70,807 results on '"Raghavan AS"'
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302. Recent Progress in Nanodielectric Composites and Their Applications
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Nitinkumar, Joshi Harsh, Reghu, Navyasree, Akhilesh, P. K., Vlad, Alexandru, Balachandran, Meera, Raghavan, Prasanth, Lockwood, David J., Series Editor, Moharana, Srikanta, editor, Gregory, Duncan H., editor, and Mahaling, Ram Naresh, editor
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- 2024
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303. Assessing Health Seeking Behaviors and Economic Consequences of Morbidity in Indian Construction Workers: A Multicenter Study
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Soundararajan, Soundarya, Viramgami, Ankit, Sheth, Ankit, Beerappa, Ravichandran, Kalahasthi, Ravibabu, Sampathraju, Raghavan, Venugopal, Dhananjayan, Sarkar, Kamalesh, and Balachandar, Rakesh
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Medical care, Cost of -- Economic aspects -- Analysis ,Morbidity -- Analysis -- Economic aspects ,Construction workers -- Behavior ,Environmental issues ,Health - Abstract
Background: Construction laborers succumb to poor health due to the inherent workplace health hazards and poor socio-economic living conditions. With rising healthcare expenses, the increased risk of poor health may aggravate their economic status, pushing them deeper into poverty. Settings and Design: The current cross-sectional multicenter study comprehensively investigated the determinants of health, health-seeking behavior, and poor economic impact regarding catastrophic health expenditure (CHE) among construction laborers. Methods and Material: We collected details on illnesses among self and family members of the construction laborers that required healthcare visits during the previous year and their approximate expenses. Among the 1110 participants with complete data, 37 reported illness requiring a healthcare visit either for self or a family member. Results: Regression models to ascertain demographic and living condition determinants of perceived illness revealed an increased risk of illness when the kitchen is shared with the living space (OR = 1.87) and use unhygienic smoky cooking fuels (OR = 1.87). More than 25 of those who reported illness incurred CHE. Conclusion: We conclude that the frequency of perceived illness and the economic impact, i.e., CHE is relatively higher among the construction laborers. Our results demonstrate that poor living conditions add to the burden of morbidity in construction workers and families. Providing healthcare coverage for this population and engaging and educating them about affordable healthcare are necessary future steps to prevent the worsening of the economic situation. Keywords: Catastrophic health expenditure, construction workers, health utilization, prevalence, worker health, Author(s): Soundarya Soundararajan [1]; Ankit Viramgami [1]; Ankit Sheth [1]; Ravichandran Beerappa [2]; Ravibabu Kalahasthi [3]; Raghavan Sampathraju [2]; Dhananjayan Venugopal [2]; Kamalesh Sarkar [1]; Rakesh Balachandar (corresponding author) [1] [...]
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- 2024
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304. Understanding BLOOM: An empirical study on diverse NLP tasks
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Dakle, Parag Pravin, Rallabandi, SaiKrishna, and Raghavan, Preethi
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Computer Science - Computation and Language - Abstract
We view the landscape of large language models (LLMs) through the lens of the recently released BLOOM model to understand the performance of BLOOM and other decoder-only LLMs compared to BERT-style encoder-only models. We achieve this by evaluating the smaller BLOOM model variants (\textit{350m/560m} and \textit{1b3/1b7}) on several NLP benchmark datasets and popular leaderboards. We make the following observations: (1) BLOOM performance does not scale with parameter size, unlike other LLMs like GPT and BERT. Experiments fine-tuning BLOOM models show that the 560m variant performs similarly to or better than the 1b7 variant, (2) Zero-shot cross-lingual and multi-lingual fine-tuning experiments show that BLOOM is at par or worse than monolingual GPT-2 models, and (3) Toxicity analysis of prompt-based text generation using the RealToxicityPrompts dataset shows that the text generated by BLOOM is at least 17\% less toxic than GPT-2 and GPT-3 models.
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- 2022
305. Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark
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Petsiuk, Vitali, Siemenn, Alexander E., Surbehera, Saisamrit, Chin, Zad, Tyser, Keith, Hunter, Gregory, Raghavan, Arvind, Hicke, Yann, Plummer, Bryan A., Kerret, Ori, Buonassisi, Tonio, Saenko, Kate, Solar-Lezama, Armando, and Drori, Iddo
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
We provide a new multi-task benchmark for evaluating text-to-image models. We perform a human evaluation comparing the most common open-source (Stable Diffusion) and commercial (DALL-E 2) models. Twenty computer science AI graduate students evaluated the two models, on three tasks, at three difficulty levels, across ten prompts each, providing 3,600 ratings. Text-to-image generation has seen rapid progress to the point that many recent models have demonstrated their ability to create realistic high-resolution images for various prompts. However, current text-to-image methods and the broader body of research in vision-language understanding still struggle with intricate text prompts that contain many objects with multiple attributes and relationships. We introduce a new text-to-image benchmark that contains a suite of thirty-two tasks over multiple applications that capture a model's ability to handle different features of a text prompt. For example, asking a model to generate a varying number of the same object to measure its ability to count or providing a text prompt with several objects that each have a different attribute to identify its ability to match objects and attributes correctly. Rather than subjectively evaluating text-to-image results on a set of prompts, our new multi-task benchmark consists of challenge tasks at three difficulty levels (easy, medium, and hard) and human ratings for each generated image., Comment: NeurIPS 2022 Workshop on Human Evaluation of Generative Models (HEGM)
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- 2022
306. FastFlow: AI for Fast Urban Wind Velocity Prediction
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Low, Shi Jer, Venugopalan, Raghavan, S. G., Gopalan, Harish, Wong, Jian Cheng, Yeoh, Justin, and Ooi, Chin Chun
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Computer Science - Machine Learning ,Computer Science - Computers and Society ,Physics - Fluid Dynamics - Abstract
Data-driven approaches, including deep learning, have shown great promise as surrogate models across many domains. These extend to various areas in sustainability. An interesting direction for which data-driven methods have not been applied much yet is in the quick quantitative evaluation of urban layouts for planning and design. In particular, urban designs typically involve complex trade-offs between multiple objectives, including limits on urban build-up and/or consideration of urban heat island effect. Hence, it can be beneficial to urban planners to have a fast surrogate model to predict urban characteristics of a hypothetical layout, e.g. pedestrian-level wind velocity, without having to run computationally expensive and time-consuming high-fidelity numerical simulations. This fast surrogate can then be potentially integrated into other design optimization frameworks, including generative models or other gradient-based methods. Here we present the use of CNNs for urban layout characterization that is typically done via high-fidelity numerical simulation. We further apply this model towards a first demonstration of its utility for data-driven pedestrian-level wind velocity prediction. The data set in this work comprises results from high-fidelity numerical simulations of wind velocities for a diverse set of realistic urban layouts, based on randomized samples from a real-world, highly built-up urban city. We then provide prediction results obtained from the trained CNN, demonstrating test errors of under 0.1 m/s for previously unseen urban layouts. We further illustrate how this can be useful for purposes such as rapid evaluation of pedestrian wind velocity for a potential new layout. It is hoped that this data set will further accelerate research in data-driven urban AI, even as our baseline model facilitates quantitative comparison to future methods.
