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BackgroundIn 2011, the American Board of Medical Specialties established clinical informatics (CI) as a subspecialty in medicine, jointly administered by the American Board of Pathology and the American Board of Preventive Medicine. Subsequently, many institutions created CI fellowship training programs to meet the growing need for informaticists. Although many programs share similar features, there is considerable variation in program funding and administrative structures.ObjectivesThe aim of our study was to characterize CI fellowship program features, including governance structures, funding sources, and expenses.MethodsWe created a cross-sectional online REDCap survey with 44 items requesting information on program administration, fellows, administrative support, funding sources, and expenses. We surveyed program directors of programs accredited by the Accreditation Council for Graduate Medical Education between 2014 and 2021.ResultsWe invited 54 program directors, of which 41 (76%) completed the survey. The average administrative support received was $27,732/year. Most programs (85.4%) were accredited to have two or more fellows per year. Programs were administratively housed under six departments: Internal Medicine (17; 41.5%), Pediatrics (7; 17.1%), Pathology (6; 14.6%), Family Medicine (6; 14.6%), Emergency Medicine (4; 9.8%), and Anesthesiology (1; 2.4%). Funding sources for CI fellowship program directors included: hospital or health systems (28.3%), clinical departments (28.3%), graduate medical education office (13.2%), biomedical informatics department (9.4%), hospital information technology (9.4%), research and grants (7.5%), and other sources (3.8%) that included philanthropy and external entities.ConclusionCI fellowships have been established in leading academic and community health care systems across the country. Due to their unique training requirements, these programs require significant resources for education, administration, and recruitment. T

Ensuring that pharmaceutical industry products and services fulfil the necessary quality standards is known as pharmaceutical quality assurance. By spotting and stopping flaws early on, it aims to build and maintain consumer confidence in the product. Within the pharmaceutical sector, quality assurance is a continuous process based on a detailed analysis of the needs and expectations of the customer. Different strategies can be used to assist with this process, but it's crucial to minimise extra costs for the business. Furthermore, pharmaceutical quality assurance's main goal is to reduce costs by upholding industry norms and pertinent regulations while maintaining high standards of quality.The current Pharmaceutical Quality Assurance book was written in accordance with the Pharmacy Council of India's (PCI) revised B. Pharmacy syllabus. This book's comprehensive coverage encompasses the broader facets of pharmaceutical quality assurance needed by postgraduates, undergrads, industry professionals, researchers, and students getting ready for different competitive exams. This book stands out for being written in an understandable, straightforward, and clear style.Links to websites and recommended reading are provided to assist readers in staying up to date on the most recent advancements in the field of pharmaceutical quality assurance. Introduction, background, objectives, principles, elements, description, organisation, concepts, and documentation have all been thoroughly divided into each chapter. According to the most recent revisions, the book has been written with graduate and postgraduate students' needs, teachers' needs, and research scientists' needs in mind.

A Cryptocurrency known as Bitcoin (BTC) is intended to function as money and a mode of payment independent of any one individual, organisation, or body. This eliminates the requirement for financial transactions to involve a reliable third party (like a bank or mint). It can be acquired on a number of exchanges and is given to blockchain miners as compensation for validating transactions. A cryptocurrency is a digital medium of exchange similar to the US dollar that controls the creation of new monetary units and verifies the transfer of funds using cryptographic techniques and protocol. Bitcoin and Cryptocurrency provide a comprehensive introduction to the revolutionary yet often misunderstood new technologies of digital currency. Whether you are a student, software developer, tech entrepreneur, or researcher in computer science, this authoritative and self-contained book tells you everything you need to know about the new global money for the Internet age. Following chapters are included in this book: 1. Bitcoin2. Bitcoin Digital Currency: Advantages & Disadvantages3. Decrypting Crypto Tax4. Cryptocurrency, Bitcoin, And the Future- A Conceptual Study5. Decrypting Dangers: Exploring the Nexus Between Cryptocurrency and Financial Crime6. Bitcoin: Economics, Technology, and Governance7. Online Payment Opportunities: PayPal and Bitcoin8. Power, Property, and Freedom: Bitcoin Discourse9. Cryptocurrency and Banking: Challenges, Considerations, and Disruptions - A Study 10. Cryptocurrency and its impact on the Banking Industry11. Advancing Technologies in Commerce for Sustainable Practices12. Cryptocurrency and Financial Markets

This study investigates the influence of IFRS enforcement on the explanatory power of earnings and book value on the market price in an emerging economy, India over five years that is two years before mandatory IFRS enforcement for 2014-2015 and three years after IFRS enforcement for 2016- 2018. Total 3470 firm-year observations were examined by assembling data from the BSE website and the annual reports of the firms for this purpose. The findings of the study conclude that the financial reporting environment has improved after introducing the IFRS, which was reflected through a strong and significant association between market price and its two dominating variables earnings and book values. However, the findings also suggest that earnings have been stronger and more significant predictors of market price post-IFRS enforcement than book values, which showed a negative association with market price and declined after IFRS enforcement. The study also suffers from diverse constraints similar to previous studies such as limited data sets and years coupled with only two variables and suggests future scope for research in diverse angles where researchers can employ the return model, and add more control variables such as cash flows, leverage, size, growth..., Este estudio investiga la influencia de la aplicación de las NIIF en el poder explicativo de las ganancias y el valor contable sobre el precio de mercado en una economía emergente, India, durante cinco años, es decir, dos años antes de la aplicación obligatoria de las NIIF para 2014-2015 y tres años después de la aplicación de las NIIF para 2016. - 2018. Se examinaron un total de 3470 observaciones por año de empresa reuniendo datos del sitio web de la BSE y los informes anuales de las empresas para este fin. Los hallazgos del estudio concluyen que el entorno de información financiera ha mejorado después de la introducción de las NIIF, lo que se reflejó a través de una asociación fuerte y significativa entre el precio de mercado y sus dos variables dominantes, las ganancias y los valores contables. Sin embargo, los hallazgos también sugieren que las ganancias han sido predictores más sólidos y significativos del precio de mercado después de la aplicación de las NIIF que los valores contables, que mostraron una asociación negativa con el precio de mercado y disminuyeron después de la aplicación de las NIIF. El estudio también adolece de diversas limitaciones similares a estudios anteriores, como conjuntos de datos limitados y años combinados con solo dos variables, y sugiere un margen futuro para la investigación en diversos ángulos donde los investigadores pueden emplear el modelo de retorno y agregar más variables de control como flujos de efectivo. apalancamiento, tamaño, crecimiento...

