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3. Enhancing Channel Estimation in Quantized Systems with a Generative Prior

Author

Fesl, Benedikt, Banna, Aziz, and Utschick, Wolfgang

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Information Theory, Computer Science - Machine Learning
Abstract

Channel estimation in quantized systems is challenging, particularly in low-resolution systems. In this work, we propose to leverage a Gaussian mixture model (GMM) as generative prior, capturing the channel distribution of the propagation environment, to enhance a classical estimation technique based on the expectation-maximization (EM) algorithm for one-bit quantization. Thereby, a maximum a posteriori (MAP) estimate of the most responsible mixture component is inferred for a quantized received signal, which is subsequently utilized in the EM algorithm as side information. Numerical results demonstrate the significant performance improvement of our proposed approach over both a simplistic Gaussian prior and current state-of-the-art channel estimators. Furthermore, the proposed estimation framework exhibits adaptability to higher resolution systems and alternative generative priors.

Published
2024

19. How did the use of the social marketing approach in Egyptian communities succeed in improving breastfeeding practices and infants’ growth?

Author

Metwally, Ammal M., Basha, Walaa A., Elshaarawy, Ghada A., Sallam, Sara F., El-Alameey, Inas R., Rifay, Amira S. El, Yousef, Walaa, Goda, Amira A., Elashry, Galal A., Ahmed, Doaa E., Hassan, Nayera E., El-Masry, Sahar A., Ibrahim, Nihad A., Dayem, Soha M. Abd El, Kandeel, Wafaa A., El-Din, Ebtissam M. Salah, Banna, Rokia Abd Elshafy S. El, Kamel, Iman H., Abdelhamid, Enas M., Abdelrahman, Mohamed, and Mahmoud, Walaa S.

Published
2024
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25. Instrumental activities of daily living in older patients with metastatic prostate cancer: results from the meet-URO network ADHERE prospective study

Author

Fratino, Lucia, Polesel, Jerry, Giunta, Emilio Francesco, Maruzzo, Marco, Buti, Sebastiano, Hassan, Mona Ali, Basso, Umberto, Rebuzzi, Sara Elena, De Giorgi, Ugo, Cinausero, Marika, Lipari, Helga, Gamba, Teresa, Bimbatti, Davide, Dri, Arianna, Ermacora, Paola, Vignani, Francesca, Fornarini, Giuseppe, Rescigno, Pasquale, and Banna, Giuseppe Luigi

Published
2024
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29. The Distribution of Polynomials in Monotone Independent Elements

Author

Banna, Marwa and Tseng, Pei-Lun

Subjects
Mathematics - Probability, 46L53, 60B20
Abstract

Building on the work of Arizmendi and Celestino (2021), we derive the $*$-distributions of polynomials in monotone independent and infinitesimally monotone independent elements. For non-zero complex numbers $\alpha$ and $\beta$, we derive explicitly the $*$-distribution of $p_{\alpha,\beta}=\alpha ab + \beta ba$ whenever $a$ and $b$ are monotone or infinitesimally monotone independent elements. This encompasses both cases of the commutator and anti-commutator. This approach can be pushed to study more general polynomials. As applications, we derive the limiting distribution with respect to the partial trace of polynomials in a certain class of random matrices., Comment: 18 pages

Published
2023

38. Long-term responders to nivolumab in previously treated advanced renal cell carcinoma: a sub-analysis of meet-URO15 study

Author

Messina, Carlo, Catalano, Martina, Roviello, Giandomenico, Gandini, Annalice, Maruzzo, Marco, De Giorgi, Ugo, Pedrazzoli, Paolo, Sbrana, Andrea, Zucal, Paolo Andrea, Masini, Cristina, Naglieri, Emanuele, Procopio, Giuseppe, Milella, Michele, Catalano, Fabio, Fratino, Lucia, Pipitone, Stefania, Ricotta, Riccardo, Panni, Stefano, Mollica, Veronica, Soraru, Mariella, Prati, Veronica, Atzori, Francesco, Di Napoli, Marilena, Messina, Marco, Morelli, Franco, Prati, Giuseppe, Nole, Franco, Malgeri, Andrea, Tudini, Marianna, Vignani, Francesca, Cavo, Alessia, Signori, Alessio, Banna, Giuseppe Luigi, Rescigno, Pasquale, Buti, Sebastiano, Rebuzzi, Sara Elena, and Fornarini, Giuseppe

Published
2024
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39. Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision

Author

Jiang, Wenxin, Banna, Vishnu, Vivek, Naveen, Goel, Abhinav, Synovic, Nicholas, Thiruvathukal, George K., and Davis, James C.

