70,807 results on '"Raghavan AS"'
Search Results
202. Nucleus segmentation from the histopathological images of liver cancer through an efficient deep learning framework
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Sunesh, Tripathi, Jyoti, Saini, Anu, Tiwari, Sunita, Kumari, Sunita, Taqui, Syed Noeman, Almoallim, Hesham S., Alharbi, Sulaiman Ali, and Raghavan, S. S.
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- 2024
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203. Multiphysics modeling of wire-to-plate electrohydrodynamic drying with air crossflow
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Oishi, Tamires K., Pouzada, Eduardo V. S., Gut, Jorge A. W., and Raghavan, Vijaya
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- 2024
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204. SC.HAWQS: A User-Friendly Web-Based Decision Support System for Regional Water Resources Management Under a Changing Climate
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Su, Qiong, Srinivasan, Raghavan, and Karthikeyan, R.
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- 2024
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205. Monitoring and Identification of Various Glucose Levels of Diabetes Patients Using Edge Based Machine Learning Approach
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Maheshwari, A., Hemalatha, B., Lakshmi, G., Kavitha, A., Tata, Ravi Kumar, Taqui, Syed Noeman, Al Obaid, Sami, Alharbi, Sulaiman Ali, and Raghavan, S. S.
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- 2024
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206. The loach genus Lepidocephalichthys (Teleostei: Cobitidae) in Sri Lanka and peninsular India: multiple colonizations and unexpected species diversity
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Sudasinghe, Hiranya, Dahanukar, Neelesh, Raghavan, Rajeev, Ranasinghe, Tharindu, Wijesooriya, Kumudu, Pethiyagoda, Rohan, Rüber, Lukas, and Meegaskumbura, Madhava
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- 2024
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207. Heterogeneous donor circles for fair liver transplant allocation
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Akshat, Shubham, Gentry, Sommer E., and Raghavan, S.
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- 2024
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208. Physicochemical feature enhancement of bioethanol through the fermentative parameter optimization
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Khodabakhshikoulaei, Azar, Sadrnia, Hassan, Tabasizadeh, Mohammad, Zarein, Mohammad, Mahfeli, Mandana, and Raghavan, Vijaya
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- 2024
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209. Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding
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Glenn, Parker, Dakle, Parag Pravin, and Raghavan, Preethi
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Computer Science - Computation and Language - Abstract
In addressing the task of converting natural language to SQL queries, there are several semantic and syntactic challenges. It becomes increasingly important to understand and remedy the points of failure as the performance of semantic parsing systems improve. We explore semantic parse correction with natural language feedback, proposing a new solution built on the success of autoregressive decoders in text-to-SQL tasks. By separating the semantic and syntactic difficulties of the task, we show that the accuracy of text-to-SQL parsers can be boosted by up to 26% with only one turn of correction with natural language. Additionally, we show that a T5-base model is capable of correcting the errors of a T5-large model in a zero-shot, cross-parser setting., Comment: ACL 2023 Workshop on NLP for Conversational AI
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- 2023
210. Polarization Independent Grating in GaN-on-Sapphire Photonic Integrated Circuit
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Suraj, Rathkanthiwar, Shashwat, Raghavan, Srinivasan, and Selvaraja, Shankar Kumar
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Physics - Optics ,Physics - Applied Physics - Abstract
In this work, we report the realization of a polarization-insensitive grating coupler, single-mode waveguide, and ring resonator in the GaN-on-Sapphire platform. We provide a detailed demonstration of the material characterization, device simulation, and experimental results. We achieve a grating coupler efficiency of -5.2 dB/coupler with a 1dB and 3dB bandwidth of 40 nm and 80 nm, respectively. We measure a single-mode waveguide loss of -6 dB/cm. The losses measured here are the lowest in a GaN-on-Sapphire photonic circuit. This demonstration provides opportunities for the development of on-chip linear and non-linear optical processes using the GaN-on-Sapphire platform. To the best of our knowledge, this is the first demonstration of an integrated photonic device using a GaN HEMT stack with 2D electron gas., Comment: 12 pages, 12 figures
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- 2023
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211. SF-SFD: Stochastic Optimization of Fourier Coefficients to Generate Space-Filling Designs
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Garg, Manisha, Chang, Tyler, and Raghavan, Krishnan
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Statistics - Methodology ,Mathematics - Optimization and Control ,Statistics - Applications - Abstract
Due to the curse of dimensionality, it is often prohibitively expensive to generate deterministic space-filling designs. On the other hand, when using na{\"i}ve uniform random sampling to generate designs cheaply, design points tend to concentrate in a small region of the design space. Although, it is preferable in these cases to utilize quasi-random techniques such as Sobol sequences and Latin hypercube designs over uniform random sampling in many settings, these methods have their own caveats especially in high-dimensional spaces. In this paper, we propose a technique that addresses the fundamental issue of measure concentration by updating high-dimensional distribution functions to produce better space-filling designs. Then, we show that our technique can outperform Latin hypercube sampling and Sobol sequences by the discrepancy metric while generating moderately-sized space-filling samples for high-dimensional problems.
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- 2023
212. Learning Continually on a Sequence of Graphs -- The Dynamical System Way
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Raghavan, Krishnan and Balaprakash, Prasanna
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Mathematics - Optimization and Control - Abstract
Continual learning~(CL) is a field concerned with learning a series of inter-related task with the tasks typically defined in the sense of either regression or classification. In recent years, CL has been studied extensively when these tasks are defined using Euclidean data -- data, such as images, that can be described by a set of vectors in an n-dimensional real space. However, the literature is quite sparse, when the data corresponding to a CL task is nonEuclidean -- data , such as graphs, point clouds or manifold, where the notion of similarity in the sense of Euclidean metric does not hold. For instance, a graph is described by a tuple of vertices and edges and similarities between two graphs is not well defined through a Euclidean metric. Due to this fundamental nature of the data, developing CL for nonEuclidean data presents several theoretical and methodological challenges. In particular, CL for graphs requires explicit modelling of nonstationary behavior of vertices and edges and their effects on the learning problem. Therefore, in this work, we develop a adaptive dynamic programming viewpoint for CL with graphs. In this work, we formulate a two-player sequential game between the act of learning new tasks~(generalization) and remembering previously learned tasks~(forgetting). We prove mathematically the existence of a solution to the game and demonstrate convergence to the solution of the game. Finally, we demonstrate the efficacy of our method on a number of graph benchmarks with a comprehensive ablation study while establishing state-of-the-art performance.
