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28 results on '"Soni, Mehul"'

1. EXPLORE -- Explainable Song Recommendation

Author

Arun, Abhinav, Soni, Mehul, Choudhary, Palash, and Arora, Saksham

Subjects
Computer Science - Information Retrieval
Abstract

This study explores the development of an explainable music recommendation system with enhanced user control. Leveraging a hybrid of collaborative filtering and content-based filtering, we address the challenges of opaque recommendation logic and lack of user influence on results. We present a novel approach combining advanced algorithms and an interactive user interface. Our methodology integrates Spotify data with user preference analytics to tailor music suggestions. Evaluation through RMSE and user studies underscores the efficacy and user satisfaction with our system. The paper concludes with potential directions for future enhancements in group recommendations and dynamic feedback integration., Comment: 6 pages, 7 figures

Published
2023

2. Towards a Unified Multimodal Reasoning Framework

Author

Arun, Abhinav, Mal, Dipendra Singh, Soni, Mehul, and Sawada, Tomohiro

Subjects
Computer Science - Computation and Language
Abstract

Recent advancements in deep learning have led to the development of powerful language models (LMs) that excel in various tasks. Despite these achievements, there is still room for improvement, particularly in enhancing reasoning abilities and incorporating multimodal data. This report investigates the potential impact of combining Chain-of-Thought (CoT) reasoning and Visual Question Answering (VQA) techniques to improve LM's accuracy in solving multiple-choice questions. By employing TextVQA and ScienceQA datasets, we assessed the effectiveness of three text embedding methods and three visual embedding approaches. Our experiments aimed to fill the gap in current research by investigating the combined impact of CoT and VQA, contributing to the understanding of how these techniques can improve the reasoning capabilities of state-of-the-art models like GPT-4. Results from our experiments demonstrated the potential of these approaches in enhancing LM's reasoning and question-answering capabilities, providing insights for further research and development in the field, and paving the way for more accurate and reliable AI systems that can handle complex reasoning tasks across multiple modalities., Comment: 6 pages, 11 figures

Published
2023

3. Numerical Reasoning for Financial Reports

Author

Arun, Abhinav, Dhiman, Ashish, Soni, Mehul, and Hu, Yibei

Subjects
Computer Science - Computation and Language
Abstract

Financial reports offer critical insights into a company's operations, yet their extensive length typically spanning 30 40 pages poses challenges for swift decision making in dynamic markets. To address this, we leveraged finetuned Large Language Models (LLMs) to distill key indicators and operational metrics from these reports basis questions from the user. We devised a method to locate critical data, and leverage the FinQA dataset to fine-tune both Llama-2 7B and T5 models for customized question answering. We achieved results comparable to baseline on the final numerical answer, a competitive accuracy in numerical reasoning and calculation., Comment: 10 pages, 11 figures, 6 tables