An experiment was conducted to study the bioefficacy of six different insecticides (thiamethoxam 25 WG @ 0.01%, thiacloprid 21.7 SC @ 0.0032%, buprofezin 25EC @ 0.05%, spinosad 48 SC @ 0.017%, chlorantraniliprole 18.5 SC @ 0.007% and fenpropathrin 30 EC @ 0.01%) with a control (water spray) against sucking pests, viz., shoot and fruit borers, jassids, whiteflies and aphids, and harvest time residues in brinjal (Surti Ravaiya) at the College Farm, North Asian University, and Navsari during summer-2020. Among the different insecticides evaluated, chlorantraniliprole and spinosad were the most effective, fenpropathrin and buprofezin were the least effective insecticides against shoot and fruit borers, respectively. Thiamethoxam and thiacloprid were the most effective agents. Fenpropathrin and buprofezin were the least effective against whiteflies, jassids and aphids. After two hours of spray, the initial deposition order of the experimental insecticides was buprofezin (10.382 mg/kg) > fenpropathrin (5.602 mg/kg) > chlorantraniliprole (5.097 mg/kg) > thiamethoxam (4.886 mg/kg) > spinosad (3.984 mg/kg) > thiacloprid (3.332 mg/kg), while after 4 days of spray, the residue status of the brinjal fruit was in the order of fenpropathrin (0.539 mg/kg) > chlorantraniliprole (0.154 mg/kg) > thiacloprid (0.127 mg/kg) > thiamethoxam (0.099 mg/kg) > buprofezin (0.076 mg/kg) > spinosad (BDL). Overall, the highest percentage degradation of insecticide residues in brinjal fruits was registered with spinosad (100%), followed by buprofezin (99.26%), thiamethoxam (97.97%), chlorantraniliprole (96.97%), thiacloprid (96.18%) and fenpropathrin (90.37%) after 4 days of II spray.

UNLABELLED: On 15 January 2022, Hunga volcano erupted, creating an extensive and high-reaching umbrella cloud over the open ocean, hindering traditional isopach mapping and fallout volume estimation. In MODIS satellite imagery, ocean surface water was discolored around Hunga following the eruption, which we attribute to ash fallout from the umbrella cloud. By relating intensity of ocean discoloration to fall deposit thicknesses in the Kingdom of Tonga, we develop a methodology for estimating airfall volume over the open ocean. Ash thickness measurements from 41 locations are used to fit a linear relationship between ash thickness and ocean reflectance. This produces a minimum airfall volume estimate of 1.8-0.4+0.3 km3. The whole eruption produced > 6.3 km3 of uncompacted pyroclastic material on the seafloor and a caldera volume change of 6 km3 DRE. Our fall estimates are consistent with the interpretation that most of the seafloor deposits were emplaced by gravity currents rather than fall deposits. Our proposed method does not account for the largest grain sizes, so is thus a minimum estimate. However, this new ocean-discoloration method provides an airfall volume estimate consistent with other independent measures of the plume and is thus effective for rapidly estimating fallout volumes in future volcanic eruptions over oceans. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00445-024-01744-6.

Debdulal Chakraborty,1 Subhendu Boral,1 Tushar Kanti Sinha,1 Arnab Das,1 Angshuman Mukherjee,1 Saptorshi Majumdar,1 Ranabir Bhattacharya,2 Ritobroto Maitra3 1Department of Vitreoretinal Services, Disha Eye Hospitals, Kolkata, West Bengal, India; 2Department of MIS, Disha Eye Hospitals, Kolkata, West Bengal, India; 3Radical Health Tech, New Delhi, IndiaCorrespondence: Debdulal Chakraborty, Department of Vitreoretinal services; Disha Eye Hospitals, Kolkata, West Bengal, India, Tel +91 33 66360000, Email devdc@rediffmail.comPurpose: This study investigates the efficacy of transitioning patients with neovascular age-related macular degeneration (nAMD) from aflibercept (T1) to biosimilar ranibizumab (T2), an approach not previously documented in literature.Methods: In this multicenter observational study, patients over 50 years of age with nAMD were shifted from intravitreal aflibercept (IVI AFL) to biosimilar ranibizumab (B-RBZ) due to financial constraints. This study employed standardized ophthalmological methods to assess visual acuity (VA), central macular thickness (CMT), and subretinal and intraretinal fluid. Statistical analyses included paired t-tests, Wilcoxon signed-rank tests, and linear regression.Results: A total of 29 eyes (12 males and 17 females) were analyzed. Mean age was 72.55 ± 6.43 years. VA improved significantly during T1, with a mean increase from 55.0 ± 10.2 to 70.0 ± 8.5 ETDRS letters at the switch time point (p < 0.01), then a slight decrease to 62.3 ± 8.9 at 12 months (p < 0.05) was noted during T2. The mean CMT decreased notably from 400 ± 50 to 290 ± 45 μm at the switch. The final CMT at 12 months after switching to B-RBZ was 280 ± 40 μm (p < 0.01). There was a significant decrease in the retinal and intra retinal fluid during T1, followed by a gradual increase during T2. A significant correlation (p < 0.05) was noted between the presence of intraretinal fluid and increased injection frequency of B-RBZ.Conclusion: The s

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

Background Intravenous prostate-specific membrane antigen (PSMA)-targeted radioligand therapy improves survival in men with metastatic castration-resistant prostate cancer. Yet, the impact of selective prostatic arterial administration on primary tumor uptake is unclear. Purpose To compare gallium 68 (68Ga)-PSMA-11 uptake using dynamic PET/CT in prostatic tumoral volumes of interest (VOIs) during intravenous and selective prostatic arterial infusions for individuals with untreated, high-risk prostate cancer. Materials and Methods In this prospective, intraindividual comparative study conducted at an academic medical center, five men aged 58, 61, 64, 66, and 68 years with treatment-naive prostate cancer were enrolled between January 2022 and February 2023 and underwent two dynamic 68Ga-PSMA-11 PET/CT examinations 1 week apart. During the first examination, the radiotracer was administered intravenously. During the second administration, the radiotracer was delivered into either the right or left prostatic artery through an angiographically placed microcatheter. The primary outcome was maximum standardized uptake value (SUVmax) in prostatic tumoral VOIs. The secondary outcomes included mean SUV (SUVmean) in prostatic tumoral VOIs and area under the SUVmean curves (AUC). Longitudinal mixed-effects models were used to compare dynamic SUVmax and SUVmean time-activity curves (TACs), and paired t tests were used for the remaining data. Results The mean SUVmax within tumoral VOIs was 14 (range, 3-43) for venous sessions and 938 (range, 460-1436) for arterial sessions (P = .008). The SUVmean within VOIs was greater during arterial sessions (P < .001) overall and 46-fold and 19-fold greater at peak uptake and final time points, respectively. The mean AUC was greater on arterial TACs than on venous TACs at 14600 SUV × min (range, 8353-20025 SUV × min) and 240 SUV × min (range, 69-622 SUV × min), respectively (P = .002). Conclusion Selective prostatic arterial infusion resul

