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Zainalabedin Samadi

Publications and source records attributed to Zainalabedin Samadi.

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Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal images

This study introduces SEFI (SEgmentation-Free Integration), a novel method for integrating morphological features of cell nuclei with spatial transcriptomics data. Cell segmentation poses a significant challenge in the analysis of spatial transcriptomics data, as tissue-specific structural complexities and densely packed cells in certain regions make it difficult to develop a universal approach. SEFI addresses this by utilizing self-supervised learning to extract morphological features from fluorescent nuclear staining images, enhancing the clustering of gene expression data without requiring segmentation. We demonstrate SEFI on spatially resolved gene expression profiles of the developing retina, acquired using multiplexed single molecule Fluorescence In Situ Hybridization (smFISH). SEFI is publicly available at https://github.com/eduardchelebian/sefi.

eess.IV

Strong Interference Alignment

Interference alignment (IA) adjusts signaling scheme such that all interfering signals are squeezed in interference subspace. IA mostly achieves its performance via infinite extension of the channel, which is a major challenge for IA in practical systems. In this paper, we make part of interference very strong and achieve perfect IA within limited number of channel extensions. A single-hop $3$ user single antenna interference channel (IFC) is considered and it is shown that only one of the interfering signal streams needs to be strong so that perfect IA is feasible.

cs.IT

Channel Aided Interference Alignment

Interference alignment (IA) techniques mostly attain their degrees of freedom (DoF) benefits as the number of channel extensions tends to infinity. Intuitively, the more interfering signals that need to be aligned, the larger the number of dimensions needed to align them. This requirement poses a major challenge for IA in practical systems. This work evaluates the necessary and sufficient conditions on channel structure of a fully connected interference network with time-varying fading to make perfect IA feasible within limited number of channel extensions. We propose a method based on the obtained conditions on the channel structure to achieve perfect IA. For the case of $3$ user interference channel, it is shown that only one condition on channel coefficients is required to make perfect IA feasible at all receivers. IA feasibility literature have mainly focused on network topology so far. In contrast, derived channel aiding conditions in this work can be considered as the perfect IA feasibility conditions on channel structure.

cs.IT

Perfect Interference Alignment for an Interference Network with General Message Demands

Dimensionality requirement poses a major challenge for Interference alignment (IA) in practical systems. This work evaluates the necessary and sufficient conditions on channel structure of a fully connected general interference network to make perfect IA feasible within limited number of channel extensions. So far, IA feasibility literature have mainly focused on network topology, in contrast, this work makes use of the channel structure to achieve total number of degrees of freedom (DoF) of the considered network by extending the channel aided IA scheme to the case of interference channel with general message demands. We consider a single-hop interference network with $K$ transmitters and $N$ receivers each equipped with a single antenna. Each transmitter emits an independent message and each receiver requests an arbitrary subset of the messages. Obtained channel aiding conditions can be considered as the optimal DoF feasibility conditions on channel structure. As a byproduct, assuming optimal DoF assignment, it is proved that in a general interference network, there is no user with a unique maximum number of DoF.

cs.IT

On feasibility of perfect interference alignment in interference networks

Interference alignment(IA) is mostly achieved by coding interference over multiple dimensions. Intuitively, the more interfering signals that need to be aligned, the larger the number of dimensions needed to align them. This dimensionality requirement poses a major challenge for IA in practical systems. This work evaluates the necessary and sufficient conditions on channel structure of a 3 user interference channel(IC) to make perfect IA feasible within limited number of channel extensions. It is shown that if only one of interfering channel coefficients can be designed to a specific value, interference would be aligned perfectly at all receivers.

cs.IT