1,816 research outputs found
Tuning thermal transport in nanotubes with topological defects
Using the atomistic nonequilibrium Green's function, we find that thermal
conductance of carbon nanotubes with presence of topological lattice imperfects
is remarkably reduced, due to the strong Rayleigh scattering of high-frequency
phonons. Phonon transmission across multiple defects behaves as a cascade
scattering based with the random phase approximation. We elucidate that phonon
scattering by structural defects is related to the spatial fluctuations of
local vibrational density of states (LVDOS). An effective method of tuning
thermal transport in low-dimensional systems through the modulation of LVDOS
has been proposed. Our findings provide insights into experimentally
controlling thermal transport in nanoscale devicesComment: 10 pages, 3 figure
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
One-loop Helicity Amplitudes for Top Quark Pair Production in Randall-Sundrum Model
In this paper, we show how to calculate analytically the one-loop helicity
amplitudes for the process induced by KK gluon,
using the spinor-helicity formalism. A minimal set of Feynman rules which are
uniquely fixed by gauge invariance and the color representation of the KK gluon
are derived and used in the calculation. Our results can be applied to a
variety of models containing a massive color octet vector boson.Comment: 37 pages, 10 figures, journal versio
A variation of a classical Turán-type extremal problem
AbstractA variation of a classical Turán-type extremal problem (Erdős on Graphs: His Legacy of Unsolved Problems (1998) p. 36) is considered as follows: determine the smallest even integer σ(Kr,s,n) such that every n-term graphic non-increasing sequence π=(d1,d2,…,dn) with term sum σ(π)=d1+d2+⋯+dn≥σ(Kr,s,n) has a realization G containing Kr,s as a subgraph, where Kr,s is a r×s complete bipartite graph. In this paper, we determine σ(Kr,s,n) exactly for every fixed s≥r≥3 when n≥n0(r,s), where m=[(r+s+1)24] andn0(r,s)=m+3s2−2s−6,ifs≤2randsis even,m+3s2+2s−8,ifs≤2randsis odd,m+2s2+(2r−6)s+4r−8,ifs≥2r+1
PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation
Unsupervised domain adaptation (UDA) has witnessed remarkable advancements in
improving the accuracy of models for unlabeled target domains. However, the
calibration of predictive uncertainty in the target domain, a crucial aspect of
the safe deployment of UDA models, has received limited attention. The
conventional in-domain calibration method, \textit{temperature scaling}
(TempScal), encounters challenges due to domain distribution shifts and the
absence of labeled target domain data. Recent approaches have employed
importance-weighting techniques to estimate the target-optimal temperature
based on re-weighted labeled source data. Nonetheless, these methods require
source data and suffer from unreliable density estimates under severe domain
shifts, rendering them unsuitable for source-free UDA settings. To overcome
these limitations, we propose PseudoCal, a source-free calibration method that
exclusively relies on unlabeled target data. Unlike previous approaches that
treat UDA calibration as a \textit{covariate shift} problem, we consider it as
an unsupervised calibration problem specific to the target domain. Motivated by
the factorization of the negative log-likelihood (NLL) objective in TempScal,
we generate a labeled pseudo-target set that captures the structure of the real
target. By doing so, we transform the unsupervised calibration problem into a
supervised one, enabling us to effectively address it using widely-used
in-domain methods like TempScal. Finally, we thoroughly evaluate the
calibration performance of PseudoCal by conducting extensive experiments on 10
UDA methods, considering both traditional UDA settings and recent source-free
UDA scenarios. The experimental results consistently demonstrate the superior
performance of PseudoCal, exhibiting significantly reduced calibration error
compared to existing calibration methods
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
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