Inherited retinal degenerative diseases (IRDs) are a group of rare diseases that lead to a progressive loss of photoreceptor cells and, ultimately, blindness. The overactivation of cGMP-dependent protein kinase G (PKG), one of the key effectors of cGMP-signaling, was previously found to be involved in photoreceptor cell death and was studied in murine IRD models to elucidate the pathophysiology of retinal degeneration. However, PKG is a serine/threonine kinase (STK) with several hundred potential phosphorylation targets and, so far, little is known about the specificity of the target interaction and downstream effects of PKG activation. Here, we carried out both the kinome activity and phosphoproteomic profiling of organotypic retinal explant cultures derived from the rd10 mouse model for IRD. After treating the explants with the PKG inhibitor CN03, an overall decrease in peptide phosphorylation was observed, with the most significant decrease occurring in seven peptides, including those from the known PKG substrate cyclic-AMP-response-element-binding CREB, but also Ca2+/calmodulin-dependent kinase (CaMK) peptides and TOP2A. The phosphoproteomic data, in turn, revealed proteins with decreased phosphorylation, as well as proteins with increased phosphorylation. The integration of both datasets identified common biological networks altered by PKG inhibition, which included kinases predominantly from the so-called AGC and CaMK families of kinases (e.g., PKG1, PKG2, PKA, CaMKs, RSKs, and AKTs). A pathway analysis confirmed the role of CREB, Calmodulin, mitogen-activated protein kinase (MAPK) and CREB modulation. Among the peptides and pathways that showed reduced phosphorylation activity, the substrates CREB, CaMK2, and CaMK4 were validated for their retinal localization and activity, using immunostaining and immunoblotting in the rd10 retina. In summary, the integrative analysis of the kinome activity and phosphoproteomic data revealed both known and novel PKG substrat

Development Strategies and Governance (DSG); Natural Resources and Resilience (NRR), This paper explores the relationship between agriculture, dietary diversity, and market access in Nepal, testing the complex causal chains involved, and the nuanced connections between production diversity and dietary diversity among smallholder farmers. While diversifying farm production could enhance dietary diversity, the case of Nepal indicates a varied and context specific relationship. Market access emerges as a crucial factor, often exerting a more significant impact on smallholder farm households than production diversity. Access to markets not only influences economic viability but also contributes directly to food and nutrition security, offering a practical solution to address dietary needs. Focusing on Nepal's diverse terrain, the study analyzes the interplay of remoteness, market access, irrigation availability, and complementary inputs in shaping farmers' decisions, providing valuable insights into sustainable agricultural strategies for improved dietary outcomes in low- and middle-income countries.

Background: Antibiotics are frequently utilized in livestock, particularly poultry, for therapy and growth promotion, resulting in antimicrobial resistance. Multidrug-resistant bacteria are frequent in poultry samples from India. The purpose of this study was to better understand main antibiotic consumption patterns in poultry value chains, as well as antibiotic knowledge and practices among the stakeholders. Methods: A cross-sectional survey was conducted in Assam and Karnataka, India. The poultry farmers were interviewed on antibiotic usage, antibiotic knowledge, feeding practices, and preventive measures on the farm. Poultry farmers reported their veterinarians, and we also interviewed them on knowledge and practices related to antimicrobial use in poultry and antimicrobial resistance. Item response theory (IRT) was used to assess the association between the answers and demographic factors. Results: This survey interviewed 62 poultry farmers and 11 veterinarians. Small poultry farms with fewer than 4000 birds were owned by 51.6% of farmers. Most poultry farmers had heard about antibiotics, and 62.9% thought they cured all diseases. If one chicken is sick, 72.6% said others should be given antibiotics to prevent the disease. All veterinarians utilized tetracyclines, aminoglycosides, and cephalexin on the poultry farms. Over half (54.5%) stated antibiotics prevent diseases, and 72.7% said they treat and prevent diseases. Some (45.5%) said antibiotics boost growth. IRT analysis showed that 8 questions assessed a knowledge scale well. Univariable analysis showed that Assam farmers and women were likely to have have more knowledge. Conclusion: The poultry farmers were mostly unaware of the relation between antibiotic use and antimicrobial resistance. Despite being aware, the veterinarians agreed with use antibiotics as a prophylactic measure. It is vital that these stakeholders understand the repercussions of such widespread antibiotic use. In order to increase knowle

Background: Antibiotics are frequently utilized in livestock, particularly poultry, for therapy and growth promotion, resulting in antimicrobial resistance. Multidrug-resistant bacteria are frequent in poultry samples from India. The purpose of this study was to better understand main antibiotic consumption patterns in poultry value chains, as well as antibiotic knowledge and practices among the stakeholders. Methods: A cross-sectional survey was conducted in Assam and Karnataka, India. The poultry farmers were interviewed on antibiotic usage, antibiotic knowledge, feeding practices, and preventive measures on the farm. Poultry farmers reported their veterinarians, and we also interviewed them on knowledge and practices related to antimicrobial use in poultry and antimicrobial resistance. Item response theory (IRT) was used to assess the association between the answers and demographic factors. Results: This survey interviewed 62 poultry farmers and 11 veterinarians. Small poultry farms with fewer than 4000 birds were owned by 51.6% of farmers. Most poultry farmers had heard about antibiotics, and 62.9% thought they cured all diseases. If one chicken is sick, 72.6% said others should be given antibiotics to prevent the disease. All veterinarians utilized tetracyclines, aminoglycosides, and cephalexin on the poultry farms. Over half (54.5%) stated antibiotics prevent diseases, and 72.7% said they treat and prevent diseases. Some (45.5%) said antibiotics boost growth. IRT analysis showed that 8 questions assessed a knowledge scale well. Univariable analysis showed that Assam farmers and women were likely to have have more knowledge. Conclusion: The poultry farmers were mostly unaware of the relation between antibiotic use and antimicrobial resistance. Despite being aware, the veterinarians agreed with use antibiotics as a prophylactic measure. It is vital that these stakeholders understand the repercussions of such widespread antibiotic use. In order to increase knowle

Using seawater to generate green hydrogen through electrolysis is a promising strategy for energy conversion. However, direct seawater splitting to form green hydrogen suffers drawbacks from electrode corrosion due to chlorine and other impurities. Herein, we demonstrate direct electrochemical seawater splitting using a forward osmosis membrane coupled with an electrolysis cell. By using this cell, high activity (270 mV at 10 mA/cm(2)) and decent stability (up to 6 days) are achieved by utilizing RuO2-(Ni,Co)(3)O-4 catalyst in a neutral electrolyte. This system is further studied in various electrolytes under neutral to alkaline conditions. This proof of concept shows that seawater splitting could be coupled with semipermeable membranes, allowing for direct utilization of seawater without pretreatment or purification and evading the challenges posed by impurities.

Background: Antibiotics are frequently utilized in livestock, particularly poultry, for therapy and growth promotion, resulting in antimicrobial resistance. Multidrug-resistant bacteria are frequent in poultry samples from India. The purpose of this study was to better understand main antibiotic consumption patterns in poultry value chains, as well as antibiotic knowledge and practices among the stakeholders. Methods: A cross-sectional survey was conducted in Assam and Karnataka, India. The poultry farmers were interviewed on antibiotic usage, antibiotic knowledge, feeding practices, and preventive measures on the farm. Poultry farmers reported their veterinarians, and we also interviewed them on knowledge and practices related to antimicrobial use in poultry and antimicrobial resistance. Item response theory (IRT) was used to assess the association between the answers and demographic factors. Results: This survey interviewed 62 poultry farmers and 11 veterinarians. Small poultry farms with fewer than 4000 birds were owned by 51.6% of farmers. Most poultry farmers had heard about antibiotics, and 62.9% thought they cured all diseases. If one chicken is sick, 72.6% said others should be given antibiotics to prevent the disease. All veterinarians utilized tetracyclines, aminoglycosides, and cephalexin on the poultry farms. Over half (54.5%) stated antibiotics prevent diseases, and 72.7% said they treat and prevent diseases. Some (45.5%) said antibiotics boost growth. IRT analysis showed that 8 questions assessed a knowledge scale well. Univariable analysis showed that Assam farmers and women were likely to have have more knowledge. Conclusion: The poultry farmers were mostly unaware of the relation between antibiotic use and antimicrobial resistance. Despite being aware, the veterinarians agreed with use antibiotics as a prophylactic measure. It is vital that these stakeholders understand the repercussions of such widespread antibiotic use. In order to increase knowle

