LightVessel: Exploring Lightweight Coronary Artery Vessel Segmentation via Similarity Knowledge Distillation

In recent years, deep convolution neural networks (DCNNs) have achieved great prospects in coronary artery vessel segmentation. However, it is difficult to deploy complicated models in clinical scenarios since high-performance approaches have excessive parameters and high computation costs. To tackle this problem, we propose \textbf{LightVessel}, a Similarity Knowledge Distillation Framework, for lightweight coronary artery vessel segmentation. Primarily, we propose a Feature-wise Similarity Distillation (FSD) module for semantic-shift modeling. Specifically, we calculate the feature similarity between the symmetric layers from the encoder and decoder. Then the similarity is transferred as knowledge from a cumbersome teacher network to a non-trained lightweight student network. Meanwhile, for encouraging the student model to learn more pixel-wise semantic information, we introduce the Adversarial Similarity Distillation (ASD) module. Concretely, the ASD module aims to construct the spatial adversarial correlation between the annotation and prediction from the teacher and student models, respectively. Through the ASD module, the student model obtains fined-grained subtle edge segmented results of the coronary artery vessel. Extensive experiments conducted on Clinical Coronary Artery Vessel Dataset demonstrate that LightVessel outperforms various knowledge distillation counterparts.Comment: 5 pages, 7 figures, conferenc

Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning

Biphasic face photo-sketch synthesis has significant practical value in wide-ranging fields such as digital entertainment and law enforcement. Previous approaches directly generate the photo-sketch in a global view, they always suffer from the low quality of sketches and complex photo variations, leading to unnatural and low-fidelity results. In this paper, we propose a novel Semantic-Driven Generative Adversarial Network to address the above issues, cooperating with Graph Representation Learning. Considering that human faces have distinct spatial structures, we first inject class-wise semantic layouts into the generator to provide style-based spatial information for synthesized face photos and sketches. Additionally, to enhance the authenticity of details in generated faces, we construct two types of representational graphs via semantic parsing maps upon input faces, dubbed the IntrA-class Semantic Graph (IASG) and the InteR-class Structure Graph (IRSG). Specifically, the IASG effectively models the intra-class semantic correlations of each facial semantic component, thus producing realistic facial details. To preserve the generated faces being more structure-coordinated, the IRSG models inter-class structural relations among every facial component by graph representation learning. To further enhance the perceptual quality of synthesized images, we present a biphasic interactive cycle training strategy by fully taking advantage of the multi-level feature consistency between the photo and sketch. Extensive experiments demonstrate that our method outperforms the state-of-the-art competitors on the CUFS and CUFSF datasets.Comment: Accepted to IEEE TNNL

Implementing Quantum Search Algorithm with Metamaterials

Metamaterials, artificially structured electromagnetic (EM) materials, have enabled the realization of many unconventional electromagnetic properties not found in nature, such as negative refractive index, magnetic response, invisibility cloaking and so on. Based on these man-made materials with novel EM properties, various devices have been designed and realized. However, quantum analog devices based on metamaterials have not been achieved so far. Here we designed and printed metamaterials to perform quantum search algorithm. The structures, comprising of an array of two-dimensional (2D) sub-wavelength air holes with different radii perforated on the dielectric layer, have been fabricated by using 3D printing technique. When an incident wave enters in the designed metamaterials, the profile of beam wavefront is processed iteratively as it propagates through the metamaterial periodically. After roundtrips, precisely the same as the efficiency of quantum search algorithm, searched items will be found with the incident wave all focusing on the marked positions. Such a metamaterial-based quantum searching simulator may lead to remarkable achievements in wave-based signal processors.Comment: 22 pages,6 figure

Gender Transformation of Kwan Yin in Central China: The Mother Stereotype

Kwan Yin, although typically depicted as female in Chinese literature and artworks, is originally a male deity Avalokite?vara in India. This essay examines the process, reason, and impact of Kwan Yin’s feminization in the ancient Chinese context and argues that her gender transformation is a transformation to the mother stereotype. The essay mainly relies on primary source of Buddhist texts and folklores of Kwan-Yin in China and secondary sources researching the gender transformation of Kwan-Yin through historical and sociological lens. The essay concludes that while the female Kwan Yin’s popularity could be seen as gender empowering, the mother stereotype she and female deities of other religions embody in fact dismisses woman’s individual value