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- 2022
307. Named Entity Recognition in Indian court judgments
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Kalamkar, Prathamesh, Agarwal, Astha, Tiwari, Aman, Gupta, Smita, Karn, Saurabh, and Raghavan, Vivek
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Identification of named entities from legal texts is an essential building block for developing other legal Artificial Intelligence applications. Named Entities in legal texts are slightly different and more fine-grained than commonly used named entities like Person, Organization, Location etc. In this paper, we introduce a new corpus of 46545 annotated legal named entities mapped to 14 legal entity types. The Baseline model for extracting legal named entities from judgment text is also developed., Comment: to be published in NLLP 2022 Workshop at EMNLP
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- 2022
308. Generalizing the Wythoff Array and other Fibonacci Facts to Tribonacci Numbers
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Chen, Eric, Ge, Adam, Kalashnikov, Andrew, Khovanova, Tanya, Kim, Ella, Liang, Evin, Lubashev, Mira, Qian, Matthew, Raghavan, Rohith, Taycher, Benjamin, and Wang, Samuel
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Mathematics - Number Theory ,11K31, 11B39 - Abstract
In this paper, we generalize a lot of facts from John Conway and Alex Ryba's paper, \textit{The extra Fibonacci series and the Empire State Building}, where we replace the Fibonacci sequence with the Tribonacci sequence. We study the Tribonacci array, which we also call \textit{the Trithoff array} to emphasize the connection to the Wythoff array. We describe 13 new sequences., Comment: 28 pages, 5 tables
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- 2022
309. Low-power In-pixel Computing with Current-modulated Switched Capacitors
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Zhang, David, van der Wal, Gooitzen, Farkya, Saurabh, Senko, Thomas, Raghavan, Aswin, Isnardi, Michael, and Piacentino, Michael
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Computer Science - Hardware Architecture - Abstract
We present a scalable in-pixel processing architecture that can reduce the data throughput by 10X and consume less than 30 mW per megapixel at the imager frontend. Unlike the state-of-the-art (SOA) analog process-in-pixel (PIP) that modulates the exposure time of photosensors when performing matrix-vector multiplications, we use switched capacitors and pulse width modulation (PWM). This non-destructive approach decouples the sensor exposure and computing, providing processing parallelism and high data fidelity. Our design minimizes the computational complexity and chip density by leveraging the patch-based feature extraction that can perform as well as the CNN. We further reduce data using partial observation of the attended objects, which performs closely to the full frame observations. We have been studying the reduction of output features as a function of accuracy, chip density and power consumption from a transformer-based backend model for object classification and detection., Comment: 5 pages, 4 figures
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- 2022
310. Spin-selective tunneling from nanowires of the candidate topological Kondo insulator SmB6
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Aishwarya, Anuva, Cai, Zhuozhen, Raghavan, Arjun, Romanelli, Marisa, Wang, Xiaoyu, Li, Xu, Gu, G. D., Hirsbrunner, Mark, Hughes, Taylor, Liu, Fei, Jiao, Lin, and Madhavan, Vidya
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Condensed Matter - Strongly Correlated Electrons ,Condensed Matter - Mesoscale and Nanoscale Physics ,Condensed Matter - Materials Science - Abstract
Incorporating relativistic physics into quantum tunneling can lead to exotic behavior such as perfect transmission via Klein tunneling. Here, we probe the tunneling properties of spin-momentum locked relativistic fermions by designing and implementing a tunneling geometry that utilizes nanowires of the topological Kondo insulator candidate, SmB6. The nanowires are attached to the end of scanning tunneling microscope tips, and used to image the bicollinear stripe spin-order in the antiferromagnet Fe1.03Te with a Neel temperature of ~50 K. The antiferromagnetic stripes become invisible above 10 K concomitant with the suppression of the topological surface states. We further demonstrate that the direction of spin-polarization is tied to the tunneling direction. Our technique establishes SmB6 nanowires as ideal conduits for spin-polarized currents., Comment: 18 pages, 4 figures
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- 2022
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311. Chemical processing and waste management using SERS: a nanovative gateway for sustainable and robust bioremediation for agricultural lands
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Naqvi, Syed Muhammad Zaigham Abbas, Awais, Muhammad, Wei, Zhang, Wu, Junfeng, Raghavan, Vijaya, Hu, Jiandong, and Khan, M. Ijaz
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- 2024
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312. Myxofibrosarcoma of Head and Neck Region – A Rare Case Report
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Purohit, Sandeep, Ahlawat, Parveen, Tandon, Sarthak, Bellige, Akash Raghavan, and Gairola, Munish
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- 2024
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313. Quantitative susceptibility mapping from basal ganglia and related structures: correlation with disease severity in progressive supranuclear palsy
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Krishnan, Syam, George, Sneha Susan, Radhakrishnan, Vineeth, Raghavan, Sheelakumari, Thomas, Bejoy, Thulaseedharan, Jissa Vinoda, and Puthenveedu, Divya Kalikavil
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- 2024
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314. Complications of Spinal Cord Stimulators—A Comprehensive Review Article
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Koushik, Sarang S., Raghavan, Jagun, Saranathan, Shreya, Slinchenkova, Kateryna, Viswanath, Omar, and Shaparin, Naum
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- 2024
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315. Interactions of Cellular Energetic Gene Clusters in the Alzheimer’s Mouse Brain
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Raju, Raghavan Pillai, Cai, Lun, Tyagi, Alpna, and Pugazhenthi, Subbiah
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- 2024
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316. An enhanced and hybrid fingerprint minutiae feature extraction method for identifying and authenticating the patient’s noisy fingerprint
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Raghavan, R. and John Singh, K.
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- 2024
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317. The urban air mobility problem
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Golden, Bruce, Oden, Eric, and Raghavan, S.
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- 2023
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318. Attention guided grad-CAM : an improved explainable artificial intelligence model for infrared breast cancer detection
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Raghavan, Kaushik, B, Sivaselvan, and v, Kamakoti
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- 2023
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319. Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup
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Kashani, Kianoush B., Awdishu, Linda, Bagshaw, Sean M., Barreto, Erin F., Claure-Del Granado, Rolando, Evans, Barbara J., Forni, Lui G., Ghosh, Erina, Goldstein, Stuart L., Kane-Gill, Sandra L., Koola, Jejo, Koyner, Jay L., Liu, Mei, Murugan, Raghavan, Nadkarni, Girish N., Neyra, Javier A., Ninan, Jacob, Ostermann, Marlies, Pannu, Neesh, Rashidi, Parisa, Ronco, Claudio, Rosner, Mitchell H., Selby, Nicholas M., Shickel, Benjamin, Singh, Karandeep, Soranno, Danielle E., Sutherland, Scott M., Bihorac, Azra, and Mehta, Ravindra L.
