334 research outputs found
Self-similar planar graphs as models for complex networks
In this paper we introduce a family of planar, modular and self-similar
graphs which have small-world and scale-free properties. The main parameters of
this family are comparable to those of networks associated to complex systems,
and therefore the graphs are of interest as mathematical models for these
systems. As the clustering coefficient of the graphs is zero, this family is an
explicit construction that does not match the usual characterization of
hierarchical modular networks, namely that vertices have clustering values
inversely proportional to their degrees.Comment: 10 pages, submitted to 19th International Workshop on Combinatorial
Algorithms (IWOCA 2008
Planar unclustered scale-free graphs as models for technological and biological networks
Many real life networks present an average path length logarithmic with the
number of nodes and a degree distribution which follows a power law. Often
these networks have also a modular and self-similar structure and, in some
cases - usually associated with topological restrictions- their clustering is
low and they are almost planar. In this paper we introduce a family of graphs
which share all these properties and are defined by two parameters. As their
construction is deterministic, we obtain exact analytic expressions for
relevant properties of the graphs including the degree distribution, degree
correlation, diameter, and average distance, as a function of the two defining
parameters. Thus, the graphs are useful to model some complex networks, in
particular several families of technological and biological networks, and in
the design of new practical communication algorithms in relation to their
dynamical processes. They can also help understanding the underlying mechanisms
that have produced their particular structure.Comment: Accepted for publication in Physica
GM-CSFとガンマ線照射腫瘍細胞の放出因子の組み合わせによる骨髄細胞からのマクロファージ分化と、それらの抗原提示機能および1型への極性化の促進
Granulocyte-macrophage colony-stimulating factor (GM-CSF) promotes dendritic cell differentiation from precursors, and consequently, enhances the antigen presentation process and adaptive immune responses. With such functions, GM-CSF has been used as immunotherapy in combination with radiotherapy for cancer treatment to augment the survival and activity of immune cells. However, an immune-suppressive tumor microenvironment may cause anergy of T cells. It has also been reported that GM-CSF contributes to the development of myeloid-derived suppressor cells from the precursors. In this study, to analyze the combined effect of GM-CSF and released factors from cancer cells after gamma-ray irradiation on bone marrow cell differentiation and dynamics, we established an in vitro culture system using mouse bone marrow cells, GM-CSF, and conditioned medium from gamma ray irradiated mouse melanoma B16 cells at 24 Gy. We analyzed the gene expression changes of the bone marrow-derived cells on day 6. The results showed that GM-CSF dose-dependently enhanced the differentiation of macrophages from bone marrow cells, their antigenpresenting function and polarization to type I. The results implied the induced macrophages from the bone marrow may potentially contribute to tumor immune responses in a systemic manner when GM-CSF is boosted during photon-beam radiation therapy.長崎大学学位論文 学位記番号:博(医歯薬)甲第1359号 学位授与年月日:令和3年9月17日Author: Lichao Chen, Shoji Imamichi, Ying Tong, Yuka Sasaki, Takae Onodera, Satoshi Nakamura, Hiroshi Igaki, Jun Itami, Mitsuko MasutaniCitation: Medicines, 8(7), art. no. 35; 2021Nagasaki University (長崎大学)課程博
Exact analytical solution of average path length for Apollonian networks
The exact formula for the average path length of Apollonian networks is
found. With the help of recursion relations derived from the self-similar
structure, we obtain the exact solution of average path length, ,
for Apollonian networks. In contrast to the well-known numerical result
[Phys. Rev. Lett. \textbf{94}, 018702
(2005)], our rigorous solution shows that the average path length grows
logarithmically as in the infinite limit of network
size . The extensive numerical calculations completely agree with our
closed-form solution.Comment: 8 pages, 4 figure
Stable Unlearnable Example: Enhancing the Robustness of Unlearnable Examples via Stable Error-Minimizing Noise
The open source of large amounts of image data promotes the development of
deep learning techniques. Along with this comes the privacy risk of these
open-source image datasets being exploited by unauthorized third parties to
train deep learning models for commercial or illegal purposes. To avoid the
abuse of public data, a poisoning-based technique, the unlearnable example, is
proposed to significantly degrade the generalization performance of models by
adding a kind of imperceptible noise to the data. To further enhance its
robustness against adversarial training, existing works leverage iterative
adversarial training on both the defensive noise and the surrogate model.
