710 research outputs found
Quantization of Even-Dimensional Actions of Chern-Simons Form with Infinite Reducibility
We investigate the quantization of even-dimensional topological actions of
Chern-Simons form which were proposed previously. We quantize the actions by
Lagrangian and Hamiltonian formulations {\`a} la Batalin, Fradkin and
Vilkovisky. The models turn out to be infinitely reducible and thus we need
infinite number of ghosts and antighosts. The minimal actions of Lagrangian
formulation which satisfy the master equation of Batalin and Vilkovisky have
the same Chern-Simons form as the starting classical actions. In the
Hamiltonian formulation we have used the formulation of cohomological
perturbation and explicitly shown that the gauge-fixed actions of both
formulations coincide even though the classical action breaks Dirac's
regularity condition. We find an interesting relation that the BRST charge of
Hamiltonian formulation is the odd-dimensional fermionic counterpart of the
topological action of Chern-Simons form. Although the quantization of two
dimensional models which include both bosonic and fermionic gauge fields are
investigated in detail, it is straightforward to extend the quantization into
arbitrary even dimensions. This completes the quantization of previously
proposed topological gravities in two and four dimensions.Comment: 50 pages, latex, no figure
Generalized Gauge Theories and Weinberg-Salam Model with Dirac-K\"ahler Fermions
We extend previously proposed generalized gauge theory formulation of
Chern-Simons type and topological Yang-Mills type actions into Yang-Mills type
actions. We formulate gauge fields and Dirac-K\"ahler matter fermions by all
degrees of differential forms. The simplest version of the model which includes
only zero and one form gauge fields accommodated with the graded Lie algebra of
supergroup leads Weinberg-Salam model. Thus the Weinberg-Salam model
formulated by noncommutative geometry is a particular example of the present
formulation.Comment: 33 pages, LaTe
Conversion of T cells to B cells by inactivation of polycomb-mediated epigenetic suppression of the B-lineage program
12 p.-6 fig.1 tab.Tomokatsu Ikawa, et al.In general, cell fate is determined primarily by transcription factors, followed by epigenetic mechanisms fixing the status. While the importance of transcription factors controlling cell fate has been well characterized, epigenetic regulation of cell fate maintenance remains to be elucidated. Here we provide an obvious fate conversion case, in which the inactivation of polycomb-medicated epigenetic regulation results in conversion of T-lineage progenitors to the B-cell fate. In T-cell-specific Ring1A/B-deficient mice, T-cell development was severely blocked at an immature stage. We found that these developmentally arrested T-cell precursors gave rise to functional B cells upon transfer to immunodeficient mice. We further demonstrated that the arrest was almost completely canceled by additional deletion of Pax5. These results indicate that the maintenance of T-cell fate critically requires epigenetic suppression of the B-lineage gene program.This work was supported in part by grants from the Japan Society for the Promotion of Science (24689042 to T.I.), the Japan Science and Technology Agency (T.I.), RIKEN Center for Integrative Medical Sciences (IMS) Young Chief Investigator program (T.I.), and the Kanae Foundation for the Promotion of Medical Science (T.I.).Peer reviewe
Game-Theoretic Understanding of Misclassification
This paper analyzes various types of image misclassification from a
game-theoretic view. Particularly, we consider the misclassification of clean,
adversarial, and corrupted images and characterize it through the distribution
of multi-order interactions. We discover that the distribution of multi-order
interactions varies across the types of misclassification. For example,
misclassified adversarial images have a higher strength of high-order
interactions than correctly classified clean images, which indicates that
adversarial perturbations create spurious features that arise from complex
cooperation between pixels. By contrast, misclassified corrupted images have a
lower strength of low-order interactions than correctly classified clean
images, which indicates that corruptions break the local cooperation between
pixels. We also provide the first analysis of Vision Transformers using
interactions. We found that Vision Transformers show a different tendency in
the distribution of interactions from that in CNNs, and this implies that they
exploit the features that CNNs do not use for the prediction. Our study
demonstrates that the recent game-theoretic analysis of deep learning models
can be broadened to analyze various malfunctions of deep learning models
including Vision Transformers by using the distribution, order, and sign of
interactions.Comment: 15 pages, 8 figure
Adversarial joint attacks on legged robots
We address adversarial attacks on the actuators at the joints of legged
robots trained by deep reinforcement learning. The vulnerability to the joint
attacks can significantly impact the safety and robustness of legged robots. In
this study, we demonstrate that the adversarial perturbations to the torque
