Universal Thermoelectric Effect of Dirac Fermions in Graphene

We numerically study the thermoelectric transports of Dirac fermions in graphene in the presence of a strong magnetic field and disorder. We find that the thermoelectric transport coefficients demonstrate universal behavior depending on the ratio between the temperature and the width of the disorder-broadened Landau levels(LLs). The transverse thermoelectric conductivity $\alpha_{xy}$ reaches a universal quantum value at the center of each LL in the high temperature regime, and it has a linear temperature dependence at low temperatures. The calculated Nernst signal has a peak at the central LL with heights of the order of $k_B/e$, and changes sign near other LLs, while the thermopower has an opposite behavior, in good agreement with experimental data. The validity of the generalized Mott relation between the thermoelectric and electrical transport coefficients is verified in a wide range of temperatures.Comment: 4 pages, 4 figures, published versio

Self-training solutions for the ICCV 2023 GeoNet Challenge

GeoNet is a recently proposed domain adaptation benchmark consisting of three challenges (i.e., GeoUniDA, GeoImNet, and GeoPlaces). Each challenge contains images collected from the USA and Asia where there are huge geographical gaps. Our solution adopts a two-stage source-free domain adaptation framework with a Swin Transformer backbone to achieve knowledge transfer from the USA (source) domain to Asia (target) domain. In the first stage, we train a source model using labeled source data with a re-sampling strategy and two types of cross-entropy loss. In the second stage, we generate pseudo labels for unlabeled target data to fine-tune the model. Our method achieves an H-score of 74.56% and ultimately ranks 1st in the GeoUniDA challenge. In GeoImNet and GeoPlaces challenges, our solution also reaches a top-3 accuracy of 64.46% and 51.23%, respectively.Comment: technical report; 1st in the ICCV-2023 GeoUniDA challeng

Towards Realistic Unsupervised Fine-tuning with CLIP

The emergence of vision-language models (VLMs), such as CLIP, has spurred a significant research effort towards their application for downstream supervised learning tasks. Although some previous studies have explored the unsupervised fine-tuning of CLIP, they often rely on prior knowledge in the form of class names associated with ground truth labels. In this paper, we delve into a realistic unsupervised fine-tuning scenario by assuming that the unlabeled data might contain out-of-distribution samples from unknown classes. Furthermore, we emphasize the importance of simultaneously enhancing out-of-distribution detection capabilities alongside the recognition of instances associated with predefined class labels. To tackle this problem, we present a simple, efficient, and effective fine-tuning approach called Universal Entropy Optimization (UEO). UEO leverages sample-level confidence to approximately minimize the conditional entropy of confident instances and maximize the marginal entropy of less confident instances. Apart from optimizing the textual prompts, UEO also incorporates optimization of channel-wise affine transformations within the visual branch of CLIP. Through extensive experiments conducted across 15 domains and 4 different types of prior knowledge, we demonstrate that UEO surpasses baseline methods in terms of both generalization and out-of-distribution detection

AdaptGuard: Defending Against Universal Attacks for Model Adaptation

Model adaptation aims at solving the domain transfer problem under the constraint of only accessing the pretrained source models. With the increasing considerations of data privacy and transmission efficiency, this paradigm has been gaining recent popularity. This paper studies the vulnerability to universal attacks transferred from the source domain during model adaptation algorithms due to the existence of malicious providers. We explore both universal adversarial perturbations and backdoor attacks as loopholes on the source side and discover that they still survive in the target models after adaptation. To address this issue, we propose a model preprocessing framework, named AdaptGuard, to improve the security of model adaptation algorithms. AdaptGuard avoids direct use of the risky source parameters through knowledge distillation and utilizes the pseudo adversarial samples under adjusted radius to enhance the robustness. AdaptGuard is a plug-and-play module that requires neither robust pretrained models nor any changes for the following model adaptation algorithms. Extensive results on three commonly used datasets and two popular adaptation methods validate that AdaptGuard can effectively defend against universal attacks and maintain clean accuracy in the target domain simultaneously. We hope this research will shed light on the safety and robustness of transfer learning. Code is available at https://github.com/TomSheng21/AdaptGuard.Comment: ICCV202

Can We Trust the Unlabeled Target Data? Towards Backdoor Attack and Defense on Model Adaptation

Model adaptation tackles the distribution shift problem with a pre-trained model instead of raw data, becoming a popular paradigm due to its great privacy protection. Existing methods always assume adapting to a clean target domain, overlooking the security risks of unlabeled samples. In this paper, we explore the potential backdoor attacks on model adaptation launched by well-designed poisoning target data. Concretely, we provide two backdoor triggers with two poisoning strategies for different prior knowledge owned by attackers. These attacks achieve a high success rate and keep the normal performance on clean samples in the test stage. To defend against backdoor embedding, we propose a plug-and-play method named MixAdapt, combining it with existing adaptation algorithms. Experiments across commonly used benchmarks and adaptation methods demonstrate the effectiveness of MixAdapt. We hope this work will shed light on the safety of learning with unlabeled data.Comment: 11 pages, 4 figure

A Novel Coordinate Transformation Based Self-coupling Computation Approach For The Method Of Moments

A new highly accurate and efficient coordinate transformation algorithm is proposed for the evaluation of the self-coupling in the Method of Moments (MoM), which produces usually the strongest contributions to the MoM system matrices. The new algorithm provides an effective solution to remove the singularity due to the Green\u27s function inside the self-couplings. Moreover, the new solution reduces the integral dimension from 4-D to 1-D. Thus, better accuracy and efficiency are obtained for the self-coupling integrals