0.15cm/0.33\textdegree on
32-channel LiDAR point cloud data, which significantly outperforms all
reference methods
Neuromorphic Incremental on-chip Learning with Hebbian Weight Consolidation
As next-generation implantable brain-machine interfaces become pervasive on
edge device, incrementally learning new tasks in bio-plasticity ways is
urgently demanded for Neuromorphic chips. Due to the inherent characteristics
of its structure, spiking neural networks are naturally well-suited for
BMI-chips. Here we propose Hebbian Weight Consolidation, as well as an on-chip
learning framework. HWC selectively masks synapse modifications for previous
tasks, retaining them to store new knowledge from subsequent tasks while
preserving the old knowledge. Leveraging the bio-plasticity of dendritic
spines, the intrinsic self-organizing nature of Hebbian Weight Consolidation
aligns naturally with the incremental learning paradigm, facilitating robust
learning outcomes. By reading out spikes layer by layer and performing
back-propagation on the external micro-controller unit, MLoC can efficiently
accomplish on-chip learning. Experiments show that our HWC algorithm up to
23.19% outperforms lower bound that without incremental learning algorithm,
particularly in more challenging monkey behavior decoding scenarios. Taking
into account on-chip computing on Synsense Speck 2e chip, our proposed
algorithm exhibits an improvement of 11.06%. This study demonstrates the
feasibility of employing incremental learning for high-performance neural
signal decoding in next-generation brain-machine interfaces.Comment: 12 pages, 6 figure
Transfer Attacks and Defenses for Large Language Models on Coding Tasks
Modern large language models (LLMs), such as ChatGPT, have demonstrated
impressive capabilities for coding tasks including writing and reasoning about
code. They improve upon previous neural network models of code, such as
code2seq or seq2seq, that already demonstrated competitive results when
performing tasks such as code summarization and identifying code
vulnerabilities. However, these previous code models were shown vulnerable to
adversarial examples, i.e. small syntactic perturbations that do not change the
program's semantics, such as the inclusion of "dead code" through false
conditions or the addition of inconsequential print statements, designed to
"fool" the models. LLMs can also be vulnerable to the same adversarial
perturbations but a detailed study on this concern has been lacking so far. In
this paper we aim to investigate the effect of adversarial perturbations on
coding tasks with LLMs. In particular, we study the transferability of
adversarial examples, generated through white-box attacks on smaller code
models, to LLMs. Furthermore, to make the LLMs more robust against such
adversaries without incurring the cost of retraining, we propose prompt-based
defenses that involve modifying the prompt to include additional information
such as examples of adversarially perturbed code and explicit instructions for
reversing adversarial perturbations. Our experiments show that adversarial
examples obtained with a smaller code model are indeed transferable, weakening
the LLMs' performance. The proposed defenses show promise in improving the
model's resilience, paving the way to more robust defensive solutions for LLMs
in code-related applications
Automatic Error Detection in Integrated Circuits Image Segmentation: A Data-driven Approach
Due to the complicated nanoscale structures of current integrated
circuits(IC) builds and low error tolerance of IC image segmentation tasks,
most existing automated IC image segmentation approaches require human experts
for visual inspection to ensure correctness, which is one of the major
bottlenecks in large-scale industrial applications. In this paper, we present
the first data-driven automatic error detection approach targeting two types of
IC segmentation errors: wire errors and via errors. On an IC image dataset
collected from real industry, we demonstrate that, by adapting existing
CNN-based approaches of image classification and image translation with
additional pre-processing and post-processing techniques, we are able to
achieve recall/precision of 0.92/0.93 in wire error detection and 0.96/0.90 in
via error detection, respectively
propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans
**Background:** Accurate 3D CT scan segmentation of gastric tumors is pivotal
for diagnosis and treatment. The challenges lie in the irregular shapes,
blurred boundaries of tumors, and the inefficiency of existing methods.
**Purpose:** We conducted a study to introduce a model, utilizing
human-guided knowledge and unique modules, to address the challenges of 3D
tumor segmentation.
**Methods:** We developed the PropNet framework, propagating radiologists'
knowledge from 2D annotations to the entire 3D space. This model consists of a
proposing stage for coarse segmentation and a refining stage for improved
segmentation, using two-way branches for enhanced performance and an up-down
strategy for efficiency.
**Results:** With 98 patient scans for training and 30 for validation, our
method achieves a significant agreement with manual annotation (Dice of 0.803)
and improves efficiency. The performance is comparable in different scenarios
and with various radiologists' annotations (Dice between 0.785 and 0.803).
Moreover, the model shows improved prognostic prediction performance (C-index
of 0.620 vs. 0.576) on an independent validation set of 42 patients with
advanced gastric cancer.
**Conclusions:** Our model generates accurate tumor segmentation efficiently
and stably, improving prognostic performance and reducing high-throughput image
reading workload. This model can accelerate the quantitative analysis of
gastric tumors and enhance downstream task performance
Polydopamine-Decorated Microcomposites Promote Functional Recovery of an Injured Spinal Cord by Inhibiting Neuroinflammation
Neuroinflammation following spinal cord injury usually aggravates spinal cord damage. Many inflammatory cytokines are key players in neuroinflammation. Owing largely to the multiplicity of cytokine targets and the complexity of cytokine interactions, it is insufficient to suppress spinal cord damage progression by regulating only one or a few cytokines. Herein, we propose a two-pronged strategy to simultaneously capture the released cytokines and inhibit the synthesis of new ones in a broad-spectrum manner. To achieve this strategy, we designed a core/shell-structured microcomposite, which was composed of a methylprednisolone-incorporated polymer inner core and a biocompatible polydopamine outer shell. Thanks to the inherent adhesive nature of polydopamine, the obtained microcomposite (MP-PLGA@PDA) efficiently neutralized the excessive cytokines in a broad-spectrum manner within 1 day after spinal cord injury. Meanwhile, the controlled release of immunosuppressive methylprednisolone reduced the secretion of new inflammatory cytokines. Benefiting from its efficient and broad-spectrum capability in reducing the level of cytokines, this core/shell-structured microcomposite suppressed the recruitment of macrophages and protected the injured spinal cord, leading to an improved recovery of motor function. Overall, the designed microcomposite successfully achieved the two-pronged strategy in cytokine neutralization, providing an alternative approach to inhibit neuroinflammation in the injured spinal cord.Peer reviewe
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