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- 2023
213. Saturation for Flagged Skew Littlewood-Richardson Coefficients
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Kundu, Siddheswar, Raghavan, K. N., Kumar, V. Sathish, and Viswanath, Sankaran
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Mathematics - Representation Theory ,Mathematics - Combinatorics ,05E05 (05E16) - Abstract
We define and study a generalization of the Littlewood-Richardson (LR) coefficients, which we call the flagged skew LR coefficients. These subsume several previously studied extensions of the LR coefficients. We establish the saturation property for these coefficients, generalizing work of Knutson-Tao and Kushwaha-Raghavan-Viswanath., Comment: Corrected references and fixed typos
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- 2023
214. Scheduling Network Function Chains Under Sub-Millisecond Latency SLOs
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Wang, Jianfeng, Gupta, Siddhant, Vieira, Marcos A. M., Raghavan, Barath, and Govindan, Ramesh
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Computer Science - Networking and Internet Architecture ,Computer Science - Distributed, Parallel, and Cluster Computing ,C.0 ,C.2.1 - Abstract
Network Function Virtualization (NFV) seeks to replace hardware middleboxes with software-based Network Functions (NFs). NFV systems are seeing greater deployment in the cloud and at the edge. However, especially at the edge, there is a mismatch between the traditional focus on NFV throughput and the need to meet very low latency SLOs, as edge services inherently require low latency. Moreover, cloud-based NFV systems need to achieve such low latency while minimizing CPU core usage. We find that real-world traffic exhibits burstiness that causes latency spikes of up to 10s of milliseconds in existing NFV systems. To address this, we built NetBlaze, which achieves sub-millisecond p99 latency SLOs, even for adversarial traffic, using a novel multi-scale core-scaling strategy. NetBlaze makes traffic-to-core allocation decisions at rack, server, and core-spatial scales, and at increasingly finer timescales, to accommodate multi-timescale bursts. In comparison with state-of-the-art approaches, NetBlaze is the only one capable of achieving sub-millisecond p99 latency SLOs while using a comparable number of cores., Comment: 12 pages + 3 pages (reference)
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- 2023
215. HeySQuAD: A Spoken Question Answering Dataset
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Wu, Yijing, Rallabandi, SaiKrishna, Srinivasamurthy, Ravisutha, Dakle, Parag Pravin, Gon, Alolika, and Raghavan, Preethi
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Spoken question answering (SQA) systems are critical for digital assistants and other real-world use cases, but evaluating their performance is a challenge due to the importance of human-spoken questions. This study presents a new large-scale community-shared SQA dataset called HeySQuAD, which includes 76k human-spoken questions, 97k machine-generated questions, and their corresponding textual answers from the SQuAD QA dataset. Our goal is to measure the ability of machines to accurately understand noisy spoken questions and provide reliable answers. Through extensive testing, we demonstrate that training with transcribed human-spoken and original SQuAD questions leads to a significant improvement (12.51%) in answering human-spoken questions compared to training with only the original SQuAD textual questions. Moreover, evaluating with a higher-quality transcription can lead to a further improvement of 2.03%. This research has significant implications for the development of SQA systems and their ability to meet the needs of users in real-world scenarios.
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- 2023
216. SemEval 2023 Task 6: LegalEval - Understanding Legal Texts
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Modi, Ashutosh, Kalamkar, Prathamesh, Karn, Saurabh, Tiwari, Aman, Joshi, Abhinav, Tanikella, Sai Kiran, Guha, Shouvik Kumar, Malhan, Sachin, and Raghavan, Vivek
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
In populous countries, pending legal cases have been growing exponentially. There is a need for developing NLP-based techniques for processing and automatically understanding legal documents. To promote research in the area of Legal NLP we organized the shared task LegalEval - Understanding Legal Texts at SemEval 2023. LegalEval task has three sub-tasks: Task-A (Rhetorical Roles Labeling) is about automatically structuring legal documents into semantically coherent units, Task-B (Legal Named Entity Recognition) deals with identifying relevant entities in a legal document and Task-C (Court Judgement Prediction with Explanation) explores the possibility of automatically predicting the outcome of a legal case along with providing an explanation for the prediction. In total 26 teams (approx. 100 participants spread across the world) submitted systems paper. In each of the sub-tasks, the proposed systems outperformed the baselines; however, there is a lot of scope for improvement. This paper describes the tasks, and analyzes techniques proposed by various teams., Comment: 13 Pages (9 Pages + References), Accepted at SemEval 2023 at ACL 2023
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- 2023
217. naplib-python: Neural Acoustic Data Processing and Analysis Tools in Python
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Mischler, Gavin, Raghavan, Vinay, Keshishian, Menoua, and Mesgarani, Nima
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Quantitative Biology - Neurons and Cognition ,Quantitative Biology - Quantitative Methods - Abstract
Recently, the computational neuroscience community has pushed for more transparent and reproducible methods across the field. In the interest of unifying the domain of auditory neuroscience, naplib-python provides an intuitive and general data structure for handling all neural recordings and stimuli, as well as extensive preprocessing, feature extraction, and analysis tools which operate on that data structure. The package removes many of the complications associated with this domain, such as varying trial durations and multi-modal stimuli, and provides a general-purpose analysis framework that interfaces easily with existing toolboxes used in the field., Comment: 9 pages including references, 1 table, 1 figure
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- 2023
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218. Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks
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Ramkumar, Vijaya Raghavan T., Arani, Elahe, and Zonooz, Bahram
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Deep neural networks (DNNs) are often trained on the premise that the complete training data set is provided ahead of time. However, in real-world scenarios, data often arrive in chunks over time. This leads to important considerations about the optimal strategy for training DNNs, such as whether to fine-tune them with each chunk of incoming data (warm-start) or to retrain them from scratch with the entire corpus of data whenever a new chunk is available. While employing the latter for training can be resource-intensive, recent work has pointed out the lack of generalization in warm-start models. Therefore, to strike a balance between efficiency and generalization, we introduce Learn, Unlearn, and Relearn (LURE) an online learning paradigm for DNNs. LURE interchanges between the unlearning phase, which selectively forgets the undesirable information in the model through weight reinitialization in a data-dependent manner, and the relearning phase, which emphasizes learning on generalizable features. We show that our training paradigm provides consistent performance gains across datasets in both classification and few-shot settings. We further show that it leads to more robust and well-calibrated models., Comment: Published in Transactions on Machine Learning Research (TMLR)
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- 2023
219. The role of elastic instability on the self-assembly of particle chains in simple shear flow
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Smith, Matthew G., Gibson, Graham M., Link, Andreas, Raghavan, Anand, Clarke, Andrew, Franke, Thomas, and Tassieri, Manlio
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Physics - Fluid Dynamics ,Condensed Matter - Soft Condensed Matter - Abstract
Flow-Induced Self-Assembly (FISA) is the phenomena of particle chaining in viscoelastic fluids while experiencing shear flow. FISA has a large number of applications across many fields including material science, food processing and biomedical engineering. Nonetheless, this phenomena is currently not fully understood and little has been done in literature so far to investigate the possible effects of the shear-induced elastic instability. In this work, a bespoke cone and plate shear cell is used to provide new insights on the FISA dynamics. In particular, we have fine tuned the applied shear rates to investigate the chaining phenomenon of micron-sized spherical particles suspended into a viscoelastic fluid characterised by a distinct onset of elastic instability. This has allowed us to reveal three phenomena never reported in literature before, i.e.: (I) the onset of the elastic instability is strongly correlated with an enhancement of FISA; (II) particle chains break apart when a constant shear is applied for `sufficiently' long-time (i.e. much longer than the fluids' longest relaxation time). This latter point correlates well with the outcomes of parallel superposition shear measurements, which (III) reveal a fading of the elastic component of the suspending fluid during continuous shear flows.