Indiscriminate use of mineral fertilizers has a broad negative impact on soil health. Because of the above, there is an urgent need to search for natural organic alternatives, including using soil microbial resources to replenish soil nutrients for enhanced Agri productivity vis a vis sustainably maintaining soil health. The nitrogen-fixing rhizobacteria (NFR) are such type of bacteria which fix gaseous atmospheric nitrogen in the soil and in nodules of certain plant species in considerable amounts that are readily available for plants' uptake and may be considered as a viable alternative source of mineral nitrogen application. The present study was conducted to isolate the most potent nitrogen-fixing bacteria from the litchi rhizosphere. Hence, Five NFR (NFR1 to NFR5) were isolated from the rhizosphere of litchi orchard of Bihar Agricultural University, Sabour, Bhgalpur, India, based on their ability to fix atmospheric nitrogen in a nitrogen-free mineral salt medium. NFR2 was found to be the most potent in fixing atmospheric nitrogen (11.31 mg N per gram carbon source)among all the isolated rhizobacterial strains. Hence, on the basis of biological nitrogen fixation ability, the isolate NFR2 was subjected to 16S ribosomal RNA (16S rRNA) gene sequencing for molecular characterization. Based on 16S rDNA sequence analysis, NFR2 showed the closest sequence homology with Beijerinckia fluminensis and was identified and reported as Beijerinckia fluminensis strain BAUMS11, Accession number MN533953. The study noticeably indicated that the B. fluminensis strain BAUMS11 was found most efficient in fixing gaseous atmospheric nitrogen and may be used for the manufacturing of nitrogenous biofertilizer, which can fix atmospheric nitrogen to the tune-up to 30 kg N ha-1yr-1.

California's housing and real estate market is one of the most valuable markets in the USA. Many shareholders such as individual homebuyers, home sellers, real estate agents, lenders, and policymakers depend on high-volume information regarding the dynamics at work and their correct estimation. The research project aimed at developing an Explainable AI machine-learning model for California house price predictions. Data on house prices were collected from reliable sources such as California home estate websites, land sites, and public datasets. Features of the data included location, size, number of rooms, area type, availability, sale prices, and oceanic proximity. In this research project, credible, proven, and renowned machine learning algorithms were used most notably, Linear Regression analysis, XG-Boost, and Random Forest. The Random Forest came up quite impressively with a superior accuracy score and low MAE and MSE; thus, it was good for learning the underlying best patterns and relationships that may exist within the data for house price predictions. XG-Boost also did relatively well, showcasing moderately high accuracy and relatively low MSE and MAE, compared to the Linear Regression.

Background: Antibiotics are frequently utilized in livestock, particularly poultry, for therapy and growth promotion, resulting in antimicrobial resistance. Multidrug-resistant bacteria are frequent in poultry samples from India. The purpose of this study was to better understand main antibiotic consumption patterns in poultry value chains, as well as antibiotic knowledge and practices among the stakeholders. Methods: A cross-sectional survey was conducted in Assam and Karnataka, India. The poultry farmers were interviewed on antibiotic usage, antibiotic knowledge, feeding practices, and preventive measures on the farm. Poultry farmers reported their veterinarians, and we also interviewed them on knowledge and practices related to antimicrobial use in poultry and antimicrobial resistance. Item response theory (IRT) was used to assess the association between the answers and demographic factors. Results: This survey interviewed 62 poultry farmers and 11 veterinarians. Small poultry farms with fewer than 4000 birds were owned by 51.6% of farmers. Most poultry farmers had heard about antibiotics, and 62.9% thought they cured all diseases. If one chicken is sick, 72.6% said others should be given antibiotics to prevent the disease. All veterinarians utilized tetracyclines, aminoglycosides, and cephalexin on the poultry farms. Over half (54.5%) stated antibiotics prevent diseases, and 72.7% said they treat and prevent diseases. Some (45.5%) said antibiotics boost growth. IRT analysis showed that 8 questions assessed a knowledge scale well. Univariable analysis showed that Assam farmers and women were likely to have have more knowledge. Conclusion: The poultry farmers were mostly unaware of the relation between antibiotic use and antimicrobial resistance. Despite being aware, the veterinarians agreed with use antibiotics as a prophylactic measure. It is vital that these stakeholders understand the repercussions of such widespread antibiotic use. In order to increase knowle

The nccrFOAM suite is a collection of nonlinear coupled constitutive relation (NCCR) solvers for rarefied and microscale gas flows with vibrational non-equilibrium, in conjunction with the conservation laws implemented in the foam-extend framework which is an open-source solver with a General Public License (GPL 3). nccrFOAM solvers are developed as an extension to the dbnsTurbFoam solver by implementing additional algebraic constitutive relations for the non-conserved quantities of the stress tensor and heat flux vector. In contrast to Navier-Stokes-Fourier (NSF) solvers that employ first-order constitutive relations to calculate non-conserved quantities and consequently suffer from obvious shortcomings when simulating gas flows in high non-equilibrium, the second-order NCCR framework presents a novel alternative to simulate rarefied and microscale gas flows in a better and a more intuitive manner. In addition to the solver for monoatomic gases, the solvers are for the first time implemented for diatomic and polyatomic gases with translational-rotational and vibrational degrees of freedom based on second-order constitutive models in a three-dimensional framework. Towards this, a new foam-extend library has been written based on the two-temperature formulation to handle the translational-rotational and vibrational modes. The new foam-extend solver was validated for several representative problems, and an exhaustive list of tutorials is documented. The solver will certainly benefit the rarefied and microscale gas dynamics and hypersonics communities at large interested in flows involving high degrees of rarefaction, speed, and temperature variations.

The adverse effects of mounting environmental challenges, including extreme temperatures, threaten the global food supply due to their impact on plant growth and productivity. Temperature extremes disrupt plant genetics, leading to significant growth issues and eventually damaging phenotypes. Plants have developed complex signaling networks to respond and tolerate temperature stimuli, including genetic, physiological, biochemical, and molecular adaptations. In recent decades, omics tools and other molecular strategies have rapidly advanced, offering crucial insights and a wealth of information about how plants respond and adapt to stress. This review explores the potential of an integrated omics-driven approach to understanding how plants adapt and tolerate extreme temperatures. By leveraging cutting-edge omics methods, including genomics, transcriptomics, proteomics, metabolomics, miRNAomics, epigenomics, phenomics, and ionomics, alongside the power of machine learning and speed breeding data, we can revolutionize plant breeding practices. These advanced techniques offer a promising pathway to developing climate-proof plant varieties that can withstand temperature fluctuations, addressing the increasing global demand for high-quality food in the face of a changing climate.