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- 2023
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320. Inter-observer agreement among specialists in the diagnosis of oral potentially malignant disorders and oral cancer using store-and-forward technology
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Keerthi, Gurushanth, Mukhia, Nirza, Sunny, Sumsum P, Song, Bofan, Raghavan, Shubhasini A, Gurudath, Shubha, Mendonca, Pramila, Li, Shaobai, Patrick, Sanjana, Imchen, Tsusennaro, Leivon, Shirley T., Shruti, Tulika, Kolur, Trupti, Shetty, Vivek, Vidya Bhushan, R, Ramesh, Rohan Michael, Pillai, Vijay, Kathryn, O.S, Smith, Petra Wilder, Suresh, Amritha, Liang, Rongguang, Praveen Birur, N, and Kuriakose, Moni Abraham
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- 2023
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321. Functional Results After Nerve-Sparing, Sphincter Preserving Rectal Cancer Surgery: Patient-Reported Outcomes of Sexual and Urinary Dysfunction
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Patel, Swapnil, Raghavan, Sriniket, Garg, Vidur, Kazi, Mufaddal, Sukumar, Vivek, Desouza, Ashwin, and Saklani, Avanish
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- 2023
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322. Clinical Evaluation of Modified Technique of Alar Base Cinch Suture to Prevent Widening of Alar Base
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Murugan, Rajasekar, Rajiah, Davidson, Prasad, Cheruvathur, Kamalakaran, Arunkumar, Palani, Triveni, and Raghavan, Priyadharshini
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- 2023
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323. Evaluation of action of steroid molecules on SARS-CoV-2 by inhibiting NSP-15, an endoribonuclease
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Dhanabalan, Anantha Krishnan, Raghavan, Sriram Srinivasa, Rajendran, Selvakumar, Ramasamy, Velavan, Abdul, Shaik Abdul Azeez, Narayanasamy, Nandhagopal, and Krishnasamy, Gunasekaran
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- 2023
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324. On the tax efficiency of startup firms
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Allen, Eric J., Allen, Jeffrey C., Raghavan, Sharat, and Solomon, David H.
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- 2023
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325. Toxicities, illegalities and protest : a landscape of coal in South India
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Raghavan, Rishabh, Cross, Jamie, and Hoek, Lotte
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South India ,state-owned coal-fired thermal power plants ,coal-fired thermal power plants ,state-owned thermal power plants ,Ennore ,Tamil Nadu - Abstract
This thesis focuses on the construction and operation of state-owned coal-fired thermal power plants in Ennore, a coastal peninsular suburb located to the north of Chennai (Tamil Nadu, India). At the time of my research, Ennore was witnessing the development of nearly 2500 megawatts of coal-based energy projects, to add to the 3000 megawatts of coal-fired thermal power that was already generated on the peninsula -- a situation of energy investment that stood in stark contrast with the Indian government's publicized stance about moving away from fossil fuels. To unpack the effects of this seeming contradiction, I build on thirteen months of ethnographic fieldwork (2018-2019), where I investigated the contemporary and everyday interactions of coal and coal-based infrastructures with Ennore's socio-natural and political contexts. In exploring how people in Ennore made sense of the presence of coal that surrounded their lives, the chapters in this thesis describe a 'landscape of coal' composed of three different yet related field-sites from which I observed and participated in these interactions. These sites included people's bodily engagement with the multiple toxic substances that emerged from the combustion and circulation of coal; the range of financial and contractual illegalities that came embedded with the construction and operation of the power plants; and the shifting forms of mobilization and protests staged by Ennore's residents, trade unions and health activists in reaction to coal's presence. In researching this 'landscape of coal', I forward three interlinked arguments in the thesis. By attending to the artisanal labour of the fishermen who lived by Ennore's power plants, the first part of this thesis explores the ways in which toxic coal (together with the many by-products of its combustion and circulation) seeped through different bodies and environments. In using the analytic of "toxicity", I argue that research on coal and its infrastructures must broaden the range of its objects to consider the variegated porous relations that coal affects: between skin-born afflictions and the disappearing welfare state, between stilting rivers and changing labour markets, between embodied physical skill and sub-contracted informal work. In the second part, the ethnography moves to trade unions and explores how the ongoing presence of coal affected labour relations in and around Ennore. Through following a union leader in his meetings with different stakeholders around the power plants, I trace the ways in which coal and its circulation facilitated a range of illegalities that preserved uneven power structures and made livelihoods increasingly precarious, putting at risk any contractual binds by which labour was set to be renumerated or protected. Thirdly and finally, I foreground the 'politics of perceptibility' that residents, trade unions and activists engaged with in Ennore, as they selectively drew the attention of different publics to this landscape of coal, in a bid to further their fluctuating interests. I show how their performances oscillated between vehemently casting light upon the government's own illegal practices, and discreetly aligning themselves with various other concealed illegalities that surrounded the power plants. As the thesis unfolds, the initial contradiction remains in the backdrop: how did different residents of Ennore - whose lives were drawn in such intimate relations with coal and its infrastructures - collectively and individually make sense of the mismatch between the effective presence of coal, and the surge of global interest in its removal? In conclusion, by looking at recent coal-linked developments that held broaden the contextual basis that surrounds this project, I show how the desire for coal-free futures is still forecasted to overlap uneasily with the ongoing presence of coal.
