On the Spectrum of a Class of Distance-transitive Graphs

Let $\Gamma=Cay(\mathbb{Z}_n, S_k)$ be the Cayley graph on the cyclic additive group $\mathbb{Z}_n$ $(n\geq 4),$ where $S_1=\{1, n-1\}$, \dots , $S_k=S_ {k-1}\cup\{k, n-k\}$ are the inverse-closed subsets of $\mathbb{Z}_n-\{0\}$ for any $k\in \mathbb{N}$, $1\leq k\leq [\frac{n}{2}]-1$. In this paper, we will show that $\chi(\Gamma) = \omega(\Gamma)=k+1$ if and only if $k+1|n$. Also, we will show that if $n$ is an even integer and $k=\frac{n}{2}-1$ then $Aut(\Gamma)\cong\mathbb{Z}_2 wr_{I} {Sym}(k+1)$ where $I=\{1, \dots , k+1\}$ and in this case, we show that $\Gamma$ is an integral graph

Assessment of cd93 stem cell growth and survival on three-dimensional biodegradable pcl-gelatin scaffold

Background and purpose: Application of three-dimensional scaffolds with the ability to simulate a three-dimensional in vivo environment has opened new perspective on targeted differentiation and therapeutic use of stem cells. In this study we examined the compatibility of CD93 stem cells with biodegradable pcl- gelatin scaffold. Materials and methods: In this experimental study, three-dimensional scaffolds made of PCL -gelatin using electrospining synthesis and its molecular structure was tested by SEM electron microscopy. The scaffold surface was disinfected by UV ray. The hematopoietic CD93stem cells of those isolated previously were divided into two groups including normal cultured (plate) and culture on scaffolds (scaffold + cell). The survival and growth of the cells were measured through MTT assay and electron microscopy at 7, 14, and 28 days after culturing. Results: Electron microscopic analysis on the seventh day showed appropriate adhesion of CD93 cells on scaffold fibers and secretion of extracellular matrix. Survival rate of the cells at 7, 14, and 28 days after culturing were not significantly different between the two groups. But at the same days significant differences were observed in the Scaffold + Cell group (P< 0.05). Conclusion: This study suggests that PCL nanofiber scaffolds has high compatibility with CD93 stem cells and proximity to this scaffold lead to increased survival and growth of the cells. Further studies on the treatment of tissue damage and scarring by CD93 stem cells using this scaffold can be effective in increasing treatment efficiency. © 2016, Mazandaran University of Medical Sciences. All rights reserved

Trading-off Mutual Information on Feature Aggregation for Face Recognition

Despite the advances in the field of Face Recognition (FR), the precision of these methods is not yet sufficient. To improve the FR performance, this paper proposes a technique to aggregate the outputs of two state-of-the-art (SOTA) deep FR models, namely ArcFace and AdaFace. In our approach, we leverage the transformer attention mechanism to exploit the relationship between different parts of two feature maps. By doing so, we aim to enhance the overall discriminative power of the FR system. One of the challenges in feature aggregation is the effective modeling of both local and global dependencies. Conventional transformers are known for their ability to capture long-range dependencies, but they often struggle with modeling local dependencies accurately. To address this limitation, we augment the self-attention mechanism to capture both local and global dependencies effectively. This allows our model to take advantage of the overlapping receptive fields present in corresponding locations of the feature maps. However, fusing two feature maps from different FR models might introduce redundancies to the face embedding. Since these models often share identical backbone architectures, the resulting feature maps may contain overlapping information, which can mislead the training process. To overcome this problem, we leverage the principle of Information Bottleneck to obtain a maximally informative facial representation. This ensures that the aggregated features retain the most relevant and discriminative information while minimizing redundant or misleading details. To evaluate the effectiveness of our proposed method, we conducted experiments on popular benchmarks and compared our results with state-of-the-art algorithms. The consistent improvement we observed in these benchmarks demonstrates the efficacy of our approach in enhancing FR performance.Comment: Accepted to 22$^{nd}$ IEEE International Conference on Machine Learning and Applications 2023 (ICMLA

Remyelination of the corpus callosum by olfactory ensheathing cell in an experimental model of multiple sclerosis

Multiple Sclerosis (MS) causes loss of the myelin sheath, which leads to loss of neurons. Regeneration of myelin sheath stimulates axon regeneration and neurons� survival. In this study, olfactory ensheathing cell (OEC) transplantation is investigated to restore myelin sheath in an experimental model of MS in male mice.OECs were isolated from the olfactory mucosa of seven-day-old infant rats and cultured. Then, cells were evaluated and approved by flow cytometry by p75 and GFAP markers. A total of 32 mice (C57BL /6) were studied in four groups; 1) without any treatment (control), 2) Sham (receiving PBS), 3) MS model and 4) MS and OEC transplantation. MS was induced by adding Cuprizon in the diet of animals for six weeks. After the expiration of 20 days, histologic analysis was performed with approval of the presence of cells in the graft area and the removal of myelin and myelin regeneration with two types of luxal fast blue (LFB) staining and immunohistochemistry. The purity of the cells ensheathing the olfactory was 90. There was a significant difference in Myelin percentage of PBS and OEC recipient groups (P�0.05). MBP and PLP of the myelin sheath in the group receiving OECs were more than MS group.According to the findings, in MS model MBP and PLP of the myelin sheath is reduced. In the group receiving OECs, it was returned to a normal level significantly compared to the sham group received only PBS significant differences were observed. The OECs transplantation can improve myelin restoration. © 2015 Tehran University of Medical Sciences. All rights reserved

