370 research outputs found
Zero-norm states and stringy symmetries
We identify spacetime symmetry charges of 26D open bosonic string theory from
an infinite number of zero-norm states (ZNS) with arbitrary high spin in the
old covariant first quantized string spectrum. We give various evidences to
support this identification. These include massive sigma-model calculation,
Witten string field theory calculation, 2D string theory calculation and, most
importantly, three methods of high-energy stringy scattering amplitude
calculations. The last calculations explicitly prove Gross's conjectures in
1988 on high energy symmetry of string theory.Comment: 6 pages. Talks presented by Jen-Chi Lee at XXVIII Spanish Relativity
Meeting (ERE2005),"A Century of Relativity Physics",Oviedo,Spain,6-10 Sep
2005 and "4th Meeting on constrained Dynamics and Quantum Gravity",Cala
Gonone,Sardinia,Italy,12-16 Sep 2005. To appear in the Journal of Physics:
Conference Serie
Transport or Store? Synthesizing Flow-based Microfluidic Biochips using Distributed Channel Storage
Flow-based microfluidic biochips have attracted much atten- tion in the EDA
community due to their miniaturized size and execution efficiency. Previous
research, however, still follows the traditional computing model with a
dedicated storage unit, which actually becomes a bottleneck of the performance
of bio- chips. In this paper, we propose the first architectural synthe- sis
framework considering distributed storage constructed tem- porarily from
transportation channels to cache fluid samples. Since distributed storage can
be accessed more efficiently than a dedicated storage unit and channels can
switch between the roles of transportation and storage easily, biochips with
this dis- tributed computing architecture can achieve a higher execution
efficiency even with fewer resources. Experimental results con- firm that the
execution efficiency of a bioassay can be improved by up to 28% while the
number of valves in the biochip can be reduced effectively.Comment: ACM/IEEE Design Automation Conference (DAC), June 201
RADAR: Robust AI-Text Detection via Adversarial Learning
Recent advances in large language models (LLMs) and the intensifying
popularity of ChatGPT-like applications have blurred the boundary of
high-quality text generation between humans and machines. However, in addition
to the anticipated revolutionary changes to our technology and society, the
difficulty of distinguishing LLM-generated texts (AI-text) from human-generated
texts poses new challenges of misuse and fairness, such as fake content
generation, plagiarism, and false accusation of innocent writers. While
existing works show that current AI-text detectors are not robust to LLM-based
paraphrasing, this paper aims to bridge this gap by proposing a new framework
called RADAR, which jointly trains a Robust AI-text Detector via Adversarial
leaRning. RADAR is based on adversarial training of a paraphraser and a
detector. The paraphraser's goal is to generate realistic contents to evade
AI-text detection. RADAR uses the feedback from the detector to update the
paraphraser, and vice versa. Evaluated with 8 different LLMs (Pythia, Dolly
2.0, Palmyra, Camel, GPT-J, Dolly 1.0, LLaMA, and Vicuna) across 4 datasets,
experimental results show that RADAR significantly outperforms existing AI-text
detection methods, especially when paraphrasing is in place. We also identify
the strong transferability of RADAR from instruction-tuned LLMs to other LLMs,
and evaluate the improved capability of RADAR via GPT-3.5.Comment: Preprint. Project page and demos: https://radar.vizhub.a
A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor Segmentation
Magnetic resonance imaging (MRI) is a commonly used technique for brain tumor
segmentation, which is critical for evaluating patients and planning treatment.
To make the labeling process less laborious and dependent on expertise,
weakly-supervised semantic segmentation (WSSS) methods using class activation
mapping (CAM) have been proposed. However, current CAM-based WSSS methods
generate the object localization map using internal neural network information,
such as gradient or trainable parameters, which can lead to suboptimal
solutions. To address these issues, we propose the confidence-induced CAM
(Cfd-CAM), which calculates the weight of each feature map by using the
confidence of the target class. Our experiments on two brain tumor datasets
show that Cfd-CAM outperforms existing state-of-the-art methods under the same
level of supervision. Overall, our proposed Cfd-CAM approach improves the
accuracy of brain tumor segmentation and may provide valuable insights for
developing better WSSS methods for other medical imaging tasks
Testing Microfluidic Fully Programmable Valve Arrays (FPVAs)
Fully Programmable Valve Array (FPVA) has emerged as a new architecture for
the next-generation flow-based microfluidic biochips. This 2D-array consists of
regularly-arranged valves, which can be dynamically configured by users to
realize microfluidic devices of different shapes and sizes as well as
interconnections. Additionally, the regularity of the underlying structure
renders FPVAs easier to integrate on a tiny chip. However, these arrays may
suffer from various manufacturing defects such as blockage and leakage in
control and flow channels. Unfortunately, no efficient method is yet known for
testing such a general-purpose architecture. In this paper, we present a novel
formulation using the concept of flow paths and cut-sets, and describe an
ILP-based hierarchical strategy for generating compact test sets that can
detect multiple faults in FPVAs. Simulation results demonstrate the efficacy of
the proposed method in detecting manufacturing faults with only a small number
of test vectors.Comment: Design, Automation and Test in Europe (DATE), March 201
How to Backdoor Diffusion Models?
Diffusion models are state-of-the-art deep learning empowered generative
models that are trained based on the principle of learning forward and reverse
diffusion processes via progressive noise-addition and denoising. To gain a
better understanding of the limitations and potential risks, this paper
presents the first study on the robustness of diffusion models against backdoor
attacks. Specifically, we propose BadDiffusion, a novel attack framework that
engineers compromised diffusion processes during model training for backdoor
implantation. At the inference stage, the backdoored diffusion model will
behave just like an untampered generator for regular data inputs, while falsely
generating some targeted outcome designed by the bad actor upon receiving the
implanted trigger signal. Such a critical risk can be dreadful for downstream
tasks and applications built upon the problematic model. Our extensive
experiments on various backdoor attack settings show that BadDiffusion can
consistently lead to compromised diffusion models with high utility and target
specificity. Even worse, BadDiffusion can be made cost-effective by simply
finetuning a clean pre-trained diffusion model to implant backdoors. We also
explore some possible countermeasures for risk mitigation. Our results call
attention to potential risks and possible misuse of diffusion models
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