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- 2023
220. Giant electromechanical response from defective non-ferroelectric epitaxial BaTiO3 integrated on Si 100
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Vura, Sandeep, Parate, Shubham Kumar, Pal, Subhajit, Khandelwal, Upanya, Rai, Rajeev Kumar, Molleti, Sri Harsha, Kumar, Vishnu, Ventrapragada, Rama Satya Sandilya, Patil, Girish, Jain, Mudit, Mallya, Ambresh, Ahmadi, Majid, Kooi, Bart, Avasthi, Sushobhan, Ranjan, Rajeev, Raghavan, Srinivasan, Chandorkar, Saurabh, and Nukala, Pavan
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Condensed Matter - Materials Science - Abstract
Lead free, silicon compatible materials showing large electromechanical responses comparable to, or better than conventional relaxor ferroelectrics, are desirable for various nanoelectromechanical devices and applications. Defect-engineered electrostriction has recently been gaining popularity to obtain enhanced electromechanical responses at sub 100 Hz frequencies. Here, we report record values of electrostrictive strain coefficients (M31) at frequencies as large as 5 kHz (1.04 x 10-14 m2 per V2 at 1 kHz, and 3.87 x 10-15 m2 per V2 at 5 kHz) using A-site and oxygen-deficient barium titanate thin-films, epitaxially integrated onto Si. The effect is robust and retained even after cycling the devices >5000 times. Our perovskite films are non-ferroelectric, exhibit a different symmetry compared to stoichiometric BaTiO3 and are characterized by twin boundaries and nano polar-like regions. We show that the dielectric relaxation arising from the defect-induced features correlates very well with the observed giant electrostrictive response. These films show large coefficient of thermal expansion (2.36 x 10-5/K), which along with the giant M31 implies a considerable increase in the lattice anharmonicity induced by the defects. Our work provides a crucial step forward towards formulating guidelines to engineer large electromechanical responses even at higher frequencies in lead-free thin films., Comment: 26 pages, 4 figures, 8 supplementary figures
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- 2023
221. High Energy Density in layered 2D Nanomaterial based Polymer Dielectric Films
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Singh, Maninderjeet, Das, Priyanka, Samanta, Pabitra Narayan, Bera, Sumit, Tanthirige, Ruskshan, Shook, Brian, Nejat, Roshanak, Behera, Banarji, Zhang, Qiqi, Dai, Qilin, Pramanik, Avijit, Ray, Paresh, Raghavan, Dharmaraj, Leszczysnki, Jerzy, Karim, Alamgir, and Pradhan, Nihar R.
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Physics - Applied Physics ,Condensed Matter - Materials Science - Abstract
Dielectric capacitors are critical components in electronics and energy storage devices. The polymer based dielectric capacitors have advantages of flexibility, fast charge and discharge, low loss, and graceful failure. Elevating the use of polymeric dielectric capacitors for advanced energy applications such as electric vehicles (EVs) however requires significant enhancement of their energy densities. Here, we report a polymer thin film heterostructure based capacitor of poly(vinylidene fluoride)/poly(methyl methacrylate) with stratified 2D nanofillers (Mica or h-BN nanosheets) (PVDF/PMMA-2D fillers/PVDF), that shows enhanced permittivity, high dielectric strength and an ultra-high energy density of 75 J/cm3 with efficiency over 79%. Density functional theory calculations verify the observed permittivity enhancement. This approach of using oriented 2D nanofillers based polymer heterostructure composites is expected to be universal for designing high energy density thin film polymeric dielectric capacitors for myriads of applications.
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- 2023
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222. The evolving genetic landscape of telomere biology disorder dyskeratosis congenita
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Hemanth Tummala, Amanda J Walne, Mohsin Badat, Manthan Patel, Abigail M Walne, Jenna Alnajar, Chi Ching Chow, Ibtehal Albursan, Jennifer M Frost, David Ballard, Sally Killick, Peter Szitányi, Anne M Kelly, Manoj Raghavan, Corrina Powell, Reinier Raymakers, Tony Todd, Elpis Mantadakis, Sophia Polychronopoulou, Nikolas Pontikos, Tianyi Liao, Pradeep Madapura, Upal Hossain, Tom Vulliamy, and Inderjeet Dokal
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Dyskeratosis Congenita ,Telomeres ,POLA1 ,ncRNAs ,Medicine (General) ,R5-920 ,Genetics ,QH426-470 - Abstract
Abstract Dyskeratosis congenita (DC) is a rare inherited bone marrow failure syndrome, caused by genetic mutations that principally affect telomere biology. Approximately 35% of cases remain uncharacterised at the genetic level. To explore the genetic landscape, we conducted genetic studies on a large collection of clinically diagnosed cases of DC as well as cases exhibiting features resembling DC, referred to as ‘DC-like’ (DCL). This led us to identify several novel pathogenic variants within known genetic loci and in the novel X-linked gene, POLA1. In addition, we have also identified several novel variants in POT1 and ZCCHC8 in multiple cases from different families expanding the allelic series of DC and DCL phenotypes. Functional characterisation of novel POLA1 and POT1 variants, revealed pathogenic effects on protein-protein interactions with primase, CTC1-STN1-TEN1 (CST) and shelterin subunit complexes, that are critical for telomere maintenance. ZCCHC8 variants demonstrated ZCCHC8 deficiency and signs of pervasive transcription, triggering inflammation in patients’ blood. In conclusion, our studies expand the current genetic architecture and broaden our understanding of disease mechanisms underlying DC and DCL disorders.
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- 2024
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223. Tumor-agnostic cancer therapy using antibodies targeting oncofetal chondroitin sulfate
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Elena Ethel Vidal-Calvo, Anne Martin-Salazar, Swati Choudhary, Robert Dagil, Sai Sundar Rajan Raghavan, Lara Duvnjak, Mie Anemone Nordmaj, Thomas Mandel Clausen, Ann Skafte, Jan Oberkofler, Kaituo Wang, Mette Ø Agerbæk, Caroline Løppke, Amalie Mundt Jørgensen, Daria Ropac, Joana Mujollari, Shona Willis, Agnès Garcias López, Rebecca Louise Miller, Richard Torbjörn Gustav Karlsson, Felix Goerdeler, Yen-Hsi Chen, Ana R. Colaço, Yong Wang, Thomas Lavstsen, Agnieszka Martowicz, Irina Nelepcu, Mona Marzban, Htoo Zarni Oo, Maj Sofie Ørum-Madsen, Yuzhuo Wang, Morten A. Nielsen, Henrik Clausen, Michael Wierer, Dominik Wolf, Ismail Gögenur, Thor G. Theander, Nader Al-Nakouzi, Tobias Gustavsson, Mads Daugaard, and Ali Salanti
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Science - Abstract
Abstract Molecular similarities between embryonic and malignant cells can be exploited to target tumors through specific signatures absent in healthy adult tissues. One such embryonic signature tumors express is oncofetal chondroitin sulfate (ofCS), which supports disease progression and dissemination in cancer. Here, we report the identification and characterization of phage display-derived antibody fragments recognizing two distinct ofCS epitopes. These antibody fragments show binding affinity to ofCS in the low nanomolar range across a broad selection of solid tumor types in vitro and in vivo with minimal binding to normal, inflamed, or benign tumor tissues. Anti-ofCS antibody drug conjugates and bispecific immune cell engagers based on these targeting moieties disrupt tumor progression in animal models of human and murine cancers. Thus, anti-ofCS antibody fragments hold promise for the development of broadly effective therapeutic and diagnostic applications targeting human malignancies.