Multi-Context Dual Hyper-Prior Neural Image Compression

Transform and entropy models are the two core components in deep image compression neural networks. Most existing learning-based image compression methods utilize convolutional-based transform, which lacks the ability to model long-range dependencies, primarily due to the limited receptive field of the convolution operation. To address this limitation, we propose a Transformer-based nonlinear transform. This transform has the remarkable ability to efficiently capture both local and global information from the input image, leading to a more decorrelated latent representation. In addition, we introduce a novel entropy model that incorporates two different hyperpriors to model cross-channel and spatial dependencies of the latent representation. To further improve the entropy model, we add a global context that leverages distant relationships to predict the current latent more accurately. This global context employs a causal attention mechanism to extract long-range information in a content-dependent manner. Our experiments show that our proposed framework performs better than the state-of-the-art methods in terms of rate-distortion performance.Comment: Accepted to IEEE 22$^nd$ International Conference on Machine Learning and Applications 2023 (ICMLA) - Selected for Oral Presentatio

Association between Women Empowerment and Social Support in the Reproductive Decision-Making of the Women Referring to the Health Centers in Sari, Iran (2017)

Background: Empowerment of women is considered to be a critical developmental strategy. Objectives: Today, empowerment of women is not only a priority, but it also is an urgent need of women as a one of the most important populations considering their roles in the family and community. Social support and empowerment of women are regarded as an investment for future generations, which will result in sustainable development. The present study aimed to explore the association between the social support and empowerment of women with their reproductive decisions in the health centers in Sari, Iran. Methods: This descriptive-correlational study was conducted on 400 women referring to the health centers in Sari, Iran in 2017. The subjects who met the inclusion criteria were selected via multistage cluster sampling. Data were collected using a demographic and reproductive characteristics questionnaire, multidimensional scale of perceived social support, and the questionnaire of women empowerment and reproductive behavior. Data analysis was performed in SPSS version 16. Results: The subjects had a moderate level of empowerment in their reproductive decisions. On the other hand, favorable and poor empowerment levels were observed in the dimensions of cultural background and family planning, respectively. Furthermore, social support had a direct, significant correlation with the empowerment of women in reproductive decisions (P=0.001; r=0.34). Conclusion: According to the results, the empowerment and social support of women are imperative issues that require special attention and investment considering the key role of women in promoting community health

Frequency Disentangled Features in Neural Image Compression

The design of a neural image compression network is governed by how well the entropy model matches the true distribution of the latent code. Apart from the model capacity, this ability is indirectly under the effect of how close the relaxed quantization is to the actual hard quantization. Optimizing the parameters of a rate-distortion variational autoencoder (R-D VAE) is ruled by this approximated quantization scheme. In this paper, we propose a feature-level frequency disentanglement to help the relaxed scalar quantization achieve lower bit rates by guiding the high entropy latent features to include most of the low-frequency texture of the image. In addition, to strengthen the de-correlating power of the transformer-based analysis/synthesis transform, an augmented self-attention score calculation based on the Hadamard product is utilized during both encoding and decoding. Channel-wise autoregressive entropy modeling takes advantage of the proposed frequency separation as it inherently directs high-informational low-frequency channels to the first chunks and conditions the future chunks on it. The proposed network not only outperforms hand-engineered codecs, but also neural network-based codecs built on computation-heavy spatially autoregressive entropy models.Comment: Accepted to 30$^{th}$ IEEE International Conference on Image Processing (ICIP 2023

Context-Aware Neural Video Compression on Solar Dynamics Observatory

NASA's Solar Dynamics Observatory (SDO) mission collects large data volumes of the Sun's daily activity. Data compression is crucial for space missions to reduce data storage and video bandwidth requirements by eliminating redundancies in the data. In this paper, we present a novel neural Transformer-based video compression approach specifically designed for the SDO images. Our primary objective is to efficiently exploit the temporal and spatial redundancies inherent in solar images to obtain a high compression ratio. Our proposed architecture benefits from a novel Transformer block called Fused Local-aware Window (FLaWin), which incorporates window-based self-attention modules and an efficient fused local-aware feed-forward (FLaFF) network. This architectural design allows us to simultaneously capture short-range and long-range information while facilitating the extraction of rich and diverse contextual representations. Moreover, this design choice results in reduced computational complexity. Experimental results demonstrate the significant contribution of the FLaWin Transformer block to the compression performance, outperforming conventional hand-engineered video codecs such as H.264 and H.265 in terms of rate-distortion trade-off.Comment: Accepted to IEEE 22$^{nd}$ International Conference on Machine Learning and Applications 2023 (ICMLA) - Selected for Oral Presentatio