BACKGROUND: The effect of contact force (CF) on lesion formation is not clear during pulsed field ablation (PFA). The aim of this study was to evaluate the impact of CF, PFA, and their interplay through the PFA index (PF index) formula on the ventricular lesion size in swine. METHODS: PFA was delivered through the CF-sensing OMNYPULSE catheter. Predefined PFA applications (×3, ×6, ×9, and ×12) were delivered maintaining low (5-25 g), high (26-50 g), and very high (51-80 g) CFs. First, PFA lesions were evaluated on necropsy in 11 swine to investigate the impact of CF/PFA-and their integration in the PF index equation-on lesion size (study characterization). Then, 3 different PF index thresholds-300, 450, and 600-were tested in 6 swine to appraise the PF index accuracy to predict the ventricular lesion depth (study validation). RESULTS: In the study characterization data set, 111 PFA lesions were analyzed. CF was 32±17 g. The average lesion depth and width were 3.5±1.2 and 12.0±3.5 mm, respectively. More than CF and PFA dose alone, it was their combined effect to impact lesion depth through an asymptotically increasing relationship. Likewise, not only was the PF index related to lesion depth in the study validation data set (r2=0.66; P<0.001) but it also provided a prediction accuracy of the observed depth of ±2 mm in 69/73 lesions (95%). CONCLUSIONS: CF and PFA applications play a key role in lesion formation during PFA. Further studies are required to evaluate the best PFA ablation settings to achieve transmural lesions.

The concept of the peer-to-peer local energy market (P2P LEM) is no longer novel to the energy community. Yet, its large-scale implementation within the current electricity network remains a complex challenge. One key reason is the lack of understanding of the supplier licensing models in different countries. For instance, in the UK, up to year 2023, a consumer is only allowed to have a single supplier at a time under its single licence supplier model. This directly contradicts the existing P2P trading models that allow a consumer to purchase electricity from multiple sellers within the local market. Given this context, this article conducts a review of recent literature and government policies in different countries on the P2P LEM and identifies the barriers behind the lack of large-scale P2P trading implementation in today's electricity markets. We explain how these barriers can be overcome by engaging prosumers in traditional and private distribution networks through either licensed or license-exempt suppliers. Particularly, we discuss six P2P LEM frameworks that can be utilised to address the supplier licensing issue. Finally, this review presents a summary of risks, and recommendations to aid the regulatory framework to implement P2P LEM.

In car-dominated urban areas, shared electric micro-mobility offers a sustainable alternative to decarbonise and reshape mobility paradigms. This paper presents a comprehensive framework for evaluating Dublin's e-bike sharing system, comprising 12 stations (eHUBs). Using six months of real-world riding data, it employs data envelopment analysis to assess the eco-efficiency of each eHUB in utilising nearby infrastructure, population in the catchment area, and location to achieve desired economic, social, and environmental outputs. Results indicate an upward trajectory for the system's eco-efficiency. The returns-to-scale analysis provides insights into the system's scalability, suggesting that expanding the e-bike sharing system, along with infrastructural enhancements, would significantly increase ridership. Analysing decarbonisation, usage patterns, and spatial factors of eHUBs reveals the substantial potential of shared e-bikes if optimally used. The research highlights the need to reevaluate car-centric policies in favour of more inclusive and environmentally sustainable alternatives and proposes actionable policy recommendations to achieve this transformation.

The comparison between aluminum and conventional formwork in construction is crucial for determining their impact on cost and time factors. Therefore, a study analyzing the cost and time factors of both aluminum and conventional formwork is essential for making informed decisions in construction projects. This paper compares formwork systems based on cost and time factors using a case study.

Iran is a developing country with low levels of economic development and globalization and is ruled by a theocratic government. To address the lack of national research on well-being in Iran, this retrospective observational study aims to examine life satisfaction and its main determinants among Iranian adults. Using World Gallup Poll data collected between 2006 and 2017, we examined life satisfaction as a cognitive aspect of subjective well-being in relation to various factors. Our results show that income is the strongest predictor of life satisfaction, followed by standard of living, gender, social support, age, negative affect, and education. In developing countries such as Iran, which face significant economic, political, and social challenges, individuals prioritize the satisfaction of basic needs by emphasizing factors such as the socioeconomic status. In contrast, developed countries with established welfare systems may emphasize other values such as social connections and healthy lifestyle behaviors as key factors in life satisfaction. This study contributes to a deeper understanding of the determinants of life satisfaction in Iran and provides insights for future research and policy making.

Iran is a developing country with low levels of economic development and globalization and is ruled by a theocratic government. To address the lack of national research on well-being in Iran, this retrospective observational study aims to examine life satisfaction and its main determinants among Iranian adults. Using World Gallup Poll data collected between 2006 and 2017, we examined life satisfaction as a cognitive aspect of subjective well-being in relation to various factors. Our results show that income is the strongest predictor of life satisfaction, followed by standard of living, gender, social support, age, negative affect, and education. In developing countries such as Iran, which face significant economic, political, and social challenges, individuals prioritize the satisfaction of basic needs by emphasizing factors such as the socioeconomic status. In contrast, developed countries with established welfare systems may emphasize other values such as social connections and healthy lifestyle behaviors as key factors in life satisfaction. This study contributes to a deeper understanding of the determinants of life satisfaction in Iran and provides insights for future research and policy making.

Flash droughts, characterized by rapid onset and intensification, are increasingly occurring as a consequence of climate change and rising temperatures. However, existing hydrometeorological definitions fail to encompass the full range of flash droughts, many of which have distinct local physical attributes. Consequently, these events often go undetected or unforecast in generic global flash drought assessments and are underrepresented in research. To address this gap, we conducted a comprehensive survey to gather information on local nomenclature, characteristics, and impacts of flash droughts worldwide. The survey revealed the widespread occurrence of these phenomena, highlighting their underre-searched nature. By analyzing case studies, through literature review often in local languages to unearth elusive studies, we identified five different types of flash droughts based on their specific characteristics. Our study aims to increase awareness about the complexity and diverse impacts of flash droughts, emphasizing the importance of considering regional contexts and the vulnerability of affected populations. The reported impacts underscore the need for better integration of all flash drought types in drought research, monitoring, and management. Monitoring a combination of indicators is crucial for timely detection and response to this emerging and escalating threat.

AIM: Vascular and peritoneal access are essential elements for sustainability of chronic dialysis programs. Data on availability, patterns of use, funding models, and workforce for vascular and peritoneal accesses for dialysis at a global scale is limited. METHODS: An electronic survey of national leaders of nephrology societies, consumer representative organizations, and policymakers was conducted from July to September 2018. Questions focused on types of accesses used to initiate dialysis, funding for services, and availability of providers for access creation. RESULTS: Data from 167 countries were available. In 31 countries (25% of surveyed countries), >75% of patients initiated haemodialysis (HD) with a temporary catheter. Seven countries (5% of surveyed countries) had >75% of patients initiating HD with arteriovenous fistulas or grafts. Seven countries (5% of surveyed countries) had >75% of their patients starting HD with tunnelled dialysis catheters. 57% of low-income countries (LICs) had >75% of their patients initiating HD with a temporary catheter compared to 5% of high-income countries (HICs). Shortages of surgeons to create vascular access were reported in 91% of LIC compared to 46% in HIC. Approximately 95% of participating countries in the LIC category reported shortages of surgeons for peritoneal dialysis (PD) access compared to 26% in HIC. Public funding was available for central venous catheters, fistula/graft creation, and PD catheter surgery in 57%, 54% and 54% of countries, respectively. CONCLUSION: There is a substantial variation in the availability, funding, workforce, and utilization of vascular and peritoneal access for dialysis across countries regions, with major gaps in low-income countries.