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- 2023
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326. Measuring myofascial shear strain in chronic shoulder pain with ultrasound shear strain imaging: a case report
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Lingyi Zhao, Jonny Huang, Muyinatu A. Lediju Bell, and Preeti Raghavan
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Myofascial dysfunction ,Shoulder pain ,Shear strain ,Ultrasound imaging ,Case report ,Diseases of the musculoskeletal system ,RC925-935 - Abstract
Abstract Background Dysfunctional gliding of deep fascia and muscle layers forms the basis of myofascial pain and dysfunction, which can cause chronic shoulder pain. Ultrasound shear strain imaging may offer a non-invasive tool to quantitatively evaluate the extent of muscular dysfunctional gliding and its correlation with pain. This case study is the first to use ultrasound shear strain imaging to report the shear strain between the pectoralis major and minor muscles in shoulders with and without chronic pain. Case presentation The shear strain between the pectoralis major and minor muscles during shoulder rotation in a volunteer with chronic shoulder pain was measured with ultrasound shear strain imaging. The results show that the mean ± standard deviation shear strain was 0.40 ± 0.09 on the affected side, compared to 1.09 ± 0.18 on the unaffected side (p
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- 2024
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327. A retrospective study about cancellation of elective surgeries in a tertiary care hospital
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Bhagyasree Raghavan, Ajoy Menon, and Ambily Kaiparambil Velayudhan
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cancelation ,ot efficiency ,reasons ,resources ,surgery ,Medicine - Abstract
Introduction: Operation theaters (OTs) are one among the core functionalities in a health care institution that require a considerable amount of 5 Ms—man, machine, money, material, and management. Any idle time or underutilization in the OT creates a huge loss of these resources and is also inconvenient to patients and those on the waiting list. Thus, a planned and well-conceived operational method has to be followed to reduce the cancelations of scheduled cases and to optimally utilize the OTs so that they become more efficient. This study has been done to understand the basic reasons for the cancelation of elective surgeries at a tertiary care hospital and to effectively tackle them by suggesting suitable solutions to overcome these challenges. Objectives: Primarily, to understand the reasons for the cancelation of surgeries in OTs, and secondarily, to put forward suggestions for optimal usage of OTs. Materials and Methods: This was a retrospective study, with Pareto analysis and root cause analysis (RCA) as the tools. Records were obtained from the Central Scheduling Department of the Hospital between January 2020 and November 2022 (35 months). Results: During the study period, 53,177 surgeries were performed in the main OTs. There were 2289 (4.3%) cancelations. Nearly 84% of canceled surgeries were canceled within 24 h. Cancelations were 63% in males and 37% in females. Seventy-nine percent were of Indian nationality, and the rest were of various nationalities. Conclusion: The reasons for the cancelation of elective surgeries were multifactorial. Controlling these, and thus preventing cancelations, is imperative in the effective functioning of the institution as a whole. We recommend thorough evaluation, early preanesthetic checkup (PAC) and well-planned scheduling without haste as some of the measures to reduce the chances of cancelation.
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- 2024
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328. Parkinsonism following bilateral chronic subdural hematoma that presented as orthostatic headache: Highlighting clinical pearls for family physicians and physiotherapists
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Colis Anwari, Nila Raghavan, B. C. Rao, and Ramakrishna Prasad
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chronic headaches ,chronic subdural hematoma ,family physicians ,parkinsonism ,physiotherapist ,Medicine - Abstract
Chronic subdural hematoma (CSDH) is a great mimicker. It should be considered in anyone presenting with chronic headaches that show postural variation. Parkinsonism following CSDH, while known, is only rarely reported in the literature. Hyponatremia, rapid correction of hyponatremia, medications, and mechanical pressure are thought to be risk factors. Here, we report a case of a 61-year-old male diagnosed with bilateral CSDH managed by craniotomy and clot evacuation who developed parkinsonism. We share several learnings (clinical pearls) that emerged from the close collaboration and co-learning curve between a family physician and physiotherapist involved in home-based rehabilitation. In conclusion, while managing the postoperative course of patients with CSDH, clinicians should maintain a high index of suspicion for parkinsonism. Early recognition and appropriate management with syndopa with supportive physiotherapy results in significant improvement of function and quality of life. Notably, parkinsonism following SDH is transient and nonprogressive and may not require lifelong therapy.
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- 2024
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329. Analyzing Tumor Budding Scores in Invasive Breast Carcinoma: A Tertiary Care Center Study in South India
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Vasudevan Sudha, Kannan Kavitha, Raghavan ATM Venkat, and Sulochana Sonti
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breast cancer ,lymphovascular invasion ,prognosis ,tumor budding score ,tumor grading ,Pharmacy and materia medica ,RS1-441 ,Analytical chemistry ,QD71-142 - Abstract
Introduction and AimTumor budding is a distinctive phenomenon which involves the presence of small clusters or individual cancer cells at the invasive front of tumors. Tumor budding has garnered attention due to its potential implications for prognosis, treatment strategies, and our understanding of cancer progression. Our aim is to study the distribution of tumor buds and its scoring in patients with infiltrating breast carcinoma and to associate with other histopathological parameters like the size of the tumor, its grade, lymphovascular invasion, and lymph node metastasis. Materials and MethodsThis was a study analyzing the data of 70 resected specimens of primary breast carcinomas and providing a descriptive overview. Tumor budding was recognized, counted, and graded in hematoxylin and eosin slides. The cases were classified as low (0–4), intermediate (5–9), and high (≥10 buds) based on the count of tumor buds. Tumor budding has significant correlation with tumor grade and tumor size. ResultsOf the 70 cases, 60 cases (85.71%) were diagnosed as invasive ductal carcinoma NOS. The majority [38 (54.28%)] of the cases showed an intermediate tumor budding score of 5–9/10 HPF. ConclusionEvaluation of tumor budding allows pathologists and oncologists to gather valuable information about the tumor’s biological aggressiveness and potential for metastasis. It also helps in better risk stratification of patients, enabling a more personalized and tailored approach to treatment planning. In conclusion, assessing tumor budding in breast carcinoma holds significant clinical importance in the management and prognosis of this disease.
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- 2024
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330. Mind the Gap in Kidney Care: Translating What We Know into what we do
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Valerie A. Luyckx, Katherine R. Tuttle, Dina Abdellatif, Ricardo Correa- Rotter, Winston W.S. Fung, Agnès Haris, Li-Li Hsiao, Makram Khalife, Latha A. Kumaraswami, Fiona Loud, Vasundhara Raghavan, Stefanos Roumeliotis, Marianella Sierra, Ifeoma Ulasi, Bill Wang, Siu-Fai Lui, Vassilios Liakopoulos, and Alessandro Balducci
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Internal medicine ,RC31-1245 ,Pediatrics ,RJ1-570 - Published
- 2024
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331. Therapeutic effect of Topical Sirolimus on Facial Angiofibromas in Patients of Tuberous Sclerosis Complex
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Leena HN, Savitha B Raghavan, Yogesh HR, Sujatha Sowmyanarayan, Amberkar Mohanbabu Vittalrao, and Abhineetha Hosthota
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facial angiofibroma ,topical sirolimus ,tuberous sclerosis complex ,Medicine - Abstract
Introduction: Tuberous sclerosis complex (TSC) is a rare autosomal dominant disorder. Facial angiofibromas are the most common cutaneous findings of TSC. Treatment modalities such as laser, surgery, and/or cryotherapy are employed. Topical therapy with Sirolimus, an mTOR inhibitor, showed beneficial effects. Objective: To study the effects of topical sirolimus (0.1%) on Facial Angiofibromas in patients of TSC. Methodology: Four patients with facial angiofibromas were included. They applied Sirolimus preparation twice daily, for 3 months. The Facial Angiofibroma Severity Index (FASI) was recorded pre-intervention, at 3 months and after 6 months. Results: All the patients showed a reduction in the FASI score at the end of three months of therapy. In three patients, on discontinuing therapy, there was no change in the FASI score at the end of six months, i.e., FASI 3 and FASI 6 were the same. Conclusion: Topical sirolimus is an effective treatment for facial angiofibroma in patients with TSC.