Published
2023

4. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Author

Bakas, Spyridon, Reyes, Mauricio, Jakab, Andras, Bauer, Stefan, Rempfler, Markus, Crimi, Alessandro, Shinohara, Russell Takeshi, Berger, Christoph, Ha, Sung Min, Rozycki, Martin, Prastawa, Marcel, Alberts, Esther, Lipkova, Jana, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan, Wiest, Roland, Kirschke, Jan, Wiestler, Benedikt, Colen, Rivka, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-Andre, Mahajan, Abhishek, Baid, Ujjwal, Gerstner, Elizabeth, Kwon, Dongjin, Acharya, Gagan, Agarwal, Manu, Alam, Mahbubul, Albiol, Alberto, Albiol, Antonio, Albiol, Francisco J., Alex, Varghese, Allinson, Nigel, Amorim, Pedro H. A., Amrutkar, Abhijit, Anand, Ganesh, Andermatt, Simon, Arbel, Tal, Arbelaez, Pablo, Avery, Aaron, Azmat, Muneeza, B., Pranjal, Bai, W, Banerjee, Subhashis, Barth, Bill, Batchelder, Thomas, Batmanghelich, Kayhan, Battistella, Enzo, Beers, Andrew, Belyaev, Mikhail, Bendszus, Martin, Benson, Eze, Bernal, Jose, Bharath, Halandur Nagaraja, Biros, George, Bisdas, Sotirios, Brown, James, Cabezas, Mariano, Cao, Shilei, Cardoso, Jorge M., Carver, Eric N, Casamitjana, Adrià, Castillo, Laura Silvana, Catà, Marcel, Cattin, Philippe, Cerigues, Albert, Chagas, Vinicius S., Chandra, Siddhartha, Chang, Yi-Ju, Chang, Shiyu, Chang, Ken, Chazalon, Joseph, Chen, Shengcong, Chen, Wei, Chen, Jefferson W, Chen, Zhaolin, Cheng, Kun, Choudhury, Ahana Roy, Chylla, Roger, Clérigues, Albert, Colleman, Steven, Colmeiro, Ramiro German Rodriguez, Combalia, Marc, Costa, Anthony, Cui, Xiaomeng, Dai, Zhenzhen, Dai, Lutao, Daza, Laura Alexandra, Deutsch, Eric, Ding, Changxing, Dong, Chao, Dong, Shidu, Dudzik, Wojciech, Eaton-Rosen, Zach, Egan, Gary, Escudero, Guilherme, Estienne, Théo, Everson, Richard, Fabrizio, Jonathan, Fan, Yong, Fang, Longwei, Feng, Xue, Ferrante, Enzo, Fidon, Lucas, Fischer, Martin, French, Andrew P., Fridman, Naomi, Fu, Huan, Fuentes, David, Gao, Yaozong, Gates, Evan, Gering, David, Gholami, Amir, Gierke, Willi, Glocker, Ben, Gong, Mingming, González-Villá, Sandra, Grosges, T., Guan, Yuanfang, Guo, Sheng, Gupta, Sudeep, Han, Woo-Sup, Han, Il Song, Harmuth, Konstantin, He, Huiguang, Hernández-Sabaté, Aura, Herrmann, Evelyn, Himthani, Naveen, Hsu, Winston, Hsu, Cheyu, Hu, Xiaojun, Hu, Xiaobin, Hu, Yan, Hu, Yifan, Hua, Rui, Huang, Teng-Yi, Huang, Weilin, Van Huffel, Sabine, Huo, Quan, HV, Vivek, Iftekharuddin, Khan M., Isensee, Fabian, Islam, Mobarakol, Jackson, Aaron S., Jambawalikar, Sachin R., Jesson, Andrew, Jian, Weijian, Jin, Peter, Jose, V Jeya Maria, Jungo, Alain, Kainz, B, Kamnitsas, Konstantinos, Kao, Po-Yu, Karnawat, Ayush, Kellermeier, Thomas, Kermi, Adel, Keutzer, Kurt, Khadir, Mohamed Tarek, Khened, Mahendra, Kickingereder, Philipp, Kim, Geena, King, Nik, Knapp, Haley, Knecht, Urspeter, Kohli, Lisa, Kong, Deren, Kong, Xiangmao, Koppers, Simon, Kori, Avinash, Krishnamurthi, Ganapathy, Krivov, Egor, Kumar, Piyush, Kushibar, Kaisar, Lachinov, Dmitrii, Lambrou, Tryphon, Lee, Joon, Lee, Chengen, Lee, Yuehchou, Lee, M, Lefkovits, Szidonia, Lefkovits, Laszlo, Levitt, James, Li, Tengfei, Li, Hongwei, Li, Wenqi, Li, Hongyang, Li, Xiaochuan, Li, Yuexiang, Li, Heng, Li, Zhenye, Li, Xiaoyu, Li, Zeju, Li, XiaoGang, Lin, Zheng-Shen, Lin, Fengming, Lio, Pietro, Liu, Chang, Liu, Boqiang, Liu, Xiang, Liu, Mingyuan, Liu, Ju, Liu, Luyan, Llado, Xavier, Lopez, Marc Moreno, Lorenzo, Pablo