Autodesk docs is a cloud-based document management system. For a long time, there was an opinion that subcontractors don’t require a document management system as main contractors or clients. However, an internal document management system is as important as an external system. A system was needed which would make relevant data available to all appropriate staff and sub-contractors, would be simple to use and administer, would track all communications including emails and issues, and would act as a single source of truth for a project. In this paper we will find how the Autodesk docs is used in construction industry.

The purpose of this paper is to understand the concept, of “Money Scripts” and evaluate the different typesof financial behaviour by measuring the relationship strength among the factors contributing to MoneyScripts/ Beliefs among Millennials in India. The primary data was collected based on the standardisedKlontz’s Money Script Inventory; based on a set of seventy-two questions categorised under four independentfactorial heads- Avoidance, Adoration, Status, and Constant Money Monitoring; obtained from two hundredand thirty-five respondents approximately. The common space between the variables was examined based onMultidimensional Scaling. Among the factors considered contributing to the Money Scripts of Millennials,‘Money Scripts’ were highly and positively correlated with ‘Money Status’. There were no significantassociations identified between Money Scripts and the demographic variables (age, gender, income level andoccupation) of millennials in India. ‘Money Avoiders’ were found moving too far distances, experiencinghigh levels of financial happiness and moderate frugality in savings. Both ‘Money Status’ and ‘MoneyVigilance’ type millennials were found very strange in their approach toward money actions. Persons withhigh ‘status’ type behaviour were longing not to save more, but looking for comforting themselves in thepresent moment. They were found deriving ‘financial happiness’ from their spending pattern and savingsmade, which was much better than those of ‘Vigilant’ Millennials. Those with ‘Money Worship’ thoughts,though good at saving high; were found the least to derive financial happiness.

Background: Antibiotics are frequently utilized in livestock, particularly poultry, for therapy and growth promotion, resulting in antimicrobial resistance. Multidrug-resistant bacteria are frequent in poultry samples from India. The purpose of this study was to better understand main antibiotic consumption patterns in poultry value chains, as well as antibiotic knowledge and practices among the stakeholders. Methods: A cross-sectional survey was conducted in Assam and Karnataka, India. The poultry farmers were interviewed on antibiotic usage, antibiotic knowledge, feeding practices, and preventive measures on the farm. Poultry farmers reported their veterinarians, and we also interviewed them on knowledge and practices related to antimicrobial use in poultry and antimicrobial resistance. Item response theory (IRT) was used to assess the association between the answers and demographic factors. Results: This survey interviewed 62 poultry farmers and 11 veterinarians. Small poultry farms with fewer than 4000 birds were owned by 51.6% of farmers. Most poultry farmers had heard about antibiotics, and 62.9% thought they cured all diseases. If one chicken is sick, 72.6% said others should be given antibiotics to prevent the disease. All veterinarians utilized tetracyclines, aminoglycosides, and cephalexin on the poultry farms. Over half (54.5%) stated antibiotics prevent diseases, and 72.7% said they treat and prevent diseases. Some (45.5%) said antibiotics boost growth. IRT analysis showed that 8 questions assessed a knowledge scale well. Univariable analysis showed that Assam farmers and women were likely to have have more knowledge. Conclusion: The poultry farmers were mostly unaware of the relation between antibiotic use and antimicrobial resistance. Despite being aware, the veterinarians agreed with use antibiotics as a prophylactic measure. It is vital that these stakeholders understand the repercussions of such widespread antibiotic use. In order to increase knowle

Iran is a developing country with low levels of economic development and globalization and is ruled by a theocratic government. To address the lack of national research on well-being in Iran, this retrospective observational study aims to examine life satisfaction and its main determinants among Iranian adults. Using World Gallup Poll data collected between 2006 and 2017, we examined life satisfaction as a cognitive aspect of subjective well-being in relation to various factors. Our results show that income is the strongest predictor of life satisfaction, followed by standard of living, gender, social support, age, negative affect, and education. In developing countries such as Iran, which face significant economic, political, and social challenges, individuals prioritize the satisfaction of basic needs by emphasizing factors such as the socioeconomic status. In contrast, developed countries with established welfare systems may emphasize other values such as social connections and healthy lifestyle behaviors as key factors in life satisfaction. This study contributes to a deeper understanding of the determinants of life satisfaction in Iran and provides insights for future research and policy making.

Supervised methods for 3D anatomy segmentation demonstrate superior performance but are often limited by the availability of annotated data. This limitation has led to a growing interest in self-supervised approaches in tandem with the abundance of available un-annotated data. Slice propagation has emerged as an self-supervised approach that leverages slice registration as a self-supervised task to achieve full anatomy segmentation with minimal supervision. This approach significantly reduces the need for domain expertise, time, and the cost associated with building fully annotated datasets required for training segmentation networks. However, this shift toward reduced supervision via deterministic networks raises concerns about the trustworthiness and reliability of predictions, especially when compared with more accurate supervised approaches. To address this concern, we propose the integration of calibrated uncertainty quantification (UQ) into slice propagation methods, providing insights into the model's predictive reliability and confidence levels. Incorporating uncertainty measures enhances user confidence in self-supervised approaches, thereby improving their practical applicability. We conducted experiments on three datasets for 3D abdominal segmentation using five UQ methods. The results illustrate that incorporating UQ improves not only model trustworthiness, but also segmentation accuracy. Furthermore, our analysis reveals various failure modes of slice propagation methods that might not be immediately apparent to end-users. This study opens up new research avenues to improve the accuracy and trustworthiness of slice propagation methods., Comment: 13 pages including Supplementary, 4 figures

In this work, we propose fully nonconforming, locally exactly divergence-free discretizations based on lowest order Crouziex-Raviart finite element and piecewise constant spaces to study the optimal control of stationary double diffusion model presented in [B\"urger, M\'endez, Ruiz-Baier, SINUM (2019), 57:1318-1343]. The well-posedness of the discrete uncontrolled state and adjoint equations are discussed using discrete lifting and fixed point arguments, and convergence results are derived rigorously under minimal regularity. Building upon our recent work [Tushar, Khan, Mohan arXiv (2023)], we prove the local optimality of a reference control using second-order sufficient optimality condition for the control problem, and use it along with an optimize-then-discretize approach to prove optimal order a priori error estimates for the control, state and adjoint variables upto the regularity of the solution. The optimal control is computed using a primal-dual active set strategy as a semi-smooth Newton method and computational tests validate the predicted error decay rates and illustrate the proposed scheme's applicability to optimal control of thermohaline circulation problems.