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- 2024
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224. Childhood maltreatment is linked to larger preferred interpersonal distances towards friends and strangers across the globe
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Shilat Haim-Nachum, Marie R. Sopp, Antonia M. Lüönd, Nimrah Afzal, Fredrik Åhs, Antje-Kathrin Allgaier, Adrián Arévalo, Christian Asongwe, Rahel Bachem, Stefanie R. Balle, Habte Belete, Tilahun Belete Mossie, Azi Berzengi, Necip Capraz, Deniz Ceylan, Daniel Dukes, Aziz Essadek, Natalia E. Fares-Otero, Sarah L. Halligan, Alla Hemi, Naved Iqbal, Laura Jobson, Einat Levy-Gigi, Chantal Martin-Soelch, Tanja Michael, Misari Oe, Miranda Olff, Helena Örnkloo, Krithika Prakash, Sarah M. Quaatz, Vijaya Raghavan, Muniarajan Ramakrishnan, Dorota Reis, Vedat Şar, Ulrich Schnyder, Soraya Seedat, Ibtihal Najm Shihab, Susilkumar Vandhana, Dany Laure Wadji, Rachel Wamser, Reut Zabag, Georgina Spies, and Monique C. Pfaltz
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Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Abstract Childhood maltreatment (CM) is thought to be associated with altered responses to social stimuli and interpersonal signals. However, limited evidence exists that CM is linked to larger comfortable interpersonal distance (CID) – the physical distance humans prefer towards others during social interactions. However, no previous study has investigated this association in a comprehensive sample, yielding sufficient statistical power. Moreover, preliminary findings are limited to the European region. Finally, it is unclear how CM affects CID towards different interaction partners, and whether CID is linked to social functioning and attachment. To address these outstanding issues, adults (N = 2986) from diverse cultures and socio-economic strata completed a reaction time task measuring CID towards an approaching stranger and friend. Higher CM was linked to a larger CID towards both friends and strangers. Moreover, insecure attachment and less social support were associated with larger CID. These findings demonstrate for the first time that CM affects CID across countries and cultures, highlighting the robustness of this association.
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- 2024
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225. Chemo-, regio- and enantioselective hydroformylation of trisubstituted cyclopropenes: access to chiral quaternary cyclopropanes
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Shuailong Li, Dequan Zhang, Aiswarya Purushothaman, Hui Lv, Shilpa Shilpa, Raghavan B. Sunoj, Xiuxiu Li, and Xumu Zhang
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Science - Abstract
Abstract Catalytic asymmetric synthesis of polysubstituted chiral cyclopropane presents a significant challenge in organic synthesis due to the difficulty in enantioselective control. Here we report a rhodium-catalyzed highly chemo-, regio- and enantioselective hydroformylation of trisubstituted cyclopropenes affording chiral quaternary cyclopropanes. Importantly, the easy made sterically bulky ligand L1 can effectively suppress hydrogenation and decomposition reactions and give quaternary cyclopropanes with high regio- and enantioselectivities for both aryl and alkyl functionalized substrates. Control experiments and computational studies reveal the sterically hindered well-defined chiral pocket instead of the substrates bearing electron-withdrawing diester groups is important for controlling the enantioselectivity and regioselectivity. Scale-up reaction and follow-up diverse transformations are also presented. Density Functional theory (DFT) computations suggest that the regio- and enantio-selectivities originate from the cyclopropene insertion to the Rh-H bond. The high regioselectivity is found to benefit from the presence of more efficient noncovalent interactions (NCIs) manifesting in the form of C–H···Cl, C–H···N, and l.p(Cl)···π contacts.
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- 2024
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226. Gamma amplitude-envelope correlations are strongly elevated within hyperexcitable networks in focal epilepsy
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Manoj Raghavan, Jared Pilet, Chad Carlson, Christopher T. Anderson, Wade Mueller, Sean Lew, Candida Ustine, Priyanka Shah-Basak, Vahab Youssofzadeh, and Scott A. Beardsley
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Focal epilepsy ,Cortical hyperexcitability ,Electrocorticography ,Functional connectivity ,Gamma amplitude envelope correlations ,Seizure onset zones ,Medicine ,Science - Abstract
Abstract Methods to quantify cortical hyperexcitability are of enormous interest for mapping epileptic networks in patients with focal epilepsy. We hypothesize that, in the resting state, cortical hyperexcitability increases firing-rate correlations between neuronal populations within seizure onset zones (SOZs). This hypothesis predicts that in the gamma frequency band (40–200 Hz), amplitude envelope correlations (AECs), a relatively straightforward measure of functional connectivity, should be elevated within SOZs compared to other areas. To test this prediction, we analyzed archived samples of interictal electrocorticographic (ECoG) signals recorded from patients who became seizure-free after surgery targeting SOZs identified by multiday intracranial recordings. We show that in the gamma band, AECs between nodes within SOZs are markedly elevated relative to those elsewhere. AEC-based node strength, eigencentrality, and clustering coefficient are also robustly increased within the SOZ with maxima in the low-gamma band (permutation test Z-scores > 8) and yield moderate discriminability of the SOZ using ROC analysis (maximal mean AUC ~ 0.73). By contrast to AECs, phase locking values (PLVs), a measure of narrow-band phase coupling across sites, and PLV-based graph metrics discriminate the seizure onset nodes weakly. Our results suggest that gamma band AECs may provide a clinically useful marker of cortical hyperexcitability in focal epilepsy.
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- 2024
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227. Cost-effectiveness of train-the-trainer versus expert consultation training models for implementing interpersonal psychotherapy in college mental health settings: evidence from a national cluster randomized trial
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Ramesh Raghavan, Ellen E. Fitzsimmons-Craft, R. Robinson Welch, Booil Jo, Enola K. Proctor, G. Terence Wilson, W. Stewart Agras, and Denise E. Wilfley
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College mental health ,Interpersonal psychotherapy ,Cost-effectiveness analysis ,Train-the-trainer ,Expert consultation ,Fidelity ,Medicine (General) ,R5-920 - Abstract
Abstract Background This study is a cost-effectiveness study of two implementation strategies designed to train therapists in college and university counseling centers to deliver interpersonal psychotherapy. Costs of implementing a train-the-trainer (TTT) strategy versus an expert consultation strategy were estimated, and their relative effects upon therapist outcomes were calculated and compared. Methods Twenty four counseling centers were recruited across the United States. These centers were randomized to either a TTT (experimental) condition, in which an in-house therapist trained other center therapists, or an expert consultation condition, in which center therapists participated in a workshop and received 12 months of ongoing supervision. The main outcome was therapist fidelity (adherence and competence) to interpersonal psychotherapy, assessed via audio recordings of therapy sessions, and analyzed using linear mixed models. Costs of each condition were quantified using time-driven activity-based costing methods, and involved a costing survey administered to center directors, follow up interviews and validation checks, and comparison of time tracking logs of trainers in the expert condition. Mean costs to produce one therapist were obtained for each condition. The costs to produce equivalent improvements in therapist-level outcomes were then compared between the two conditions. Results Mean cost incurred by counseling centers to train one therapist using the TTT strategy was $3,407 (median = $3,077); mean cost to produce one trained therapist in the control condition was $2,055 (median = $1,932). Therapists in the TTT condition, on average, demonstrated a 0.043 higher adherence score compared to therapists in the control condition; however, this difference was not statistically significant. For the competence outcome, effect size for therapists in the TTT condition was in the large range (1.16; 95% CI: 0.85–1.46; p
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- 2024
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228. Global DNA and RNA Methylation Signature in Response to Antipsychotic Treatment in First-Episode Schizophrenia Patients
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Angelin M, Gopinath P, Raghavan V, Thara R, Ahmad F, Munirajan AK, and Sudesh R