Ribalta, Lu, Zhentai, Luo, Lin, Luo, Zhigang, Ma, Jun, Ma, Kai, Mackie, Thomas, Madabushi, Anant, Mahmoudi, Issam, Maier-Hein, Klaus H., Maji, Pradipta, Mammen, CP, Mang, Andreas, Manjunath, B. S., Marcinkiewicz, Michal, McDonagh, S, McKenna, Stephen, McKinley, Richard, Mehl, Miriam, Mehta, Sachin, Mehta, Raghav, Meier, Raphael, Meinel, Christoph, Merhof, Dorit, Meyer, Craig, Miller, Robert, Mitra, Sushmita, Moiyadi, Aliasgar, Molina-Garcia, David, Monteiro, Miguel A. B., Mrukwa, Grzegorz, Myronenko, Andriy, Nalepa, Jakub, Ngo, Thuyen, Nie, Dong, Ning, Holly, Niu, Chen, Nuechterlein, Nicholas K, Oermann, Eric, Oliveira, Arlindo, Oliveira, Diego D. C., Oliver, Arnau, Osman, Alexander F. I., Ou, Yu-Nian, Ourselin, Sebastien, Paragios, Nikos, Park, Moo Sung, Paschke, Brad, Pauloski, J. Gregory, Pawar, Kamlesh, Pawlowski, Nick, Pei, Linmin, Peng, Suting, Pereira, Silvio M., Perez-Beteta, Julian, Perez-Garcia, Victor M., Pezold, Simon, Pham, Bao, Phophalia, Ashish, Piella, Gemma, Pillai, G. N., Piraud, Marie, Pisov, Maxim, Popli, Anmol, Pound, Michael P., Pourreza, Reza, Prasanna, Prateek, Prkovska, Vesna, Pridmore, Tony P., Puch, Santi, Puybareau, Élodie, Qian, Buyue, Qiao, Xu, Rajchl, Martin, Rane, Swapnil, Rebsamen, Michael, Ren, Hongliang, Ren, Xuhua, Revanuru, Karthik, Rezaei, Mina, Rippel, Oliver, Rivera, Luis Carlos, Robert, Charlotte, Rosen, Bruce, Rueckert, Daniel, Safwan, Mohammed, Salem, Mostafa, Salvi, Joaquim, Sanchez, Irina, Sánchez, Irina, Santos, Heitor M., Sartor, Emmett, Schellingerhout, Dawid, Scheufele, Klaudius, Scott, Matthew R., Scussel, Artur A., Sedlar, Sara, Serrano-Rubio, Juan Pablo, Shah, N. Jon, Shah, Nameetha, Shaikh, Mazhar, Shankar, B. Uma, Shboul, Zeina, Shen, Haipeng, Shen, Dinggang, Shen, Linlin, Shen, Haocheng, Shenoy, Varun, Shi, Feng, Shin, Hyung Eun, Shu, Hai, Sima, Diana, Sinclair, M, Smedby, Orjan, Snyder, James M., Soltaninejad, Mohammadreza, Song, Guidong, Soni, Mehul, Stawiaski, Jean, Subramanian, Shashank, Sun, Li, Sun, Roger, Sun, Jiawei, Sun, Kay, Sun, Yu, Sun, Guoxia, Sun, Shuang, Suter, Yannick R, Szilagyi, Laszlo, Talbar, Sanjay, Tao, Dacheng, Teng, Zhongzhao, Thakur, Siddhesh, Thakur, Meenakshi H, Tharakan, Sameer, Tiwari, Pallavi, Tochon, Guillaume, Tran, Tuan, Tsai, Yuhsiang M., Tseng, Kuan-Lun, Tuan, Tran Anh, Turlapov, Vadim, Tustison, Nicholas, Vakalopoulou, Maria, Valverde, Sergi, Vanguri, Rami, Vasiliev, Evgeny, Ventura, Jonathan, Vera, Luis, Vercauteren, Tom, Verrastro, C. A., Vidyaratne, Lasitha, Vilaplana, Veronica, Vivekanandan, Ajeet, Wang, Guotai, Wang, Qian, Wang, Chiatse J., Wang, Weichung, Wang, Duo, Wang, Ruixuan, Wang, Yuanyuan, Wang, Chunliang, Wen, Ning, Wen, Xin, Weninger, Leon, Wick, Wolfgang, Wu, Shaocheng, Wu, Qiang, Wu, Yihong, Xia, Yong, Xu, Yanwu, Xu, Xiaowen, Xu, Peiyuan, Yang, Tsai-Ling, Yang, Xiaoping, Yang, Hao-Yu, Yang, Junlin, Yang, Haojin, Yang, Guang, Yao, Hongdou, Ye, Xujiong, Yin, Changchang, Young-Moxon, Brett, Yu, Jinhua, Yue, Xiangyu, Zhang, Songtao, Zhang, Angela, Zhang, Kun, Zhang, Xuejie, Zhang, Lichi, Zhang, Xiaoyue, Zhang, Yazhuo, Zhang, Lei, Zhang, Jianguo, Zhang, Xiang, Zhang, Tianhao, Zhao, Sicheng, Zhao, Yu, Zhao, Xiaomei, Zhao, Liang, Zheng, Yefeng, Zhong, Liming, Zhou, Chenhong, Zhou, Xiaobing, Zhou, Fan, Zhu, Hongtu, Zhu, Jin, Zhuge, Ying, Zong, Weiwei, Kalpathy-Cramer, Jayashree, Farahani, Keyvan, Davatzikos, Christos, van Leemput, Koen, and Menze, Bjoern