control signals of the actuators can significantly reduce the rewards and cause
walking instability in robots. To find the adversarial torque perturbations, we
develop black-box adversarial attacks, where, the adversary cannot access the
neural networks trained by deep reinforcement learning. The black box attack
can be applied to legged robots regardless of the architecture and algorithms
of deep reinforcement learning. We employ three search methods for the
black-box adversarial attacks: random search, differential evolution, and
numerical gradient descent methods. In experiments with the quadruped robot
Ant-v2 and the bipedal robot Humanoid-v2, in OpenAI Gym environments, we find
that differential evolution can efficiently find the strongest torque
perturbations among the three methods. In addition, we realize that the
quadruped robot Ant-v2 is vulnerable to the adversarial perturbations, whereas
the bipedal robot Humanoid-v2 is robust to the perturbations. Consequently, the
joint attacks can be used for proactive diagnosis of robot walking instability.Comment: 6 pages, 8 figure
Adversarially Trained Object Detector for Unsupervised Domain Adaptation
Unsupervised domain adaptation, which involves transferring knowledge from a
label-rich source domain to an unlabeled target domain, can be used to
substantially reduce annotation costs in the field of object detection. In this
study, we demonstrate that adversarial training in the source domain can be
employed as a new approach for unsupervised domain adaptation. Specifically, we
establish that adversarially trained detectors achieve improved detection
performance in target domains that are significantly shifted from source
domains. This phenomenon is attributed to the fact that adversarially trained
detectors can be used to extract robust features that are in alignment with
human perception and worth transferring across domains while discarding
domain-specific non-robust features. In addition, we propose a method that
combines adversarial training and feature alignment to ensure the improved
alignment of robust features with the target domain. We conduct experiments on
four benchmark datasets and confirm the effectiveness of our proposed approach
on large domain shifts from real to artistic images. Compared to the baseline
models, the adversarially trained detectors improve the mean average precision
by up to 7.7%, and further by up to 11.8% when feature alignments are
incorporated. Although our method degrades performance for small domain shifts,
quantification of the domain shift based on the Frechet distance allows us to
determine whether adversarial training should be conducted.Comment: 10 pages, 6 figures. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
Fourier Analysis on Robustness of Graph Convolutional Neural Networks for Skeleton-based Action Recognition
Using Fourier analysis, we explore the robustness and vulnerability of graph
convolutional neural networks (GCNs) for skeleton-based action recognition. We
adopt a joint Fourier transform (JFT), a combination of the graph Fourier
transform (GFT) and the discrete Fourier transform (DFT), to examine the
robustness of adversarially-trained GCNs against adversarial attacks and common
corruptions. Experimental results with the NTU RGB+D dataset reveal that
adversarial training does not introduce a robustness trade-off between
adversarial attacks and low-frequency perturbations, which typically occurs
during image classification based on convolutional neural networks. This
finding indicates that adversarial training is a practical approach to
enhancing robustness against adversarial attacks and common corruptions in
skeleton-based action recognition. Furthermore, we find that the Fourier
approach cannot explain vulnerability against skeletal part occlusion
corruption, which highlights its limitations. These findings extend our
understanding of the robustness of GCNs, potentially guiding the development of
more robust learning methods for skeleton-based action recognition.Comment: 17 pages, 13 figure
Improving Accuracy of Zero-Shot Action Recognition with Handcrafted Features
With the development of machine learning, datasets for models are getting
increasingly larger. This leads to increased data annotation costs and training
time, which undoubtedly hinders the development of machine learning. To solve
this problem, zero-shot learning is gaining considerable attention. With
zero-shot learning, objects can be recognized or classified, even without
having been seen before. Nevertheless, the accuracy of this method is still
low, thus limiting its practical application. To solve this problem, we propose
a video-text matching model, which can learn from handcrafted features. Our
model can be used alone to predict the action classes and can also be added to
any other model to improve its accuracy. Moreover, our model can be
continuously optimized to improve its accuracy. We only need to manually
annotate some features, which incurs some labor costs; in many situations, the
costs are worth it. The results with UCF101 and HMDB51 show that our model
achieves the best accuracy and also improves the accuracies of other models.Comment: 15 pages, 7 figure
- …