Subjects
Computer Science - Software Engineering, Computer Science - Artificial Intelligence
Abstract

Many engineering organizations are reimplementing and extending deep neural networks from the research community. We describe this process as deep learning model reengineering. Deep learning model reengineering - reusing, reproducing, adapting, and enhancing state-of-the-art deep learning approaches - is challenging for reasons including under-documented reference models, changing requirements, and the cost of implementation and testing. In addition, individual engineers may lack expertise in software engineering, yet teams must apply knowledge of software engineering and deep learning to succeed. Prior work has examined on DL systems from a "product" view, examining defects from projects regardless of the engineers' purpose. Our study is focused on reengineering activities from a "process" view, and focuses on engineers specifically engaged in the reengineering process. Our goal is to understand the characteristics and challenges of deep learning model reengineering. We conducted a case study of this phenomenon, focusing on the context of computer vision. Our results draw from two data sources: defects reported in open-source reeengineering projects, and interviews conducted with open-source project contributors and the leaders of a reengineering team. Our results describe how deep learning-based computer vision techniques are reengineered, analyze the distribution of defects in this process, and discuss challenges and practices. Integrating our quantitative and qualitative data, we proposed a novel reengineering workflow. Our findings inform several future directions, including: measuring additional unknown aspects of model reengineering; standardizing engineering practices to facilitate reengineering; and developing tools to support model reengineering and model reuse., Comment: Under submission to EMSE

Published
2023

40. Unsupervised physics-informed neural network in reaction-diffusion biology models (Ulcerative colitis and Crohn's disease cases) A preliminary study

Author

Rebai, Ahmed, Boukhris, Louay, Toujani, Radhi, Gueddiche, Ahmed, Banna, Fayad Ali, Souissi, Fares, Lasram, Ahmed, Rayana, Elyes Ben, and Zaag, Hatem

Subjects
Computer Science - Machine Learning, Mathematics - Analysis of PDEs, Quantitative Biology - Quantitative Methods
Abstract

We propose to explore the potential of physics-informed neural networks (PINNs) in solving a class of partial differential equations (PDEs) used to model the propagation of chronic inflammatory bowel diseases, such as Crohn's disease and ulcerative colitis. An unsupervised approach was privileged during the deep neural network training. Given the complexity of the underlying biological system, characterized by intricate feedback loops and limited availability of high-quality data, the aim of this study is to explore the potential of PINNs in solving PDEs. In addition to providing this exploratory assessment, we also aim to emphasize the principles of reproducibility and transparency in our approach, with a specific focus on ensuring the robustness and generalizability through the use of artificial intelligence. We will quantify the relevance of the PINN method with several linear and non-linear PDEs in relation to biology. However, it is important to note that the final solution is dependent on the initial conditions, chosen boundary conditions, and neural network architectures.

Published
2023

41. Refugee Women’s Receptiveness for Virtual Engagement on Reproductive Health During the COVID-19 Pandemic

Author

Thiel de Bocanegra, Heike, Goliaei, Zahra, Khan, Nossin, Banna, Sereen, Behnam, Rawnaq, and Mody, Sheila K

Subjects
Health Services and Systems, Health Sciences, Basic Behavioral and Social Science, Behavioral and Social Science, Contraception/Reproduction, Health Services, Prevention, Clinical Research, Management of diseases and conditions, 7.1 Individual care needs, Reproductive health and childbirth, Good Health and Well Being, Female, Humans, Reproductive Health, Refugees, Early Detection of Cancer, Pandemics, COVID-19, Uterine Cervical Neoplasms, Qualitative study, Telehealth, Reproductive health, Refugee women, Public Health and Health Services, Psychology, Public Health, Curriculum and pedagogy, Public health, Biological psychology
Abstract

BackgroundRefugee women who leave their country due to persecution and violence have multiple barriers to sexual and reproductive health (SRH) services. The COVID-19 pandemic added an additional barrier to in-person reproductive health education, dialogue, and clinical care. This study aimed to assess the potential of using virtual group meetings as a forum for refugee women to learn about and discuss reproductive health concerns such as cervical cancer screening, family planning, childbirth, and postpartum care.MethodWe conducted semi-structured interviews with 36 refugee women and stakeholders to assess factors that impact refugee women's receptiveness for virtual platforms to obtain information and engage in discussions on reproductive health. Thematic analysis was conducted using the software Dedoose.ResultsOpenness to engage in virtual platforms varied by refugee community, women's demographic, and life experience. The women's involvement with local refugee groups facilitated their engagement with virtual platforms. Furthermore, individuals' family structure and marital relationship, along with literacy and English proficiency, and access to and familiarity with technology impacted engagement. Virtual groups needed to mirror confidentiality and women expressed a strong preference that groups were all-women.ConclusionRefugee women are receptive to virtual groups on SRH when they are offered in a culturally appropriate manner that considers the living situations and access to technology after arrival to the USA. Findings from this study provide a framework to develop and tailor effective virtual or hybrid virtual-in-person programs for women in refugee communities.