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset., Comment: The International Multimodal Brain Tumor Segmentation (BraTS) Challenge

Published
2018

5. Ensemble of Fully Convolutional Neural Network for Brain Tumor Segmentation from Magnetic Resonance Images

Author

Kori, Avinash, Soni, Mehul, Pranjal, B., Khened, Mahendra, Alex, Varghese, Krishnamurthi, Ganapathy, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Crimi, Alessandro, editor, Bakas, Spyridon, editor, Kuijf, Hugo, editor, Keyvan, Farahani, editor, Reyes, Mauricio, editor, and van Walsum, Theo, editor

Published
2019
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20. Multi-Template based Homology Modeling of specificity protein 3

Author

Soni Mehul N, S, Kumar Prasanth, Shetty Shilpa S, Kaid Johar SR., and Pandya Himanshu A

Subjects
Specificity Protein3 (SP3), Modeller, Multi-template Homology Modeling
Abstract

SP3 Transcription factor contains 81,925 Dalton mass, which member of the Kruppel like zinc finger protein family that is clinically relevant for many neuronal transmission diseases. Considering the functional importance and lack of X-ray crystal structure of SP3 TFs protein, present work was undertaken to build the3D structure of aprotein using homology modeling with a multi-template approach. This present study, we chose three different SP3 templates (PDB ID: 3EBT, 4M9E, and 2WBS) were used for homology modeling. Five models were developedwith the help of multiple sequence alignment respect to templates using Modeller 8.0.0 software. All models were refined and ranked as per their overall DOPE-score. The top-ranked predicted model of SP3 TFs had 93.8% of residues in favored regions as revealed by Ramachandran plot and the ERRAT score was 100% which indicated an accurate model. The results of the homology modeling study and the proposed model can be further used for understanding the structural and functional characteristics of SP3 and to gain more insights to the molecular basis of SP3 inhibition through docking and molecular dynamics simulation studies.

Published
2018
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23. Cardiotonic steroids as potential Na+/K+-ATPase inhibitors – a computational study.

Author

Patel, Chirag N., Kumar, Sivakumar Prasanth, Modi, Krunal M., Soni, Mehul N., Modi, Nainesh R., and Pandya, Himanshu A.

Abstract

Cardiotonic steroids (CTS) are steroidal drugs, processed from the seeds and dried leaves of the genus Digitalis as well as from the skin and parotid gland of amphibians. The most commonly known CTS are ouabain, digoxin, digoxigenin and bufalin. CTS can be used for safer medication of congestive heart failure and other related conditions due to promising pharmacological and medicinal properties. Ouabain isolated from plants is widely utilized in in vitro studies to specifically block the sodium potassium (Na+/K+-ATPase) pump. For checking, whether ouabain derivatives are robust inhibitors of Na+/K+-ATPase pump, molecular docking simulation was performed between ouabain and its derivatives using YASARA software. The docking energy falls within the range of 8.470 kcal/mol to 7.234 kcal/mol, in which digoxigenin was found to be the potential ligand with the best docking energy of 8.470 kcal/mol. Furthermore, pharmacophore modeling was applied to decipher the electronic features of CTS. Molecular dynamics simulation was also employed to determine the conformational properties of Na+/K+-ATPase-ouabain and Na+/K+-ATPase-digoxigenin complexes with the plausible structural integrity through conformational ensembles for 100 ns which promoted digoxigenin as the most promising CTS for treating conditions of congestive heart failure patients. [ABSTRACT FROM AUTHOR]

Published
2019
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26. Investigating the Inhibitory Effects of Fats, Oils and Grease Addition on Fatty Acids Degradation During Anaerobic Co-Digestion

Author

Soni, Mehul Narendra

Subjects
Anaerobic co-digestion, fats, oils and grease (FOG), fatty acids, inhibition, intermediates, sludge
Abstract

