Tootfinder

Generative Software Engineering
Yuan Huang, Yinan Chen, Xiangping Chen, Junqi Chen, Rui Peng, Zhicao Tang, Jinbo Huang, Furen Xu, Zibin Zheng
https://arxiv.org/abs/2403.02583

Generative Software Engineering
The rapid development of deep learning techniques, improved computational power, and the availability of vast training data have led to significant advancements in pre-trained models and large language models (LLMs). Pre-trained models based on architectures such as BERT and Transformer, as well as LLMs like ChatGPT, have demonstrated remarkable language capabilities and found applications in Software engineering. Software engineering tasks can be divided into many categories, among which gener…

Discovering robust biomarkers of neurological disorders from functional MRI using graph neural networks: A Review
Yi Hao Chan, Deepank Girish, Sukrit Gupta, Jing Xia, Chockalingam Kasi, Yinan He, Conghao Wang, Jagath C. Rajapakse
https://arxiv.org/abs/2405.00577

Discovering robust biomarkers of neurological disorders from functional MRI using graph neural networks: A Review
Graph neural networks (GNN) have emerged as a popular tool for modelling functional magnetic resonance imaging (fMRI) datasets. Many recent studies have reported significant improvements in disorder classification performance via more sophisticated GNN designs and highlighted salient features that could be potential biomarkers of the disorder. In this review, we provide an overview of how GNN and model explainability techniques have been applied on fMRI datasets for disorder prediction tasks, w…

Precoding for Multi-Cell ISAC: from Coordinated Beamforming to Coordinated Multipoint and Bi-Static Sensing
Nithin Babu, Christos Masouros, Constantinos B. Papadias, Yonina C. Eldar
https://arxiv.org/abs/2402.18387

Precoding for Multi-Cell ISAC: from Coordinated Beamforming to Coordinated Multipoint and Bi-Static Sensing
This paper proposes a framework for designing robust precoders for a multi-input single-output (MISO) system that performs integrated sensing and communication (ISAC) across multiple cells and users. We use Cramer-Rao-Bound (CRB) to measure the sensing performance and derive its expressions for two multi-cell scenarios, namely coordinated beamforming (CBF) and coordinated multi-point (CoMP). In the CBF scheme, a BS shares channel state information (CSI) and estimates target parameters using mon…

Discovering robust biomarkers of neurological disorders from functional MRI using graph neural networks: A Review
Yi Hao Chan, Deepank Girish, Sukrit Gupta, Jing Xia, Chockalingam Kasi, Yinan He, Conghao Wang, Jagath C. Rajapakse
https://arxiv.org/abs/2405.00577

Discovering robust biomarkers of neurological disorders from functional MRI using graph neural networks: A Review
Graph neural networks (GNN) have emerged as a popular tool for modelling functional magnetic resonance imaging (fMRI) datasets. Many recent studies have reported significant improvements in disorder classification performance via more sophisticated GNN designs and highlighted salient features that could be potential biomarkers of the disorder. In this review, we provide an overview of how GNN and model explainability techniques have been applied on fMRI datasets for disorder prediction tasks, w…

Collaborative Interactive Evolution of Art in the Latent Space of Deep Generative Models
Ole Hall, Anil Yaman
https://arxiv.org/abs/2403.19620 https://

Collaborative Interactive Evolution of Art in the Latent Space of Deep Generative Models
Generative Adversarial Networks (GANs) have shown great success in generating high quality images and are thus used as one of the main approaches to generate art images. However, usually the image generation process involves sampling from the latent space of the learned art representations, allowing little control over the output. In this work, we first employ GANs that are trained to produce creative images using an architecture known as Creative Adversarial Networks (CANs), then, we employ an…

Deep, convergent, unrolled half-quadratic splitting for image deconvolution
Yanan Zhao, Yuelong Li, Haichuan Zhang, Vishal Monga, Yonina C. Eldar
https://arxiv.org/abs/2402.12872 …

Deep, convergent, unrolled half-quadratic splitting for image deconvolution
In recent years, algorithm unrolling has emerged as a powerful technique for designing interpretable neural networks based on iterative algorithms. Imaging inverse problems have particularly benefited from unrolling-based deep network design since many traditional model-based approaches rely on iterative optimization. Despite exciting progress, typical unrolling approaches heuristically design layer-specific convolution weights to improve performance. Crucially, convergence properties of the un…

UniSAR: Modeling User Transition Behaviors between Search and Recommendation
Teng Shi, Zihua Si, Jun Xu, Xiao Zhang, Xiaoxue Zang, Kai Zheng, Dewei Leng, Yanan Niu, Yang Song
https://arxiv.org/abs/2404.09520

UniSAR: Modeling User Transition Behaviors between Search and Recommendation
Nowadays, many platforms provide users with both search and recommendation services as important tools for accessing information. The phenomenon has led to a correlation between user search and recommendation behaviors, providing an opportunity to model user interests in a fine-grained way. Existing approaches either model user search and recommendation behaviors separately or overlook the different transitions between user search and recommendation behaviors. In this paper, we propose a framew…

Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution Microscopy
Yair Ben Sahel, Yonina C. Eldar
https://arxiv.org/abs/2403.16974 https:/…

Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution Microscopy
The use of fluorescent molecules to create long sequences of low-density, diffraction-limited images enables highly-precise molecule localization. However, this methodology requires lengthy imaging times, which limits the ability to view dynamic interactions of live cells on short time scales. Many techniques have been developed to reduce the number of frames needed for localization, from classic iterative optimization to deep neural networks. Particularly, deep algorithm unrolling utilizes bot…