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schizophrenia ,global methylation ,treatment response ,5mc ,5hmc ,m6a ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
Mary Angelin,1 Padmavathi Gopinath,1 Vijaya Raghavan,1,2 Rangaswamy Thara,2 Faraz Ahmad,3 Arasamabattu Kannan Munirajan,1 Ravi Sudesh4 1Department of Genetics, University of Madras, Dr ALM PG Institute of Basic Medical Sciences, Taramani Campus, Chennai, Tamil Nadu, 600 113, India; 2Schizophrenia Research Foundation, Chennai, Tamil Nadu, 600 101, India; 3Department of Biotechnology, School of Bioscience and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India; 4Department of Biomedical Sciences, School of Bioscience and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, IndiaCorrespondence: Ravi Sudesh, Department of Biomedical Sciences, School of Bioscience and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India, Tel +91-9176028134, Email sudesh.ravi@vit.ac.in; sudesh.genetics@gmail.com Arasamabattu Kannan Munirajan, Department of Genetics, Dr. ALM PG Institute Basic Medical Sciences, University of Madras, Taramani, Chennai, Tamil Nadu, 600 113, India, Tel +91-44-24547064 ; +91-9444460136, Email akmunirajan@gmail.com; akmunirajan@unom.ac.inBackground: Schizophrenia is a heterogeneous chronic psychiatric disorder influenced by genetic and environmental factors. Environmental factors can alter epigenetic marks, which regulate gene expression and cause an array of systemic changes. Several studies have demonstrated the association of epigenetic modulations in schizophrenia, which can influence clinical course, symptoms, and even treatment. Based on this, we have examined the global DNA methylation patterns, namely the 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC); and the global RNA modification N6-methyladenosine (m6A) RNA methylation status in peripheral blood cells. First-Episode Psychosis (FEP) patients who were diagnosed with Schizophrenia (SCZ) and undergoing treatment were stratified as Treatment-Responsive (TR) and Treatment-Non-Responsive (TNR). Age- and sex-matched healthy subjects served as controls.Results: The methylation pattern of 5mC and 5hmC showed significant increases in patients in comparison to controls. Further, when patients were classified based on their response to treatment, there was a statistically significant increase in methylation patterns in the treatment non-responder group. 5fC and m6A levels did not show any statistical significance across the groups. Further, gender-based stratification did not yield any significant difference for the markers.Conclusion: The study highlights the increased global methylation pattern in SCZ patients and a significant difference between the TR versus TNR groups. Global 5mC and 5hmC epigenetic marks suggest their potential roles in schizophrenia pathology, and also in the treatment response to antipsychotics. Since not many studies were available on the treatment response, further validation and the use of more sensitive techniques to study methylation status could unravel the potential of these epigenetic modifications as biomarkers for SCZ as well as distinguishing the antipsychotic treatment response in patients.Keywords: schizophrenia, global methylation, treatment response, 5mC, 5hmC, m6A
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- 2024
229. Can white matter hyperintensities based Fazekas visual assessment scales inform about Alzheimer’s disease pathology in the population?
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Aishwarya Pradeep, Sheelakumari Raghavan, Scott A. Przybelski, Gregory M. Preboske, Christopher G. Schwarz, Val J. Lowe, David S. Knopman, Ronald C. Petersen, Clifford R. Jack, Jonathan Graff-Radford, Petrice M. Cogswell, and Prashanthi Vemuri
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White matter hyperintensities ,Alzheimer’s disease ,Fazekas score ,Periventricular white matter hyperintensity ,Deep white matter hyperintensity ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
Abstract Background White matter hyperintensities (WMH) are considered hallmark features of cerebral small vessel disease and have recently been linked to Alzheimer’s disease (AD) pathology. Their distinct spatial distributions, namely periventricular versus deep WMH, may differ by underlying age-related and pathobiological processes contributing to cognitive decline. We aimed to identify the spatial patterns of WMH using the 4-scale Fazekas visual assessment and explore their differential association with age, vascular health, AD imaging markers, namely amyloid and tau burden, and cognition. Because our study consisted of scans from GE and Siemens scanners with different resolutions, we also investigated inter-scanner reproducibility and combinability of WMH measurements on imaging. Methods We identified 1144 participants from the Mayo Clinic Study of Aging consisting of a population-based sample from Olmsted County, Minnesota with available structural magnetic resonance imaging (MRI), amyloid, and tau positron emission tomography (PET). WMH distribution patterns were assessed on FLAIR-MRI, both 2D axial and 3D, using Fazekas ratings of periventricular and deep WMH severity. We compared the association of periventricular and deep WMH scales with vascular risk factors, amyloid-PET, and tau-PET standardized uptake value ratio, automated WMH volume, and cognition using Pearson partial correlation after adjusting for age. We also evaluated vendor compatibility and reproducibility of the Fazekas scales using intraclass correlations (ICC). Results Periventricular and deep WMH measurements showed similar correlations with age, cardiometabolic conditions score (vascular risk), and cognition, (p
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- 2024
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230. Crisis in the gut: navigating gastrointestinal challenges in Gulf War Illness with bioengineering
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Claudia A. Collier, Aelita Salikhova, Sufiyan Sabir, Steven Foncerrada, and Shreya A. Raghavan
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Gulf War Illness ,Bioengineering ,Neuroimmune crosstalk ,Gastrointestinal motility ,Medicine (General) ,R5-920 ,Military Science - Abstract
Abstract Gulf War Illness (GWI) is characterized by a wide range of symptoms that manifests largely as gastrointestinal symptoms. Among these gastrointestinal symptoms, motility disorders are highly prevalent, presenting as chronic constipation, stomach pain, indigestion, diarrhea, and other conditions that severely impact the quality of life of GWI veterans. However, despite a high prevalence of gastrointestinal impairments among these veterans, most research attention has focused on neurological disturbances. This perspective provides a comprehensive overview of current in vivo research advancements elucidating the underlying mechanisms contributing to gastrointestinal disorders in GWI. Generally, these in vivo and in vitro models propose that neuroinflammation alters gut motility and drives the gastrointestinal symptoms reported in GWI. Additionally, this perspective highlights the potential and challenges of in vitro bioengineering models, which could be a crucial contributor to understanding and treating the pathology of gastrointestinal related-GWI.
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- 2024
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231. A(H2N2) and A(H3N2) influenza pandemics elicited durable cross-reactive and protective antibodies against avian N2 neuraminidases
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Zaolan Liang, Xia Lin, Lihong Sun, Kimberly M. Edwards, Wenjun Song, Hailiang Sun, Yanmin Xie, Fangmei Lin, Shiman Ling, Tingting Liang, Biying Xiao, Jiaqi Wang, Min Li, Chin-Yu Leung, Huachen Zhu, Nisha Bhandari, Raghavan Varadarajan, Min Z. Levine, Malik Peiris, Robert Webster, Vijaykrishna Dhanasekaran, Nancy H. L. Leung, Benjamin J. Cowling, Richard J. Webby, Mariette Ducatez, Mark Zanin, and Sook-San Wong
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Science - Abstract
Abstract Human cases of avian influenza virus (AIV) infections are associated with an age-specific disease burden. As the influenza virus N2 neuraminidase (NA) gene was introduced from avian sources during the 1957 pandemic, we investigate the reactivity of N2 antibodies against A(H9N2) AIVs. Serosurvey of healthy individuals reveal the highest rates of AIV N2 antibodies in individuals aged ≥65 years. Exposure to the 1968 pandemic N2, but not recent N2, protected against A(H9N2) AIV challenge in female mice. In some older adults, infection with contemporary A(H3N2) virus could recall cross-reactive AIV NA antibodies, showing discernable human- or avian-NA type reactivity. Individuals born before 1957 have higher anti-AIV N2 titers compared to those born between 1957 and 1968. The anti-AIV N2 antibodies titers correlate with antibody titers to the 1957 N2, suggesting that exposure to the A(H2N2) virus contribute to this reactivity. These findings underscore the critical role of neuraminidase immunity in zoonotic and pandemic influenza risk assessment.