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- 2024
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332. Earth Virtualization Engines (EVE)
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B. Stevens, S. Adami, T. Ali, H. Anzt, Z. Aslan, S. Attinger, J. Bäck, J. Baehr, P. Bauer, N. Bernier, B. Bishop, H. Bockelmann, S. Bony, G. Brasseur, D. N. Bresch, S. Breyer, G. Brunet, P. L. Buttigieg, J. Cao, C. Castet, Y. Cheng, A. Dey Choudhury, D. Coen, S. Crewell, A. Dabholkar, Q. Dai, F. Doblas-Reyes, D. Durran, A. El Gaidi, C. Ewen, E. Exarchou, V. Eyring, F. Falkinhoff, D. Farrell, P. M. Forster, A. Frassoni, C. Frauen, O. Fuhrer, S. Gani, E. Gerber, D. Goldfarb, J. Grieger, N. Gruber, W. Hazeleger, R. Herken, C. Hewitt, T. Hoefler, H.-H. Hsu, D. Jacob, A. Jahn, C. Jakob, T. Jung, C. Kadow, I.-S. Kang, S. Kang, K. Kashinath, K. Kleinen-von Königslöw, D. Klocke, U. Kloenne, M. Klöwer, C. Kodama, S. Kollet, T. Kölling, J. Kontkanen, S. Kopp, M. Koran, M. Kulmala, H. Lappalainen, F. Latifi, B. Lawrence, J. Y. Lee, Q. Lejeun, C. Lessig, C. Li, T. Lippert, J. Luterbacher, P. Manninen, J. Marotzke, S. Matsouoka, C. Merchant, P. Messmer, G. Michel, K. Michielsen, T. Miyakawa, J. Müller, R. Munir, S. Narayanasetti, O. Ndiaye, C. Nobre, A. Oberg, R. Oki, T. Özkan-Haller, T. Palmer, S. Posey, A. Prein, O. Primus, M. Pritchard, J. Pullen, D. Putrasahan, J. Quaas, K. Raghavan, V. Ramaswamy, M. Rapp, F. Rauser, M. Reichstein, A. Revi, S. Saluja, M. Satoh, V. Schemann, S. Schemm, C. Schnadt Poberaj, T. Schulthess, C. Senior, J. Shukla, M. Singh, J. Slingo, A. Sobel, S. Solman, J. Spitzer, P. Stier, T. Stocker, S. Strock, H. Su, P. Taalas, J. Taylor, S. Tegtmeier, G. Teutsch, A. Tompkins, U. Ulbrich, P.-L. Vidale, C.-M. Wu, H. Xu, N. Zaki, L. Zanna, T. Zhou, and F. Ziemen
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Environmental sciences ,GE1-350 ,Geology ,QE1-996.5 - Abstract
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
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- 2024
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333. Personality and Job Performance in Türkiye: Psychometric Meta-analysis of Turkish Studies
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Volkan Aşkun, Mukhunth Raghavan, Edina Ajanovic, Rabia Çizel, and Brenton M. Wiernik
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big five ,job performance ,psychometric meta-analysis ,collectivist cultures ,türkiye ,Psychology ,BF1-990 - Abstract
For decades researchers have explored the link between the Big Five personality traits and job performance, conducting studies across various contexts and sectors. The study seeks to test the link between the Big Five dimensions of personality and job performance in Türkiye, for which an integration of 38 studies involving 18,021 participants was performed. By using psychometric meta-analysis, the study compares and evaluates the similarities and differences among the Türkiye studies and the broader literature on this topic. Additionally, this study is among the first to address the moderating effect of evaluators and sectors on the relationship between Big Five personality traits and job performance dimensions. The findings suggest that there are differences between the Turkish studies and the existing literature, which could be explained by cultural differences and social norms specific to collectivist countries like Türkiye.
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- 2024
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334. A systematic review and meta-analysis of hybrid vs. cemented stems – which method is more optimal for revision total knee arthroplasty?
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Yogen Thever, Sir Young James Loh, Raghuraman Raghavan, Rong Chuin Toh, and Ing How Moo
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Cemented fixation ,Hybrid fixation ,Revision TKA ,Loosening ,Re-revision ,Diseases of the musculoskeletal system ,RC925-935 - Abstract
Abstract Introduction The number of primary and revision Total Knee Arthroplasty (TKA) cases are expected to increase in future. There are various advantages and disadvantage to employing either of the two main types of stem fixation methods – cemented or hybrid technique. This review aimed to study the most optimal fixation method for revision TKAs by comparing radiological outcomes and re-revision rates. Methods A systematic review and meta-analysis was performed using PubMed and Cochrane Library from 2010 to identify studies explicitly comparing outcomes between cemented against hybrid fixation revision TKA techniques, with a minimum follow up of at least 24 months. A total of 8 studies was included in this review. Egger’s test and visual inspection of the funnel plot did not reveal publication bias. Results There was no statistically significant difference in radiological failure and loosening (OR 0.79, CI 0.37–1.66, I2 = 29%, p = 0.22), all causes of re-revision (OR 1.03, CI 0.73–1.44, I2 = 0%, p = 0.56) and aseptic revision (OR 0.74, CI 0.27–2.02, I2 = 0%, p = 0.41) between cemented and hybrid techniques. Functional and pain outcomes compared between the two fixation techniques were largely similar across the studies included in this meta-analysis. Conclusion Despite a trend favouring hybrid stems in revision TKA, current evidence revealed that radiological outcomes and re-revision rates are largely similar between cemented and hybrid fixation techniques.
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- 2024
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335. Influenza antibody breadth and effector functions are immune correlates from acquisition of pandemic infection of children
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Janice Z. Jia, Carolyn A. Cohen, Haogao Gu, Milla R. McLean, Raghavan Varadarajan, Nisha Bhandari, Malik Peiris, Gabriel M. Leung, Leo L. M. Poon, Tim Tsang, Amy W. Chung, Benjamin J. Cowling, Nancy H. L. Leung, and Sophie A. Valkenburg
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Science - Abstract
Abstract Cross-reactive antibodies with Fc receptor (FcR) effector functions may mitigate pandemic virus impact in the absence of neutralizing antibodies. In this exploratory study, we use serum from a randomized placebo-controlled trial of seasonal trivalent influenza vaccination in children (NCT00792051) conducted at the onset of the 2009 H1N1 pandemic (pH1N1) and monitored for infection. We found that seasonal vaccination increases pH1N1 specific antibodies and FcR effector functions. Furthermore, prospective baseline antibody profiles after seasonal vaccination, prior to pH1N1 infection, show that unvaccinated uninfected children have elevated ADCC effector function, FcγR3a and FcγR2a binding antibodies to multiple pH1N1 proteins, past seasonal and avian (H5, H7 and H9) strains. Whereas, children that became pH1N1 infected after seasonal vaccination have antibodies focussed to seasonal strains without FcR functions, and greater aggregated HA-specific profiles for IgM and IgG3. Modeling to predict infection susceptibility, ranked baseline hemagglutination antibody inhibition as the highest contributor to lack of pH1N1 infection, in combination with features that include pH1-IgG1, H1-stem responses and FcR binding to seasonal vaccine and pH1 proteins. Thus, seasonal vaccination can have benefits against pandemic influenza viruses, and some children already have broadly reactive antibodies with Fc potential without vaccination and may be considered ‘elite influenza controllers’.