Statistical Shape Modeling (SSM) is an effective method for quantitatively analyzing anatomical variations within populations. However, its utility is limited by the need for manual segmentations of anatomies, a task that relies on the scarce expertise of medical professionals. Recent advances in deep learning have provided a promising approach that automatically generates statistical representations from unsegmented images. Once trained, these deep learning-based models eliminate the need for manual segmentation for new subjects. Nonetheless, most current methods still require manual pre-alignment of image volumes and specifying a bounding box around the target anatomy prior for inference, resulting in a partially manual inference process. Recent approaches facilitate anatomy localization but only estimate statistical representations at the population level. However, they cannot delineate anatomy directly in images and are limited to modeling a single anatomy. Here, we introduce MASSM, a novel end-to-end deep learning framework that simultaneously localizes multiple anatomies in an image, estimates population-level statistical representations, and delineates each anatomy. Our findings emphasize the crucial role of local correspondences, showcasing their indispensability in providing superior shape information for medical imaging tasks.

In recent advancements within the domain of Large Language Models (LLMs), there has been a notable emergence of agents capable of addressing Robotic Process Automation (RPA) challenges through enhanced cognitive capabilities and sophisticated reasoning. This development heralds a new era of scalability and human-like adaptability in goal attainment. In this context, we introduce AUTONODE (Autonomous User-interface Transformation through Online Neuro-graphic Operations and Deep Exploration). AUTONODE employs advanced neuro-graphical techniques to facilitate autonomous navigation and task execution on web interfaces, thereby obviating the necessity for predefined scripts or manual intervention. Our engine empowers agents to comprehend and implement complex workflows, adapting to dynamic web environments with unparalleled efficiency. Our methodology synergizes cognitive functionalities with robotic automation, endowing AUTONODE with the ability to learn from experience. We have integrated an exploratory module, DoRA (Discovery and mapping Operation for graph Retrieval Agent), which is instrumental in constructing a knowledge graph that the engine utilizes to optimize its actions and achieve objectives with minimal supervision. The versatility and efficacy of AUTONODE are demonstrated through a series of experiments, highlighting its proficiency in managing a diverse array of web-based tasks, ranging from data extraction to transaction processing., Comment: Accepted in MIPR-2024

Hematoxylin and Eosin (H&E) staining is the most commonly used for disease diagnosis and tumor recurrence tracking. Hematoxylin excels at highlighting nuclei, whereas eosin stains the cytoplasm. However, H&E stain lacks details for differentiating different types of cells relevant to identifying the grade of the disease or response to specific treatment variations. Pathologists require special immunohistochemical (IHC) stains that highlight different cell types. These stains help in accurately identifying different regions of disease growth and their interactions with the cell's microenvironment. The advent of deep learning models has made Image-to-Image (I2I) translation a key research area, reducing the need for expensive physical staining processes. Pix2Pix and CycleGAN are still the most commonly used methods for virtual staining applications. However, both suffer from hallucinations or staining irregularities when H&E stain has less discriminate information about the underlying cells IHC needs to highlight (e.g.,CD3 lymphocytes). Diffusion models are currently the state-of-the-art models for image generation and conditional generation tasks. However, they require extensive and diverse datasets (millions of samples) to converge, which is less feasible for virtual staining applications.Inspired by the success of multitask deep learning models for limited dataset size, we propose StainDiffuser, a novel multitask dual diffusion architecture for virtual staining that converges under a limited training budget. StainDiffuser trains two diffusion processes simultaneously: (a) generation of cell-specific IHC stain from H&E and (b) H&E-based cell segmentation using coarse segmentation only during training. Our results show that StainDiffuser produces high-quality results for easier (CK8/18,epithelial marker) and difficult stains(CD3, Lymphocytes).

The adoption of renewable energy resources, such as solar power, is on the rise. However, the excessive installation and lack of recycling facilities pose environmental risks. This paper suggests a circular economy approach to address the issue. By implementing blockchain technology, the end-of-life (EOL) of solar panels can be tracked, and responsibilities can be assigned to relevant stakeholders. The degradation of panels can be monetized by tracking users' energy-related activities, and these funds can be used for future recycling. A new coin, the recycling coin (RC-Coin), incentivizes solar panel recycling and utilizes decentralized finance to stabilize the coin price and supply issue., Comment: Accepted in IEEE Industrial Electronics Magazine

Transformers have emerged as the state-of-the-art architecture in medical image registration, outperforming convolutional neural networks (CNNs) by addressing their limited receptive fields and overcoming gradient instability in deeper models. Despite their success, transformer-based models require substantial resources for training, including data, memory, and computational power, which may restrict their applicability for end users with limited resources. In particular, existing transformer-based 3D image registration architectures face three critical gaps that challenge their efficiency and effectiveness. Firstly, while mitigating the quadratic complexity of full attention by focusing on local regions, window-based attention mechanisms often fail to adequately integrate local and global information. Secondly, feature similarities across attention heads that were recently found in multi-head attention architectures indicate a significant computational redundancy, suggesting that the capacity of the network could be better utilized to enhance performance. Lastly, the granularity of tokenization, a key factor in registration accuracy, presents a trade-off; smaller tokens improve detail capture at the cost of higher computational complexity, increased memory demands, and a risk of overfitting. Here, we propose EfficientMorph, a transformer-based architecture for unsupervised 3D image registration. It optimizes the balance between local and global attention through a plane-based attention mechanism, reduces computational redundancy via cascaded group attention, and captures fine details without compromising computational efficiency, thanks to a Hi-Res tokenization strategy complemented by merging operations. Notably, EfficientMorph sets a new benchmark for performance on the OASIS dataset with 16-27x fewer parameters.

Lately, researchers in artificial intelligence have been really interested in how language and vision come together, giving rise to the development of multimodal models that aim to seamlessly integrate textual and visual information. Multimodal models, an extension of Large Language Models (LLMs), have exhibited remarkable capabilities in addressing a diverse array of tasks, ranging from image captioning and visual question answering (VQA) to visual grounding. While these models have showcased significant advancements, challenges persist in accurately interpreting images and answering the question, a common occurrence in real-world scenarios. This paper introduces a novel approach to enhance the multimodal capabilities of existing models. In response to the limitations observed in current Vision Language Models (VLMs) and Multimodal Large Language Models (MLLMs), our proposed model Veagle, incorporates a unique mechanism inspired by the successes and insights of previous works. Veagle leverages a dynamic mechanism to project encoded visual information directly into the language model. This dynamic approach allows for a more nuanced understanding of intricate details present in visual contexts. To validate the effectiveness of Veagle, we conduct comprehensive experiments on benchmark datasets, emphasizing tasks such as visual question answering and image understanding. Our results indicate a improvement of 5-6 \% in performance, with Veagle outperforming existing models by a notable margin. The outcomes underscore the model's versatility and applicability beyond traditional benchmarks.