Most of the wastewater treatment plants are being upgraded to serve as water resource recovery facilities, reducing and in some cases eliminating, the dependence on the electricity grid for energy requirements remains one of the preeminent targets. Methane yields from conventional anaerobic digestion systems are not often sufficient to fulfil the requirements of these modern-day resource recovery facilities. Although there are numerous articles on co-digestion of municipal sludge with fats, oils and grease (FOG) waste reporting noticeable increase in biogas production, there are enough reports of inhibition and digester upset to warrant further study. Our present study is focused on characterizing formation and consumption of intermediates at various stages occurring in biochemical methane potential tests. This information is crucial in understanding the nature and the mechanism of inhibition. Preliminary studies were conducted to find a sludge to FOG ratio which enabled us to produce maximum possible biogas and use that ratio for setting up bench-scale digesters for further investigations. Among all the compositions tested, FOG 25 was the only reactor to overcome inhibition after lag phase of 45 days. We also found that FOG-acclimated culture improved the performance of the digester by increasing cumulative methane production by 29.9%, during batch operation. When operated in semi-continuous mode, methane production rate per g-VS added in FOG 25 was 7.5% greater than control and %COD conversion to methane increased by 30.34%. For samples with FOG content higher than 25%, methanogenesis was inhibited, with very little methane produced. These results suggest possible recovery of digesters inhibited after a certain lag phase and contradicts earlier hypothesis of LCFA inhibition to be irreversible. Results obtained from the VFA and LCFA analysis suggest that accumulation of palmitate and stearate at high concentrations can be inhibitory to the methanogens as well as the microbes degrading VFAs and LCFAs. Additionally, absence of fatty acids with carbon chains of 14>n(C)>6 could be indicative of degradation mechanisms other than β-oxidation.

Published
2018

27. Cardiotonic steroids as potential Na + /K + -ATPase inhibitors - a computational study.

Author

Patel CN, Kumar SP, Modi KM, Soni MN, Modi NR, and Pandya HA

Subjects
Diffusion, Digoxin chemistry, Digoxin pharmacology, Hydrogen Bonding, Ligands, Models, Biological, Ouabain chemistry, Ouabain pharmacology, Quantitative Structure-Activity Relationship, Reproducibility of Results, Sodium-Potassium-Exchanging ATPase metabolism, Cardiac Glycosides pharmacology, Molecular Docking Simulation, Sodium-Potassium-Exchanging ATPase antagonists & inhibitors
Abstract

Cardiotonic steroids (CTS) are steroidal drugs, processed from the seeds and dried leaves of the genus Digitalis as well as from the skin and parotid gland of amphibians. The most commonly known CTS are ouabain, digoxin, digoxigenin and bufalin. CTS can be used for safer medication of congestive heart failure and other related conditions due to promising pharmacological and medicinal properties. Ouabain isolated from plants is widely utilized in in vitro studies to specifically block the sodium potassium (Na + /K + -ATPase) pump. For checking, whether ouabain derivatives are robust inhibitors of Na + /K + -ATPase pump, molecular docking simulation was performed between ouabain and its derivatives using YASARA software. The docking energy falls within the range of 8.470 kcal/mol to 7.234 kcal/mol, in which digoxigenin was found to be the potential ligand with the best docking energy of 8.470 kcal/mol. Furthermore, pharmacophore modeling was applied to decipher the electronic features of CTS. Molecular dynamics simulation was also employed to determine the conformational properties of Na + /K + -ATPase-ouabain and Na + /K + -ATPase-digoxigenin complexes with the plausible structural integrity through conformational ensembles for 100 ns which promoted digoxigenin as the most promising CTS for treating conditions of congestive heart failure patients.

Published
2019
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28. Quality of life and symptom measures in oncology: an overview.

Author

Soni MK and Cella D

Subjects
Cost of Illness, Humans, Neoplasms psychology, United States, Medical Oncology standards, Neoplasms physiopathology, Quality of Life, Sickness Impact Profile
Abstract

Improving quality of life (QOL) in oncology patients is an important therapeutic goal, and most treatment decisions are heavily influenced by their effect on QOL. Although measuring QOL has been a significant challenge because of a lack of consensus on the definition of QOL, research in this field has advanced rapidly. Numerous instruments now exist for measuring QOL and symptom burden, ranging from general health status measures to considerably more focused symptom measures. QOL measures have been routinely incorporated in clinical trials, and their use in clinical settings is strongly encouraged because their value in cancer patient management is now established. These measures also have a potential impact in the managed care environment because they provide information on patient satisfaction and quality of care provided. This article clarifies the definition of QOL, provides a brief overview of several useful measurement instruments, and addresses some common concerns encountered in measuring QOL in cancer patients. In addition, potential uses of such measures are explored and their value in various settings, including managed care, is highlighted.

Published
2002

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