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- 2024
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336. Influences of amyloid-β and tau on white matter neurite alterations in dementia with Lewy bodies
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Elijah Mak, Robert I. Reid, Scott A. Przybelski, Timothy G. Lesnick, Christopher G. Schwarz, Matthew L. Senjem, Sheelakumari Raghavan, Prashanthi Vemuri, Clifford R. Jack, Hoon Ki Min, Manoj K. Jain, Toji Miyagawa, Leah K. Forsberg, Julie A. Fields, Rodolfo Savica, Jonathan Graff-Radford, David T. Jones, Hugo Botha, Erik K. St. Louis, David S. Knopman, Vijay K. Ramanan, Dennis W. Dickson, Neill R. Graff-Radford, Tanis J. Ferman, Ronald C. Petersen, Val J. Lowe, Bradley F. Boeve, John T. O’Brien, and Kejal Kantarci
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Neurology. Diseases of the nervous system ,RC346-429 - Abstract
Abstract Dementia with Lewy bodies (DLB) is a neurodegenerative condition often co-occurring with Alzheimer’s disease (AD) pathology. Characterizing white matter tissue microstructure using Neurite Orientation Dispersion and Density Imaging (NODDI) may help elucidate the biological underpinnings of white matter injury in individuals with DLB. In this study, diffusion tensor imaging (DTI) and NODDI metrics were compared in 45 patients within the dementia with Lewy bodies spectrum (mild cognitive impairment with Lewy bodies (n = 13) and probable dementia with Lewy bodies (n = 32)) against 45 matched controls using conditional logistic models. We evaluated the associations of tau and amyloid-β with DTI and NODDI parameters and examined the correlations of AD-related white matter injury with Clinical Dementia Rating (CDR). Structural equation models (SEM) explored relationships among age, APOE ε4, amyloid-β, tau, and white matter injury. The DLB spectrum group exhibited widespread white matter abnormalities, including reduced fractional anisotropy, increased mean diffusivity, and decreased neurite density index. Tau was significantly associated with limbic and temporal white matter injury, which was, in turn, associated with worse CDR. SEM revealed that amyloid-β exerted indirect effects on white matter injury through tau. We observed widespread disruptions in white matter tracts in DLB that were not attributed to AD pathologies, likely due to α-synuclein-related injury. However, a fraction of the white matter injury could be attributed to AD pathology. Our findings underscore the impact of AD pathology on white matter integrity in DLB and highlight the utility of NODDI in elucidating the biological basis of white matter injury in DLB.
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- 2024
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337. Reversibly Sticking Metals and Graphite to Hydrogels and Tissues
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Wenhao Xu, Faraz A. Burni, and Srinivasa R. Raghavan
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Chemistry ,QD1-999 - Published
- 2024
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338. Hardware implementation of memristor-based artificial neural networks
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Fernando Aguirre, Abu Sebastian, Manuel Le Gallo, Wenhao Song, Tong Wang, J. Joshua Yang, Wei Lu, Meng-Fan Chang, Daniele Ielmini, Yuchao Yang, Adnan Mehonic, Anthony Kenyon, Marco A. Villena, Juan B. Roldán, Yuting Wu, Hung-Hsi Hsu, Nagarajan Raghavan, Jordi Suñé, Enrique Miranda, Ahmed Eltawil, Gianluca Setti, Kamilya Smagulova, Khaled N. Salama, Olga Krestinskaya, Xiaobing Yan, Kah-Wee Ang, Samarth Jain, Sifan Li, Osamah Alharbi, Sebastian Pazos, and Mario Lanza
- Subjects
Science - Abstract
Abstract Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.
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- 2024
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339. The Lonely Hearts Club: A polyphonic reflection on the making of an erotica performance in India
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Ghosalkar, Anuja, Jacob, Nidhi Mariam, Madhav, Tushar, Oishorjyo, Philip, Rency, Raghavan, Balakrishnan, and Rajendran, Bhavana
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- 2024
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340. Differencing based Self-supervised pretraining for Scene Change Detection
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Ramkumar, Vijaya Raghavan T., Arani, Elahe, and Zonooz, Bahram
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Scene change detection (SCD), a crucial perception task, identifies changes by comparing scenes captured at different times. SCD is challenging due to noisy changes in illumination, seasonal variations, and perspective differences across a pair of views. Deep neural network based solutions require a large quantity of annotated data which is tedious and expensive to obtain. On the other hand, transfer learning from large datasets induces domain shift. To address these challenges, we propose a novel \textit{Differencing self-supervised pretraining (DSP)} method that uses feature differencing to learn discriminatory representations corresponding to the changed regions while simultaneously tackling the noisy changes by enforcing temporal invariance across views. Our experimental results on SCD datasets demonstrate the effectiveness of our method, specifically to differences in camera viewpoints and lighting conditions. Compared against the self-supervised Barlow Twins and the standard ImageNet pretraining that uses more than a million additional labeled images, DSP can surpass it without using any additional data. Our results also demonstrate the robustness of DSP to natural corruptions, distribution shift, and learning under limited labeled data., Comment: Published at Conference on Lifelong Learning Agents (CoLLAs 2022)
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- 2022
341. Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2
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Daniels, Zachary, Raghavan, Aswin, Hostetler, Jesse, Rahman, Abrar, Sur, Indranil, Piacentino, Michael, and Divakaran, Ajay
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
One approach to meet the challenges of deep lifelong reinforcement learning (LRL) is careful management of the agent's learning experiences, to learn (without forgetting) and build internal meta-models (of the tasks, environments, agents, and world). Generative replay (GR) is a biologically inspired replay mechanism that augments learning experiences with self-labelled examples drawn from an internal generative model that is updated over time. We present a version of GR for LRL that satisfies two desiderata: (a) Introspective density modelling of the latent representations of policies learned using deep RL, and (b) Model-free end-to-end learning. In this paper, we study three deep learning architectures for model-free GR, starting from a na\"ive GR and adding ingredients to achieve (a) and (b). We evaluate our proposed algorithms on three different scenarios comprising tasks from the Starcraft-2 and Minigrid domains. We report several key findings showing the impact of the design choices on quantitative metrics that include transfer learning, generalization to unseen tasks, fast adaptation after task change, performance wrt task expert, and catastrophic forgetting. We observe that our GR prevents drift in the features-to-action mapping from the latent vector space of a deep RL agent. We also show improvements in established lifelong learning metrics. We find that a small random replay buffer significantly increases the stability of training. Overall, we find that "hidden replay" (a well-known architecture for class-incremental classification) is the most promising approach that pushes the state-of-the-art in GR for LRL and observe that the architecture of the sleep model might be more important for improving performance than the types of replay used. Our experiments required only 6% of training samples to achieve 80-90% of expert performance in most Starcraft-2 scenarios., Comment: Accepted to the First Conference on Lifelong Learning Agents (CoLLAs 2022)