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- 2024
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232. An Innovation development of deep-sea bacterial monitoring and classification based on artificial intelligence microbiological model
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M. Vidhyalakshmi, V. Manjula, H. Mickle Aancy, M. Beulah Viji Christiana, M. Jogendra Kumar, P. Nirmala, Hesham S. Almoallim, Sulaiman Ali Alharbi, and S.S. Raghavan
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Artificial intelligence ,biodiversity ,environmental pollution ,high fuel cost ,sea monitoring equipments ,Control engineering systems. Automatic machinery (General) ,TJ212-225 ,Automation ,T59.5 - Abstract
The current sea monitoring equipments are being used for a variety of purposes around the world. Currently used vehicles have some drawbacks. The first is the high fuel cost. The Vehicle engines cost more fuel as they have to release more power and environment and pollution. As well as not being able to stay under the sea for long days, there will often be a need for vehicles to come to the surface to refuel. The second is the vibrations and noise of these vehicles. The vibrations caused by these can be detrimental to the biodiversity of the ocean. Also, the noise makes it easier for enemies to identify our vehicles. Similarly when these vehicles go under water, water waves form on the surface. With this in mind, radar can detect what a vehicle under the sea looks like. In this paper, an artificial intelligence based microbiological model was proposed to monitor the sea level. With this biological model can greatly reduce fuels. It can get more capacity than normal vehicles. As fuel consumption decreases, so it does environmental pollution and since it operates quietly and without high vibrations, there is no threat to the biodiversity of the ocean.
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- 2024
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233. Evaluation of agricultural non-point source pollution using an in-situ and automated photochemical flow analysis system
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Yongqi Chen, Muhammad Awais, Junfeng Wu, Zhenfeng Li, Syed Muhammad Zaigham Abbas Naqvi, Mukhtar Iderawumi Abdulraheem, Hao Zhang, Ling Wang, Wei Zhang, Vijaya Raghavan, and Jiandong Hu
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Agricultural non-point source pollution (ANPS) ,FDR soil water content sensor ,Automatic photochemical flow analysis ,Sampling well ,Porous ceramic probe ,Medicine ,Science - Abstract
Abstract Off-line leachate collection from agricultural landscapes cannot guarantee precise evaluation of agricultural non-point source (ANPS) due to geospatial variations, time, and transportation from the field to the laboratory. Implementing an in-situ nitrogen and phosphorous monitoring system with a robust photochemical flow analysis is imperative for precision agriculture, enabling real-time intervention to minimize non-point source pollution and overcome the limitations posed by conventional analysis in laboratory. A reliable, robust and in-situ approach was proposed to monitor nitrogen and phosphorous for determining ANPS pollution. In this study, a home-made porous ceramic probe and the frequency domain reflectometer (FDR) based water content sensors were strategically placed at different soil depths to facilitate the collection of leachates. These solutions were subsequently analyzed by in-situ photochemical flow analysis monitoring system built across the field to estimate the concentrations of phosphorus and nitrogen. After applying both natural and artificial irrigation to the agricultural landscape, at least 10 mL of soil leachates was consistently collected using the porous ceramic probe within 20 min, regardless of the depth of the soil layers when the volumetric soil water contents are greater than 19%. The experimental results showed that under different weather conditions and irrigation conditions, the soil water content of 50 cm and 90 cm below the soil surface was 19.58% and 26.08%, respectively. The average concentrations of NH4 +-N, NO3 −-N, PO4 3− are 0.584 mg/L, 15.7 mg/L, 0.844 mg/L, and 0.562 mg/L, 16.828 mg/L and 0.878 mg/L at depths of 50 cm and 90 cm below the soil surface, respectively. Moreover, the comparison with conventional laboratory spectroscopic analysis confirmed R2 values of 0.9951, 0.9943, 0.9947 average concentration ranges of NH4 +-N, NO3 −-N, and PO4 3−, showcasing the accuracy and reliability of robust photochemical flow analysis in-situ monitoring system. The suggested monitoring system can be helpful in the assessment of soil nutrition for precision agriculture.
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- 2024
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234. Bacterial contamination in contact lens training area in private optical clinics
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Sana Badar Baig, Kalaivani Manokaran, Nagarajan Theruveethi, and Vivek Raghavan Muduthan
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Bacterial contamination ,Contact lens hygiene ,Contact lens complications ,Private eye clinic ,Contact lens container ,Solution ,Ophthalmology ,RE1-994 - Abstract
Abstract Background Contamination in the contact lens training area could be due to bacteria, which can lead to the major consequence of ocular infections. We aimed to investigate the contamination caused by bacteria in the contact lens training area in private optical clinics of the Udupi district, India. Methods A cross-sectional study evaluated the swabs from the contact lens container, contact lens solution tip, washing area and lens fitting area for bacterial contamination. Twenty swabs collected from different areas of five optical clinics were inoculated in Brain heart infusion broth (BHIB). The broth was streaked in MacConkey and Blood agar and incubated at standard conditions for the growth of bacteria. All isolates were identified using conventional culture methods, and Gram staining was performed. Results Twenty samples (contact lens case, n = 5; contact lens solution tip, n = 5; washing area, n = 5; cleaning towel, n = 5) from private optical clinics were recruited for the study. Bacterial growth was indicated in which lactose fermentation was seen at (15%), non-lactose fermentation at (35%), and no bacterial growth at (50%) in MacConkey agar. Partial or alpha-hemolytic (α hemolysis) was seen in (5%), complete or beta-hemolytic (β hemolysis) was seen in (40%), no hemolysis or gamma hemolysis (ϫ haemolysis), was seen in (30%), no growth was seen in (25%) on blood agar. Gram-positive cocci (45%), Gram-negative bacilli (20%), and no increase in (35%) were observed in MacConkey agar and Blood agar. Bacterial species were not identified in this study. Conclusion Contamination was found in lenses, solution tips, washing areas, and cleaning towels which might lead to ocular infections. Perception should be given to those responsible for fitting lenses.
