317 research outputs found
Boosting the Adversarial Transferability of Surrogate Models with Dark Knowledge
Deep neural networks (DNNs) are vulnerable to adversarial examples. And, the
adversarial examples have transferability, which means that an adversarial
example for a DNN model can fool another model with a non-trivial probability.
This gave birth to the transfer-based attack where the adversarial examples
generated by a surrogate model are used to conduct black-box attacks. There are
some work on generating the adversarial examples from a given surrogate model
with better transferability. However, training a special surrogate model to
generate adversarial examples with better transferability is relatively
under-explored. This paper proposes a method for training a surrogate model
with dark knowledge to boost the transferability of the adversarial examples
generated by the surrogate model. This trained surrogate model is named dark
surrogate model (DSM). The proposed method for training a DSM consists of two
key components: a teacher model extracting dark knowledge, and the mixing
augmentation skill enhancing dark knowledge of training data. We conducted
extensive experiments to show that the proposed method can substantially
improve the adversarial transferability of surrogate models across different
architectures of surrogate models and optimizers for generating adversarial
examples, and it can be applied to other scenarios of transfer-based attack
that contain dark knowledge, like face verification. Our code is publicly
available at \url{https://github.com/ydc123/Dark_Surrogate_Model}.Comment: Accepted at 2023 International Conference on Tools with Artificial
Intelligence (ICTAI
Neural Point Process for Learning Spatiotemporal Event Dynamics
Learning the dynamics of spatiotemporal events is a fundamental problem.
Neural point processes enhance the expressivity of point process models with
deep neural networks. However, most existing methods only consider temporal
dynamics without spatial modeling. We propose Deep Spatiotemporal Point Process
(\ours{}), a deep dynamics model that integrates spatiotemporal point
processes. Our method is flexible, efficient, and can accurately forecast
irregularly sampled events over space and time. The key construction of our
approach is the nonparametric space-time intensity function, governed by a
latent process. The intensity function enjoys closed form integration for the
density. The latent process captures the uncertainty of the event sequence. We
use amortized variational inference to infer the latent process with deep
networks. Using synthetic datasets, we validate our model can accurately learn
the true intensity function. On real-world benchmark datasets, our model
demonstrates superior performance over state-of-the-art baselines. Our code and
data can be found at the https://github.com/Rose-STL-Lab/DeepSTPP
EHRTutor: Enhancing Patient Understanding of Discharge Instructions
Large language models have shown success as a tutor in education in various
fields. Educating patients about their clinical visits plays a pivotal role in
patients' adherence to their treatment plans post-discharge. This paper
presents EHRTutor, an innovative multi-component framework leveraging the Large
Language Model (LLM) for patient education through conversational
question-answering. EHRTutor first formulates questions pertaining to the
electronic health record discharge instructions. It then educates the patient
through conversation by administering each question as a test. Finally, it
generates a summary at the end of the conversation. Evaluation results using
LLMs and domain experts have shown a clear preference for EHRTutor over the
baseline. Moreover, EHRTutor also offers a framework for generating synthetic
patient education dialogues that can be used for future in-house system
training.Comment: To appear in NeurIPS'23 Workshop on Generative AI for Education
(GAIED
FISEdit: Accelerating Text-to-image Editing via Cache-enabled Sparse Diffusion Inference
Due to the recent success of diffusion models, text-to-image generation is
becoming increasingly popular and achieves a wide range of applications. Among
them, text-to-image editing, or continuous text-to-image generation, attracts
lots of attention and can potentially improve the quality of generated images.
It's common to see that users may want to slightly edit the generated image by
making minor modifications to their input textual descriptions for several
rounds of diffusion inference. However, such an image editing process suffers
from the low inference efficiency of many existing diffusion models even using
GPU accelerators. To solve this problem, we introduce Fast Image Semantically
Edit (FISEdit), a cached-enabled sparse diffusion model inference engine for
efficient text-to-image editing. The key intuition behind our approach is to
utilize the semantic mapping between the minor modifications on the input text
and the affected regions on the output image. For each text editing step,
FISEdit can automatically identify the affected image regions and utilize the
cached unchanged regions' feature map to accelerate the inference process.
Extensive empirical results show that FISEdit can be and
faster than existing methods on NVIDIA TITAN RTX and A100 GPUs
respectively, and even generates more satisfactory images.Comment: 12 pages, 7 figure
Crocs: Cross-Technology Clock Synchronization for WiFi and ZigBee
Clock synchronization is a key function in embedded wireless systems and
networks. This issue is equally important and more challenging in IoT systems
nowadays, which often include heterogeneous wireless devices that follow
different wireless standards. Conventional solutions to this problem employ
gateway-based indirect synchronization, which suffers low accuracy. This paper
for the first time studies the problem of cross-technology clock
synchronization. Our proposal called Crocs synchronizes WiFi and ZigBee devices
by direct cross-technology communication. Crocs decouples the synchronization
signal from the transmission of a timestamp. By incorporating a barker-code
based beacon for time alignment and cross-technology transmission of
timestamps, Crocs achieves robust and accurate synchronization among WiFi and
ZigBee devices, with the synchronization error lower than 1 millisecond. We
further make attempts to implement different cross-technology communication
methods in Crocs and provide insight findings with regard to the achievable
accuracy and expected overhead
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