Exploiting sparsity in deep neural networks (DNNs) has been a promising area to meet the growing computation need of modern DNNs. However, in practice, sparse DNN acceleration still faces a key challenge. To minimize the overhead of sparse acceleration, hardware designers have proposed structured sparse hardware support recently, which provides limited flexibility and requires extra model fine-tuning. Moreover, any sparse model fine-tuned for certain structured sparse hardware cannot be accelerated by other structured hardware. To bridge the gap between sparse DNN models and hardware, this paper proposes tensor approximation via structured decomposition (TASD), which leverages the distributive property in linear algebra to turn any sparse tensor into a series of structured sparse tensors. Next, we develop a software framework, TASDER, to accelerate DNNs by searching layer-wise, high-quality structured decomposition for both weight and activation tensors so that they can be accelerated by any systems with structured sparse hardware support. Evaluation results show that, by exploiting prior structured sparse hardware baselines, our method can accelerate off-the-shelf dense and sparse DNNs without fine-tuning and improves energy-delay-product by up to 83% and 74% on average.

Traditional Deep Neural Network (DNN) quantization methods using integer, fixed-point, or floating-point data types struggle to capture diverse DNN parameter distributions at low precision, and often require large silicon overhead and intensive quantization-aware training. In this study, we introduce Logarithmic Posits (LP), an adaptive, hardware-friendly data type inspired by posits that dynamically adapts to DNN weight/activation distributions by parameterizing LP bit fields. We also develop a novel genetic-algorithm based framework, LP Quantization (LPQ), to find optimal layer-wise LP parameters while reducing representational divergence between quantized and full-precision models through a novel global-local contrastive objective. Additionally, we design a unified mixed-precision LP accelerator (LPA) architecture comprising of processing elements (PEs) incorporating LP in the computational datapath. Our algorithm-hardware co-design demonstrates on average <1% drop in top-1 accuracy across various CNN and ViT models. It also achieves ~ 2x improvements in performance per unit area and 2.2x gains in energy efficiency compared to state-of-the-art quantization accelerators using different data types., Comment: 2024 61st IEEE/ACM Design Automation Conference (DAC)

Key-value (KV) caching has become the de-facto to accelerate generation speed for large language models (LLMs) inference. However, the growing cache demand with increasing sequence length has transformed LLM inference to be a memory bound problem, significantly constraining the system throughput. Existing methods rely on dropping unimportant tokens or quantizing all entries uniformly. Such methods, however, often incur high approximation errors to represent the compressed matrices. The autoregressive decoding process further compounds the error of each step, resulting in critical deviation in model generation and deterioration of performance. To tackle this challenge, we propose GEAR, an efficient KV cache compression framework that achieves near-lossless high-ratio compression. GEAR first applies quantization to majority of entries of similar magnitudes to ultra-low precision. It then employs a low rank matrix to approximate the quantization error, and a sparse matrix to remedy individual errors from outlier entries. By adeptly integrating three techniques, GEAR is able to fully exploit their synergistic potentials. Our experiments demonstrate that compared to alternatives, GEAR achieves near-lossless 4-bit KV cache compression with up to 2.38x throughput improvement, while reducing peak-memory size up to 2.29x. Our code is publicly available at https://github.com/HaoKang-Timmy/GEAR.

As part of human core knowledge, the representation of objects is the building block of mental representation that supports high-level concepts and symbolic reasoning. While humans develop the ability of perceiving objects situated in 3D environments without supervision, models that learn the same set of abilities with similar constraints faced by human infants are lacking. Towards this end, we developed a novel network architecture that simultaneously learns to 1) segment objects from discrete images, 2) infer their 3D locations, and 3) perceive depth, all while using only information directly available to the brain as training data, namely: sequences of images and self-motion. The core idea is treating objects as latent causes of visual input which the brain uses to make efficient predictions of future scenes. This results in object representations being learned as an essential byproduct of learning to predict., Comment: 21 pages, 11 figures. Project webpage can be found at https://jday54.github.io/opple_site

Large language models (LLMs) are powerful tools for content moderation, but their inference costs and latency make them prohibitive for casual use on large datasets, such as the Google Ads repository. This study proposes a method for scaling up LLM reviews for content moderation in Google Ads. First, we use heuristics to select candidates via filtering and duplicate removal, and create clusters of ads for which we select one representative ad per cluster. We then use LLMs to review only the representative ads. Finally, we propagate the LLM decisions for the representative ads back to their clusters. This method reduces the number of reviews by more than 3 orders of magnitude while achieving a 2x recall compared to a baseline non-LLM model. The success of this approach is a strong function of the representations used in clustering and label propagation; we found that cross-modal similarity representations yield better results than uni-modal representations.

Most video captioning models are designed to process short video clips of few seconds and output text describing low-level visual concepts (e.g., objects, scenes, atomic actions). However, most real-world videos last for minutes or hours and have a complex hierarchical structure spanning different temporal granularities. We propose Video ReCap, a recursive video captioning model that can process video inputs of dramatically different lengths (from 1 second to 2 hours) and output video captions at multiple hierarchy levels. The recursive video-language architecture exploits the synergy between different video hierarchies and can process hour-long videos efficiently. We utilize a curriculum learning training scheme to learn the hierarchical structure of videos, starting from clip-level captions describing atomic actions, then focusing on segment-level descriptions, and concluding with generating summaries for hour-long videos. Furthermore, we introduce Ego4D-HCap dataset by augmenting Ego4D with 8,267 manually collected long-range video summaries. Our recursive model can flexibly generate captions at different hierarchy levels while also being useful for other complex video understanding tasks, such as VideoQA on EgoSchema. Data, code, and models are available at: https://sites.google.com/view/vidrecap, Comment: Accepted by CVPR 2024

The advancement of Large Language Models (LLM) has also resulted in an equivalent proliferation in its applications. Software design, being one, has gained tremendous benefits in using LLMs as an interface component that extends fixed user stories. However, inclusion of LLM-based AI agents in software design often poses unexpected challenges, especially in the estimation of development efforts. Through the example of UI-based user stories, we provide a comparison against traditional methods and propose a new way to enhance specifications of natural language-based questions that allows for the estimation of development effort by taking into account data sources, interfaces and algorithms.

N:M Structured sparsity has garnered significant interest as a result of relatively modest overhead and improved efficiency. Additionally, this form of sparsity holds considerable appeal for reducing the memory footprint owing to their modest representation overhead. There have been efforts to develop training recipes for N:M structured sparsity, they primarily focus on low-sparsity regions ($\sim$50\%). Nonetheless, performance of models trained using these approaches tends to decline when confronted with high-sparsity regions ($>$80\%). In this work, we study the effectiveness of existing sparse training recipes at \textit{high-sparsity regions} and argue that these methods fail to sustain the model quality on par with low-sparsity regions. We demonstrate that the significant factor contributing to this disparity is the presence of elevated levels of induced noise in the gradient magnitudes. To mitigate this undesirable effect, we employ decay mechanisms to progressively restrict the flow of gradients towards pruned elements. Our approach improves the model quality by up to 2$\%$ and 5$\%$ in vision and language models at high sparsity regime, respectively. We also evaluate the trade-off between model accuracy and training compute cost in terms of FLOPs. At iso-training FLOPs, our method yields better performance compared to conventional sparse training recipes, exhibiting an accuracy improvement of up to 2$\%$. The source code is available at https://github.com/abhibambhaniya/progressive_gradient_flow_nm_sparsity., Comment: 18 pages, 8 figures, 17 tables. Code is available at https://github.com/abhibambhaniya/progressive_gradient_flow_nm_sparsity