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- 2024
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235. Blocking Oncostatin M receptor abrogates STAT3 mediated integrin signaling and overcomes chemoresistance in ovarian cancer
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Anjali Geethadevi, Zhiqiang Ku, Shirng-Wern Tsaih, Deepak Parashar, Ishaque P. Kadamberi, Wei Xiong, Hui Deng, Jasmine George, Sudhir Kumar, Sonam Mittal, Ningyan Zhang, Sunila Pradeep, Zhiqiang An, and Pradeep Chaluvally-Raghavan
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Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Abstract Chemotherapy such as cisplatin is widely used to treat ovarian cancer either before or after surgical debulking. However, cancer relapse due to chemotherapy resistance is a major challenge in the treatment of ovarian cancer. The underlying mechanisms related to chemotherapy resistance remain largely unclear. Therefore, identification of effective therapeutic strategies is urgently needed to overcome therapy resistance. Transcriptome-based analysis, in vitro studies and functional assays identified that cisplatin-resistant ovarian cancer cells express high levels of OSMR compared to cisplatin sensitive cells. Furthermore, OSMR expression associated with a module of integrin family genes and predominantly linked with integrin αV (ITGAV) and integrin β3 (ITGB3) for cisplatin resistance. Using ectopic expression and knockdown approaches, we proved that OSMR directly regulates ITGAV and ITGB3 gene expression through STAT3 activation. Notably, targeting OSMR using anti-OSMR human antibody inhibited the growth and metastasis of ovarian cancer cells and sensitized cisplatin treatment. Taken together, our results underscore the pivotal role of OSMR as a requirement for cisplatin resistance in ovarian cancer. Notably, OSMR fostered the expression of a distinct set of integrin genes, which in turn resulted into a crosstalk between OSMR and integrins for signaling activation that is critical for cisplatin resistance. Therefore, targeting OSMR emerges as a promising and viable strategy to reverse cisplatin-resistance in ovarian cancer.
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- 2024
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236. Improvisation and live accompaniment increase motor response and reward during a music playing task
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Anna Palumbo, Karleigh Groves, Eva Luna Munoz-Vidal, Alan Turry, Robert Codio, Preeti Raghavan, Heidi Schambra, Gerald T. Voelbel, and Pablo Ripollés
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Medicine ,Science - Abstract
Abstract Music provides a reward that can enhance learning and motivation in humans. While music is often combined with exercise to improve performance and upregulate mood, the relationship between music-induced reward and motor output is poorly understood. Here, we study music reward and motor output at the same time by capitalizing on music playing. Specifically, we investigate the effects of music improvisation and live accompaniment on motor, autonomic, and affective responses. Thirty adults performed a drumming task while (i) improvising or maintaining the beat and (ii) with live or recorded accompaniment. Motor response was characterized by acceleration of hand movements (accelerometry), wrist flexor and extensor muscle activation (electromyography), and the drum strike count (i.e., the number of drum strikes played). Autonomic arousal was measured by tonic response of electrodermal activity (EDA) and heart rate (HR). Affective responses were measured by a 12-item Likert scale. The combination of improvisation and live accompaniment, as compared to all other conditions, significantly increased acceleration of hand movements and muscle activation, as well as participant reports of reward during music playing. Improvisation, regardless of type of accompaniment, increased the drum strike count and autonomic arousal (including tonic EDA responses and several measures of HR), as well as participant reports of challenge. Importantly, increased motor response was associated with increased reward ratings during music improvisation, but not while participants were maintaining the beat. The increased motor responses achieved with improvisation and live accompaniment have important implications for enhancing dose of movement during exercise and physical rehabilitation.
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- 2024
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237. Atomic-scale visualization of a cascade of magnetic orders in the layered antiferromagnet GdTe3
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Arjun Raghavan, Marisa Romanelli, Julian May-Mann, Anuva Aishwarya, Leena Aggarwal, Anisha G. Singh, Maja D. Bachmann, Leslie M. Schoop, Eduardo Fradkin, Ian R. Fisher, and Vidya Madhavan
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Materials of engineering and construction. Mechanics of materials ,TA401-492 ,Atomic physics. Constitution and properties of matter ,QC170-197 - Abstract
Abstract GdTe3 is a layered antiferromagnet which has attracted attention due to its exceptionally high mobility, distinctive unidirectional incommensurate charge density wave (CDW), superconductivity under pressure, and a cascade of magnetic transitions between 7 and 12 K, with as yet unknown order parameters. Here, we use spin-polarized scanning tunneling microscopy to directly image the charge and magnetic orders in GdTe3. Below 7 K, we find a striped antiferromagnetic phase with twice the periodicity of the Gd lattice and perpendicular to the CDW. As we heat the sample, we discover a spin density wave with the same periodicity as the CDW between 7 and 12 K; the viability of this phase is supported by our Landau free energy model. Our work reveals the order parameters of the magnetic phases in GdTe3 and shows how the interplay between charge and spin can generate a cascade of magnetic orders.
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- 2024
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238. Rheo-kinetics and thermodynamics of oxazolidone modified epoxy film adhesive
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Pal, Ranajit, Sudhi, Suraj, and Raghavan, Rajeev
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- 2024
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239. The effect of 2020 Covid-19 pandemic lockdowns on seismic ambient noise recorded in Eastern Dharwar region, south-eastern India
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Saha, Satish, Biswas, Rahul, Raghavan, R. Vijaya, Sharma, A. N. S., Shekar, M., and Suresh, G.
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- 2024
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240. Preventing Post stroke spasticity: A goal for precision rehabilitation
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Raghavan, Preeti
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- 2024
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241. Correction: Bridging the Divide: Assessing Digital Infrastructure for Higher Education Online Learning
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Vishnu, Sreeram, Tengli, Mahesh B., Ramadas, Sendhil, Sathyan, Archana Raghavan, and Bhatt, Archana
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- 2024
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242. Effects of post-COVID-19 vaccination in oral cavity: a systematic review
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Sood, Anubhuti, Raghavan, Sreevatsan, Mishra, Deepika, and Priya, Harsh
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- 2024
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243. Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles
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Maulik, Romit, Egele, Romain, Raghavan, Krishnan, and Balaprakash, Prasanna
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Computer Science - Machine Learning ,Mathematics - Dynamical Systems - Abstract
Classical problems in computational physics such as data-driven forecasting and signal reconstruction from sparse sensors have recently seen an explosion in deep neural network (DNN) based algorithmic approaches. However, most DNN models do not provide uncertainty estimates, which are crucial for establishing the trustworthiness of these techniques in downstream decision making tasks and scenarios. In recent years, ensemble-based methods have achieved significant success for the uncertainty quantification in DNNs on a number of benchmark problems. However, their performance on real-world applications remains under-explored. In this work, we present an automated approach to DNN discovery and demonstrate how this may also be utilized for ensemble-based uncertainty quantification. Specifically, we propose the use of a scalable neural and hyperparameter architecture search for discovering an ensemble of DNN models for complex dynamical systems. We highlight how the proposed method not only discovers high-performing neural network ensembles for our tasks, but also quantifies uncertainty seamlessly. This is achieved by using genetic algorithms and Bayesian optimization for sampling the search space of neural network architectures and hyperparameters. Subsequently, a model selection approach is used to identify candidate models for an ensemble set construction. Afterwards, a variance decomposition approach is used to estimate the uncertainty of the predictions from the ensemble. We demonstrate the feasibility of this framework for two tasks - forecasting from historical data and flow reconstruction from sparse sensors for the sea-surface temperature. We demonstrate superior performance from the ensemble in contrast with individual high-performing models and other benchmarks.