Stability and performance analysis of a congestion control algorithm for networks

Dimirovski, Georgi M. (Dogus Author) -- Conference full title: 46th IEEE Conference on Decision and Control : December 12-14, 2007, New Orleans, LA.In this paper, we show that max-min fair congestion control methods with a stable symmetric Jacobian remain stable under arbitrary feedback delay and the stability condition of such methods does not involve any of the delays. To demonstrate the practicality of the obtained result, we change the original algorithm in Kelly's work to become robust under random feedback delay and fixed constants of the control equation. The performance analysis shows that it offers smooth sending rate, exponential convergence to efficiency, and fast convergence to fairness, all of which make it be meaningful for future high-speed networks

Topological Regularization for Representation Learning via Persistent Homology

Generalization is challenging in small-sample-size regimes with over-parameterized deep neural networks, and a better representation is generally beneficial for generalization. In this paper, we present a novel method for controlling the internal representation of deep neural networks from a topological perspective. Leveraging the power of topology data analysis (TDA), we study the push-forward probability measure induced by the feature extractor, and we formulate a notion of “separation” to characterize a property of this measure in terms of persistent homology for the first time. Moreover, we perform a theoretical analysis of this property and prove that enforcing this property leads to better generalization. To impose this property, we propose a novel weight function to extract topological information, and we introduce a new regularizer including three items to guide the representation learning in a topology-aware manner. Experimental results in the point cloud optimization task show that our method is effective and powerful. Furthermore, results in the image classification task show that our method outperforms the previous methods by a significant margin

Analysis of land use dynamic econometric change in Chaohu basin from 2000 to 2015

Chaohu basin is located in the central economic belt, as an important part of the Yangtze River Basin. Based on the data of land use classification from 2000 to 2015, this paper makes a dynamic econometric analysis on the spatial structure of land use in Chaohu basin. The results show that: (1) The agricultural land has been in a downward trend from 2000 to 2015. However, the construction land has been increasing. From 2005 to 2010, the change of agriculture and the construction land have reached the maximum value, which are -0.279% and 1.814%, respectively. (2) During 2000-2015, every five years, the change of land use degree in Chaohu basin is greater than 0. The land use in the study area is in the development period, among which the change of land use from 2000 to 2005 is the largest. (3) The information entropy of land use in Chaohu basin is more than 1, and the change of information entropy is more than 0, which indicates that the information entropy is on the rise and the land use is in disorder. The results are of practical significance to the ecological security and optimal regulation of Chaohu Basin

Radial growth of two dominant montane conifer tree species in response to climate change in North-Central China.

North-Central China is a region in which the air temperature has clearly increased for several decades. Picea meyeri and Larix principis-rupprechtii are the most dominant co-occurring tree species within the cold coniferous forest belt ranging vertically from 1800 m to 2800 m a.s.l. in this region. Based on a tree-ring analysis of 292 increment cores sampled from 146 trees at different elevations, this study aimed to examine if the radial growth of the two species in response to climate is similar, whether the responses are consistent along altitudinal gradients and which species might be favored in the future driven by the changing climate. The results indicated the following: (1) The two species grew in different rhythms at low and high elevation respectively; (2) Both species displayed inconsistent relationships between radial growth and climate data along altitudinal gradients. The correlation between radial growth and the monthly mean temperature in the spring or summer changed from negative at low elevation into positive at high elevation, whereas those between the radial growth and the total monthly precipitation displayed a change from positive into negative along the elevation gradient. These indicate the different influences of the horizontal climate and vertical mountainous climate on the radial growth of the two species; (3) The species-dependent different response to climate in radial growth appeared mainly in autumn of the previous year. The radial growth of L. principis-rupprechtii displayed negative responses both to temperature and to precipitation in the previous September, October or November, which was not observed in the radial growth of P. meyeri. (4) The radial growth of both species will tend to be increased at high elevation and limited at low elevation, and L. principis-rupprechtii might be more favored in the future, if the temperature keeps rising