However, it still remains unknown whether the robustness of unlearnable
examples primarily comes from the effect of enhancement in the surrogate model
or the defensive noise. Observing that simply removing the adversarial noise on
the training process of the defensive noise can improve the performance of
robust unlearnable examples, we identify that solely the surrogate model's
robustness contributes to the performance. Furthermore, we found a negative
correlation exists between the robustness of defensive noise and the protection
performance, indicating defensive noise's instability issue. Motivated by this,
to further boost the robust unlearnable example, we introduce stable
error-minimizing noise (SEM), which trains the defensive noise against random
perturbation instead of the time-consuming adversarial perturbation to improve
the stability of defensive noise. Through extensive experiments, we demonstrate
that SEM achieves a new state-of-the-art performance on CIFAR-10, CIFAR-100,
and ImageNet Subset in terms of both effectiveness and efficiency. The code is
available at https://github.com/liuyixin-louis/Stable-Unlearnable-Example.Comment: Accepted to AAAI 202
Brain-inspired automated visual object discovery and detection
Despite significant recent progress, machine vision systems lag considerably behind their biological counterparts in performance, scalability, and robustness. A distinctive hallmark of the brain is its ability to automatically discover and model objects, at multiscale resolutions, from repeated exposures to unlabeled contextual data and then to be able to robustly detect the learned objects under various nonideal circumstances, such as partial occlusion and different view angles. Replication of such capabilities in a machine would require three key ingredients: (i) access to large-scale perceptual data of the kind that humans experience, (ii) flexible representations of objects, and (iii) an efficient unsupervised learning algorithm. The Internet fortunately provides unprecedented access to vast amounts of visual data. This paper leverages the availability of such data to develop a scalable framework for unsupervised learning of object prototypes—brain-inspired flexible, scale, and shift invariant representations of deformable objects (e.g., humans, motorcycles, cars, airplanes) comprised of parts, their different configurations and views, and their spatial relationships. Computationally, the object prototypes are represented as geometric associative networks using probabilistic constructs such as Markov random fields. We apply our framework to various datasets and show that our approach is computationally scalable and can construct accurate and operational part-aware object models much more efficiently than in much of the recent computer vision literature. We also present efficient algorithms for detection and localization in new scenes of objects and their partial views
RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation
Source-Free domain adaptation transits the source-trained model towards
target domain without exposing the source data, trying to dispel these concerns
about data privacy and security. However, this paradigm is still at risk of
data leakage due to adversarial attacks on the source model. Hence, the
Black-Box setting only allows to use the outputs of source model, but still
suffers from overfitting on the source domain more severely due to source
model's unseen weights. In this paper, we propose a novel approach named RAIN
(RegulArization on Input and Network) for Black-Box domain adaptation from both
input-level and network-level regularization. For the input-level, we design a
new data augmentation technique as Phase MixUp, which highlights task-relevant
objects in the interpolations, thus enhancing input-level regularization and
class consistency for target models. For network-level, we develop a Subnetwork
Distillation mechanism to transfer knowledge from the target subnetwork to the
full target network via knowledge distillation, which thus alleviates
overfitting on the source domain by learning diverse target representations.
Extensive experiments show that our method achieves state-of-the-art
performance on several cross-domain benchmarks under both single- and
multi-source black-box domain adaptation.Comment: Accepted by IJCAI 202
Planar unclustered graphs to model technological and biological networks
Many real life networks present an average path length logarithmic with the
number of nodes and a degree distribution which follows a power law. Often
these networks have also a modular and self-similar structure and, in some
cases - usually associated with topological restrictions- their clustering is
low and they are almost planar. In this paper we introduce a family of graphs
which share all these properties and are defined by two parameters. As their
construction is deterministic, we obtain exact analytic expressions for
relevant properties of the graphs including the degree distribution, degree
correlation, diameter, and average distance, as a function of the two defining
parameters. Thus, the graphs are useful to model some complex networks, in
particular technological and biological networks
- …