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- 2023
244. Simple Procedures for Left and Right Keys of Semi-Standard Young Tableaux
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Kushwaha, Mrigendra Singh, Raghavan, K. N., and Viswanath, Sankaran
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Mathematics - Combinatorics ,05E10, 17B10, 22E46 - Abstract
We give simple procedures to obtain the left and right keys of a semi-standard Young tableau. Keys derive their interest from the fact that they encode the characters of Demazure and opposite Demazure modules for the general and special linear groups. Given the importance of keys, there are indeed several procedures available in the literature to determine them. In comparison, our procedures are new (to the best of our knowledge) and especially simple. Having said that, we hasten to add that there is nothing new in any individual ingredient that goes into our procedures. These ingredients are all routine, straightforward, and (in any case) occur in the literature. But they never quite seem to have been put together as done here. Our procedures end up repeatedly performing the Deodhar lifts, maximal lifts for the left key and minimal lifts for right key. Together with the well known fact that keys can be obtained by such repeated lifts, this justifies the procedures. The relevance of Deodhar lifts to combinatorial models for Demazure characters is well known in Standard Monomial Theory. Right and left keys appear respectively as initial and final directions of Lakshmibai-Seshadri paths in Littelmanns Path Model Theory., Comment: 24 pages; for v3: a whole section reviewing earlier procedures is added; other changes are minor edits
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- 2023
245. Stable ordered-union versus selective ultrafilters
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Raghavan, Dilip and Steprans, Juris
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Mathematics - Logic ,Mathematics - Combinatorics ,Mathematics - General Topology ,03E35, 05D10, 22A15, 54D35, 03E40 - Abstract
It will be shown to be consistent that there are at least two non-isomorphic selective ultrafilters, but no stable ordered-union ultrafilters. This answers a question of Blass from his 1987 paper which introduced the concept of a stable ordered-union ultrafilter., Comment: 42 pages. Submitted. Minor revisions made
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- 2023
246. A SWAT-based Reinforcement Learning Framework for Crop Management
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Madondo, Malvern, Azmat, Muneeza, Dipietro, Kelsey, Horesh, Raya, Jacobs, Michael, Bawa, Arun, Srinivasan, Raghavan, and O'Donncha, Fearghal
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Computer Science - Machine Learning - Abstract
Crop management involves a series of critical, interdependent decisions or actions in a complex and highly uncertain environment, which exhibit distinct spatial and temporal variations. Managing resource inputs such as fertilizer and irrigation in the face of climate change, dwindling supply, and soaring prices is nothing short of a Herculean task. The ability of machine learning to efficiently interrogate complex, nonlinear, and high-dimensional datasets can revolutionize decision-making in agriculture. In this paper, we introduce a reinforcement learning (RL) environment that leverages the dynamics in the Soil and Water Assessment Tool (SWAT) and enables management practices to be assessed and evaluated on a watershed level. This drastically saves time and resources that would have been otherwise deployed during a full-growing season. We consider crop management as an optimization problem where the objective is to produce higher crop yield while minimizing the use of external farming inputs (specifically, fertilizer and irrigation amounts). The problem is naturally subject to environmental factors such as precipitation, solar radiation, temperature, and soil water content. We demonstrate the utility of our framework by developing and benchmarking various decision-making agents following management strategies informed by standard farming practices and state-of-the-art RL algorithms.
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- 2023
247. A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
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Baker, Megan M., New, Alexander, Aguilar-Simon, Mario, Al-Halah, Ziad, Arnold, Sébastien M. R., Ben-Iwhiwhu, Ese, Brna, Andrew P., Brooks, Ethan, Brown, Ryan C., Daniels, Zachary, Daram, Anurag, Delattre, Fabien, Dellana, Ryan, Eaton, Eric, Fu, Haotian, Grauman, Kristen, Hostetler, Jesse, Iqbal, Shariq, Kent, Cassandra, Ketz, Nicholas, Kolouri, Soheil, Konidaris, George, Kudithipudi, Dhireesha, Learned-Miller, Erik, Lee, Seungwon, Littman, Michael L., Madireddy, Sandeep, Mendez, Jorge A., Nguyen, Eric Q., Piatko, Christine D., Pilly, Praveen K., Raghavan, Aswin, Rahman, Abrar, Ramakrishnan, Santhosh Kumar, Ratzlaff, Neale, Soltoggio, Andrea, Stone, Peter, Sur, Indranil, Tang, Zhipeng, Tiwari, Saket, Vedder, Kyle, Wang, Felix, Xu, Zifan, Yanguas-Gil, Angel, Yedidsion, Harel, Yu, Shangqun, and Vallabha, Gautam K.
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Computer Science - Machine Learning ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Robotics - Abstract
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development - both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future., Comment: To appear in Neural Networks
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- 2023
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248. Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection
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Hays, Chris, Schutzman, Zachary, Raghavan, Manish, Walk, Erin, and Zimmer, Philipp
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Computer Science - Machine Learning ,Computer Science - Social and Information Networks - Abstract
Accurate bot detection is necessary for the safety and integrity of online platforms. It is also crucial for research on the influence of bots in elections, the spread of misinformation, and financial market manipulation. Platforms deploy infrastructure to flag or remove automated accounts, but their tools and data are not publicly available. Thus, the public must rely on third-party bot detection. These tools employ machine learning and often achieve near perfect performance for classification on existing datasets, suggesting bot detection is accurate, reliable and fit for use in downstream applications. We provide evidence that this is not the case and show that high performance is attributable to limitations in dataset collection and labeling rather than sophistication of the tools. Specifically, we show that simple decision rules -- shallow decision trees trained on a small number of features -- achieve near-state-of-the-art performance on most available datasets and that bot detection datasets, even when combined together, do not generalize well to out-of-sample datasets. Our findings reveal that predictions are highly dependent on each dataset's collection and labeling procedures rather than fundamental differences between bots and humans. These results have important implications for both transparency in sampling and labeling procedures and potential biases in research using existing bot detection tools for pre-processing., Comment: 10 pages, 6 figures; updated citation, clarified language
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- 2023
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249. Online Class-Incremental Learning For Real-World Food Image Classification
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Raghavan, Siddeshwar, He, Jiangpeng, and Zhu, Fengqing
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Food image classification is essential for monitoring health and tracking dietary in image-based dietary assessment methods. However, conventional systems often rely on static datasets with fixed classes and uniform distribution. In contrast, real-world food consumption patterns, shaped by cultural, economic, and personal influences, involve dynamic and evolving data. Thus, require the classification system to cope with continuously evolving data. Online Class Incremental Learning (OCIL) addresses the challenge of learning continuously from a single-pass data stream while adapting to the new knowledge and reducing catastrophic forgetting. Experience Replay (ER) based OCIL methods store a small portion of previous data and have shown encouraging performance. However, most existing OCIL works assume that the distribution of encountered data is perfectly balanced, which rarely happens in real-world scenarios. In this work, we explore OCIL for real-world food image classification by first introducing a probabilistic framework to simulate realistic food consumption scenarios. Subsequently, we present an attachable Dynamic Model Update (DMU) module designed for existing ER methods, which enables the selection of relevant images for model training, addressing challenges arising from data repetition and imbalanced sample occurrences inherent in realistic food consumption patterns within the OCIL framework. Our performance evaluation demonstrates significant enhancements compared to established ER methods, showing great potential for lifelong learning in real-world food image classification scenarios. The code of our method is publicly accessible at https://gitlab.com/viper-purdue/OCIL-real-world-food-image-classification, Comment: Accepted at IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024)
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- 2023
250. An Integrated Framework for Multiscale Materials Simulation and Design
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Liu, Zi-Kui, primary, Chen, L.-Q., additional, Raghavan, P., additional, Du, Q., additional, Sofo, J. O., additional, Langer, S. A., additional, and Wolverton, C., additional
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- 2024
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