Published
2023

42. External validation of a red cell-based blood prognostic score in patients with metastatic renal cell carcinoma treated with first-line immunotherapy combinations

Author

Maffezzoli, Michele, Santoni, Matteo, Mazzaschi, Giulia, Rodella, Sara, Lai, Eleonora, Maruzzo, Marco, Basso, Umberto, Bimbatti, Davide, Iacovelli, Roberto, Anghelone, Annunziato, Fiala, Ondřej, Rebuzzi, Sara Elena, Fornarini, Giuseppe, Lolli, Cristian, Massari, Francesco, Rosellini, Matteo, Mollica, Veronica, Nasso, Cecilia, Acunzo, Alessandro, Silini, Enrico Maria, Quaini, Federico, De Filippo, Massimo, Brunelli, Matteo, Banna, Giuseppe L., Rescigno, Pasquale, Signori, Alessio, and Buti, Sebastiano

Published
2024
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45. Quantitative Estimates for Operator-Valued and Infinitesimal Boolean and Monotone Limit Theorems

Author

Arizmendi, Octavio, Banna, Marwa, and Tseng, Pei-Lun

Subjects
Mathematics - Probability, Mathematics - Operator Algebras
Abstract

We provide Berry-Esseen bounds for sums of operator-valued Boolean and monotone independent variables, in terms of the first moments of the summands. Our bounds are on the level of Cauchy transforms as well as the L\'evy distance. As applications, we obtain quantitative bounds for the corresponding CLTs, provide a quantitative "fourth moment theorem" for monotone independent random variables including the operator-valued case, and generalize the results by Hao and Popa on matrices with Boolean entries. Our approach relies on a Lindeberg method that we develop for sums of Boolean/monotone independent random variables. Furthermore, we push this approach to the infinitesimal setting to obtain the first quantitative estimates for the operator-valued infinitesimal free, Boolean and monotone CLT., Comment: 46 pages

Published
2022

46. Self-Reported Severity and Causes of Traumatic Brain Injury in Patients With Epileptic or Functional Seizures

Author

Hickman, L Brian, Patel, Akash B, Dubey, Ishita, Karimi, Amir H, Zhang, Xingruo, Janio, Emily A, Allas, Corinne H, Sreenivasan, Siddhanka S, Bauirjan, Janar, D'Ambrosio, Shannon, Al Banna, Mona, Cho, Andrew Y, Engel, Jerome, Stern, John M, and Kerr, Wesley T

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Traumatic Brain Injury (TBI), Neurosciences, Brain Disorders, Neurodegenerative, Epilepsy, Clinical Research, Physical Injury - Accidents and Adverse Effects, Traumatic Head and Spine Injury, Neurological
Abstract

Background and objectivesAlthough moderate and severe traumatic brain injury (TBI) can cause posttraumatic epilepsy (PTE), many patients with functional seizures (FS) also report a history of mild TBI. To determine whether features of TBI history differ between patients with epileptic seizures (ES) and FS, we compared patient reports of TBI severity, symptoms, and causes of injury.MethodsWe recruited patients undergoing video-EEG evaluation for the diagnosis of ES, FS, mixed ES and FS, or physiologic seizure-like events at an academic, tertiary referral center. Patients and their caregivers were interviewed before final video-EEG diagnosis regarding their TBI histories, including concussive symptoms and causes of injury.ResultsOf 506 patients, a greater percentage of patients with FS reported a history of TBI than patients with ES (70% vs 59%, aOR = 1.75 [95% CI: 1.00-3.05], p = 0.047). TBI with loss of consciousness (LOC) lasting less than 30 minutes was more frequently reported among patients with FS than with ES (27% vs 13%, aOR = 2.38 [1.26-4.47], p < 0.01). The proportion of patients reporting other neurologic symptoms immediately after TBI was not significantly different between FS and ES (40% vs 29%, p = 0.08). Causes of TBI were found to differ, with TBIs caused by falls from a height (17% vs 10%, aOR = 2.24 [1.06-4.70], p = 0.03) or motor vehicle collisions (27% vs 11%, aOR = 2.96 [1.54-5.67], p < 0.01) reported more frequently in FS than ES.DiscussionOur findings further the association of mild TBI with FS and prompt reconsideration of typical assumptions regarding the significance of a reported TBI history in patients with previously undifferentiated seizures. Although common in both groups, TBI with LOC less than 30 minutes and causes of injury that are commonly believed to be more severe were reported more frequently in FS than ES. This suggests that a patient or caregiver reporting of these features does not imply that PTE is a more probable diagnosis than FS. Although a history of TBI with LOC and presumed high-risk causes of injury intuitively raises suspicion for PTE, clinicians should be cautioned that these historical factors also were a frequent finding in patients with FS.

Published
2022

50. A Novel to Method to Measure Food Waste: The Mobile Food Record

Author

Danible, Kaitlin, Panizza, Chloe, Boushey, Carol, Kerr, Deborah, Zhu, Fengqing, and Banna, Jinan C.

Abstract

The mobile food record™ (mFR™) is a novel app that allows for the tracking of individual food waste and addresses the limitations of current methods to accurately quantify food waste. Extension educators may use data from the mFR™ to create educational initiatives for food waste prevention and education that may be implemented in settings such as schools and universities. The mFR is an innovative application that simplifies the process of collecting food-based data and accurately quantifying food waste for use in Extension.

Published
2021

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