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- 2022
342. Magnetic-field sensitive charge density wave orders in the superconducting phase of UTe2
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Aishwarya, Anuva, May-Mann, Julian, Raghavan, Arjun, Nie, Laimei, Romanelli, Marisa, Ran, Sheng, Saha, Shanta R., Paglione, Johnpierre, Butch, Nicholas P., Fradkin, Eduardo, and Madhavan, Vidya
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Condensed Matter - Superconductivity ,Condensed Matter - Strongly Correlated Electrons - Abstract
The intense interest in triplet superconductivity partly stems from theoretical predictions of exotic excitations such as non-abelian Majorana modes, chiral supercurrents, and half-quantum vortices. However, fundamentally new, and unexpected states may emerge when triplet superconductivity appears in a strongly correlated system. In this work we use scanning tunneling microscopy to reveal an unusual charge density wave (CDW) order in the heavy fermion triplet superconductor, UTe2. Our high-resolution maps reveal a multi-component incommensurate CDW whose intensity get weaker with increasing field, eventually disappearing at the superconducting critical field, Hc2. To explain the origin and phenomenology of this unusual CDW, we construct a Ginzburg-Landau theory for a uniform triplet superconductor coexisting with three triplet pair density wave (PDW) states. This theory gives rise to daughter CDWs which would be sensitive to magnetic field due to their origin in a triplet PDW state, and naturally explains our data. Our discovery of a CDW sensitive to magnetic fields and strongly intertwined with superconductivity, provides important new information for understanding the order parameter of UTe2 and uncovers the possible existence of a new kind of triplet PDW order which has not been previously explored., Comment: 12 pages, 4 figures, Supplementary information
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- 2022
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343. Fibonacci Sequences of 1D, 2D Words: Enumerating and Locating the Factors of the Fixed Points
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Mohankumar, Sivasankar and Raghavan, Rama
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Mathematics - Combinatorics - Abstract
Given an infinite word, enumerating its factors is an important exercise for understanding the structure of the word. The process of finding all the factors is quite tricky for two-dimensional words. In this paper, two possible ways of enumerating the factors of the fixed point ($f_{\infty,\infty}$) of the sequence of Fibonacci arrays and a method for locating these factors in $f_{\infty,\infty}$ are explored. In addition, the factor complexity and the locations of the factors of the fixed point of Fibonacci sequence of arrays are also analysed.
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- 2022
344. Understanding blockchain: definitions, architecture, design, and system comparison
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Tabatabaei, Mohammad Hossein, Vitenberg, Roman, and Veeraragavan, Narasimha Raghavan
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Computer Science - Networking and Internet Architecture ,Computer Science - Databases - Abstract
The explosive advent of the blockchain technology has led to hundreds of blockchain systems in the industry, thousands of academic papers published over the last few years, and an even larger number of new initiatives and projects. Despite the emerging consolidation efforts, the area remains highly turbulent without systematization, educational materials, or cross-system comparative analysis. In this paper, we provide a systematic and comprehensive study of four popular yet widely different blockchain systems: Bitcoin, Ethereum, Hyperledger Fabric, and IOTA. The study is presented as a cross-system comparison, which is organized by clearly identified aspects: definitions, roles of the participants, entities, and the characteristics and design of each of the commonly used layers in the cross-system blockchain architecture. Our exploration goes deeper compared to what is currently available in academic surveys and tutorials. For example, we provide the first extensive coverage of the storage layer in Ethereum and the most comprehensive explanation of the consensus protocol in IOTA. The exposition is due to the consolidation of fragmented information gathered from white and yellow papers, academic publications, blogs, developer documentation, communication with the developers, as well as additional analysis gleaned from the source code. We hope that this survey will help the readers gain in-depth understanding of the design principles behind blockchain systems and contribute towards systematization of the area., Comment: 34 pages, 10 figures, 8 tables
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- 2022
345. Combinatorial properties of MAD families
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Brendle, Jörg, Guzmán, Osvaldo, Hrušák, Michael, and Raghavan, Dilip
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Mathematics - Logic ,Mathematics - General Topology - Abstract
We study some strong combinatorial properties of $\textsf{MAD}$ families. An ideal $\mathcal{I}$ is Shelah-Stepr\={a}ns if for every set $X\subseteq{\left[ \omega\right]}^{<\omega}$ there is an element of $\mathcal{I}$ that either intersects every set in $X$ or contains infinitely many members of it. We prove that a Borel ideal is Shelah-Stepr\={a}ns if and only if it is Kat\v{e}tov above the ideal $\textsf{fin}\times\textsf{fin}$. We prove that Shelah-Stepr\={a}ns $\textsf{MAD}$ families have strong indestructibility properties (in particular, they are both Cohen and random indestructible). We also consider some other strong combinatorial properties of $\textsf{MAD}$ families. Finally, it is proved that it is consistent to have $\mathrm{non}(\mathcal{M}) = {\aleph}_{1}$ and no Shelah-Stepr\={a}ns families of size ${\aleph}_{1}$., Comment: 43 pages. Submitted. arXiv admin note: text overlap with arXiv:1810.09680
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- 2022
346. Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators
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Kandaswamy, Indhumathi, Farkya, Saurabh, Daniels, Zachary, van der Wal, Gooitzen, Raghavan, Aswin, Zhang, Yuzheng, Hu, Jun, Lomnitz, Michael, Isnardi, Michael, Zhang, David, and Piacentino, Michael
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Hardware Architecture - Abstract
In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at the edge leveraging non-MAC (free of floating-point MultiplyACcumulate operations) deep neural nets (DNN) combined with hyperdimensional (HD) computing accelerators. We describe the algorithm, trained quantized model generation, and simulated performance of a feature extractor free of multiply-accumulates feeding a hyperdimensional logic-based classifier. Then we show how performance increases with the number of hyperdimensions. We describe the realized low-SWaP FPGA hardware and embedded software system compared to traditional DNNs and detail the implemented hardware accelerators. We discuss the measured system latency and power, noise robustness due to use of learnable quantization and HD computing, actual versus simulated system performance for a video activity classification task and demonstration of reconfiguration on this same dataset. We show that reconfigurability in the field is achieved by retraining only the feed-forward HD classifier without gradient descent backpropagation (gradient-free), using few-shot learning of new classes at the edge. Initial work performed used LRCN DNN and is currently extended to use Two-stream DNN with improved performance., Comment: 9 pages, 15 figures. Will be presented in Embedded Vision Workshop at CVPR2022
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- 2022
347. Saccade Mechanisms for Image Classification, Object Detection and Tracking
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Farkya, Saurabh, Daniels, Zachary, Raghavan, Aswin Nadamuni, Zhang, David, and Piacentino, Michael
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,Computer Science - Neural and Evolutionary Computing - Abstract
We examine how the saccade mechanism from biological vision can be used to make deep neural networks more efficient for classification and object detection problems. Our proposed approach is based on the ideas of attention-driven visual processing and saccades, miniature eye movements influenced by attention. We conduct experiments by analyzing: i) the robustness of different deep neural network (DNN) feature extractors to partially-sensed images for image classification and object detection, and ii) the utility of saccades in masking image patches for image classification and object tracking. Experiments with convolutional nets (ResNet-18) and transformer-based models (ViT, DETR, TransTrack) are conducted on several datasets (CIFAR-10, DAVSOD, MSCOCO, and MOT17). Our experiments show intelligent data reduction via learning to mimic human saccades when used in conjunction with state-of-the-art DNNs for classification, detection, and tracking tasks. We observed minimal drop in performance for the classification and detection tasks while only using about 30\% of the original sensor data. We discuss how the saccade mechanism can inform hardware design via ``in-pixel'' processing., Comment: 4 Pages, 6 figures, will be presented at CVPR2022-NeuroVision workshop as a Lightning talk
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- 2022
348. Learning to Ask Like a Physician
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Lehman, Eric, Lialin, Vladislav, Legaspi, Katelyn Y., Sy, Anne Janelle R., Pile, Patricia Therese S., Alberto, Nicole Rose I., Ragasa, Richard Raymund R., Puyat, Corinna Victoria M., Alberto, Isabelle Rose I., Alfonso, Pia Gabrielle I., Taliño, Marianne, Moukheiber, Dana, Wallace, Byron C., Rumshisky, Anna, Liang, Jenifer J., Raghavan, Preethi, Celi, Leo Anthony, and Szolovits, Peter
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Computer Science - Computation and Language - Abstract
Existing question answering (QA) datasets derived from electronic health records (EHR) are artificially generated and consequently fail to capture realistic physician information needs. We present Discharge Summary Clinical Questions (DiSCQ), a newly curated question dataset composed of 2,000+ questions paired with the snippets of text (triggers) that prompted each question. The questions are generated by medical experts from 100+ MIMIC-III discharge summaries. We analyze this dataset to characterize the types of information sought by medical experts. We also train baseline models for trigger detection and question generation (QG), paired with unsupervised answer retrieval over EHRs. Our baseline model is able to generate high quality questions in over 62% of cases when prompted with human selected triggers. We release this dataset (and all code to reproduce baseline model results) to facilitate further research into realistic clinical QA and QG: https://github.com/elehman16/discq.
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- 2022
349. LightCardiacNet: light-weight deep ensemble network with attention mechanism for cardiac sound classification
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Suma K. V., Deepali B. Koppad, Dharini Raghavan, and Manjunath P. R.
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Cardiovascular diseases ,neural networks ,sparsity ,gated recurrent units ,long short-term memory networks ,ensemble learning ,Control engineering systems. Automatic machinery (General) ,TJ212-225 ,Systems engineering ,TA168 - Abstract
Cardiovascular diseases (CVDs) account for about 32% of global deaths. While digital stethoscopes can record heart sounds, expert analysis is often lacking. To address this, we propose LightCardiacNet, an interpretable, lightweight ensemble neural network using Bi-Directional Gated Recurrent Units (Bi-GRU). It is trained on the PASCAL Heart Challenge and CirCor DigiScope datasets. Static network pruning enhances model sparsity for real-time deployment. We employ various data augmentation techniques to improve resilience to background noise. An ensemble of the two networks is constructed by employing a weighted average approach that combines the two light-weight attention Bi-GRU networks trained on different datasets, which outperforms several state-of-the-art networks achieving an accuracy of 99.8%, specificity of 99.6%, sensitivity of 95.2%, ROC-AUC of 0.974 and inference time of 17 ms on the PASCAL dataset, accuracy of 98.5%, specificity of 95.1%, sensitivity of 90.9%, ROC-AUC of 0.961 and inference time of 18 ms on the CirCor dataset, and an accuracy of 96.21%, sensitivity of 92.78%, specificity of 93.16%, ROC-AUC of 0.913 and inference time of 17.5 ms on real-world data. We adopt the SHAP algorithm to incorporate model interpretability and provide insights to make it clinically explainable and useful to healthcare professionals.
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- 2024
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350. Social support and (complex) posttraumatic stress symptom severity: does gender matter?
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Natalia E. Fares-Otero, Tamsin H. Sharp, Stefanie R. Balle, Sarah M. Quaatz, Eduard Vieta, Fredrik Åhs, Antje-Kathrin Allgaier, Adrián Arévalo, Rahel Bachem, Habte Belete, Tilahun Belete Mossie, Azi Berzengi, Necip Capraz, Deniz Ceylan, Daniel Dukes, Aziz Essadek, Naved Iqbal, Laura Jobson, Einat Levy-Gigi, Antonia Lüönd, Chantal Martin-Soelch, Tanja Michael, Misari Oe, Miranda Olff, Helena Örnkloo, Krithika Prakash, Muniarajan Ramakrishnan, Vijaya Raghavan, Vedat Şar, Soraya Seedat, Georgina Spies, Vandhana SusilKumar, Dany Laure Wadji, Rachel Wamser-Nanney, Shilat Haim-Nachum, Ulrich Schnyder, Marie R. Sopp, Monique C. Pfaltz, and Sarah L. Halligan
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PTSD ,complex PTSD ,social support ,sex ,gender ,adults ,Psychiatry ,RC435-571 - Abstract
Background: Perceived social support is an established predictor of post-traumatic stress disorder (PTSD) after exposure to a traumatic event. Gender is an important factor that could differentiate responses to social support, yet this has been little explored. Symptoms of complex PTSD are also common following trauma but have been under-researched in this context. Large scale studies with culturally diverse samples are particularly lacking.Objectives: In a multi-country sample, we examined: (a) gender differences in perceived social support and both posttraumatic stress symptom severity (PTSS) and complex posttraumatic stress symptom severity (CPTSS); (b) associations between social support and PTSS/CPTSS; and (c) the potential moderating role of gender in the relationship between perceived social support and trauma-related distress.Method: A total of 2483 adults (Mage = 30yrs, 69.9% females) from 39 countries, who had been exposed to mixed trauma types, completed the Multidimensional Scale of Perceived Social Support and the International Trauma Questionnaire (which captures PTSS/CPTSS). Regression analyses examined associations between gender, perceived social support, and PTSS/CPTSS; and tested for gender by social support interactions in predicting PTSS/CPTSS scores. Models were adjusted for age and socioeconomic status.Results: In our cross-country sample, females had greater PTSS/CPTSS than males (B = .23 [95% CI 0.16, 0.30], p
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- 2024
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