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Fu, Xuan, Jiangnan Du, Hai-Tao Zheng, Jianfeng Li, Cuiqin Hou, Qiyu Zhou, and Hong-Gee Kim. "SS-BERT: A Semantic Information Selecting Approach for Open-Domain Question Answering." Electronics 12, no. 7 (April 3, 2023): 1692. http://dx.doi.org/10.3390/electronics12071692.
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Open-Domain Question Answering (Open-Domain QA) aims to answer any factoid questions from users. Recent progress in Open-Domain QA adopts the “retriever-reader” structure, which has proven effective. Retriever methods are mainly categorized as sparse retrievers and dense retrievers. In recent work, the dense retriever showed a stronger semantic interpretation than the sparse retriever. When training a dual-encoder dense retriever for document retrieval and reranking, there are two challenges: negative selection and a lack of training data. In this study, we make three major contributions to this topic: negative selection by query generation, data augmentation from negatives, and a passage evaluation method. We prove that the model performs better by focusing on false negatives and data augmentation in the Open-Domain QA passage rerank task. Our model outperforms other single dual-encoder rerankers over BERT-base and BM25 by 0.7 in MRR@10, achieving the highest Recall@50 and the max Recall@1000, which is restricted by the BM25 retrieval results.
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Söderlund, Ella-Erika, Heikki Kyröläinen, Outi M. Laitinen-Vapaavuori, and Heli K. Hyytiäinen. "Proposed Protocol for Field Testing of Endurance Fitness of Young Labrador Retrievers." Methods and Protocols 6, no. 4 (June 28, 2023): 61. http://dx.doi.org/10.3390/mps6040061.
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The number of dogs and, with it, dog sports are growing in popularity, and the training of dogs begins at an early age. Although fitness testing is an imperative part of purposeful training and sports, to our knowledge, no objective field tests are available for measuring young dogs’ endurance fitness. The aim of this study is to describe a simple, easy-to-repeat, and inexpensive way to test training intervention effects on endurance fitness in young Labrador Retrievers. Healthy client-owned 16-week-old Labrador Retrievers will be recruited and divided into test and control groups. The test group will have an eight-week training program followed by a four-week detraining period, while the control group will live a normal puppy life. All dogs will be tested for endurance fitness four times at four-week intervals: at baseline, one month later, two months later at the end of the training period, and one month after ending the training program. Each of the four testing sessions will be identical and will consist of four measurements of heart rate (HR) and blood lactate (BL): at baseline, after trotting 1000 m, after sprinting 200 m, and at recovery 5–8 min after the sprint. The training-induced changes in endurance fitness are evaluated by changes in HR and heart rate recovery times (HRR), BL, and running times.
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Steiss, J., H. A. Ahmad, P. Cooper, and C. Ledford. "Physiologic Responses in Healthy Labrador Retrievers during Field Trial Training and Competition." Journal of Veterinary Internal Medicine 18, no. 2 (March 2004): 147–51. http://dx.doi.org/10.1111/j.1939-1676.2004.tb00153.x.
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Luo, Man, Arindam Mitra, Tejas Gokhale, and Chitta Baral. "Improving Biomedical Information Retrieval with Neural Retrievers." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11038–46. http://dx.doi.org/10.1609/aaai.v36i10.21352.
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Information retrieval (IR) is essential in search engines and dialogue systems as well as natural language processing tasks such as open-domain question answering. IR serve an important function in the biomedical domain, where content and sources of scientific knowledge may evolve rapidly. Although neural retrievers have surpassed traditional IR approaches such as TF-IDF and BM25 in standard open-domain question answering tasks, they are still found lacking in the biomedical domain. In this paper, we seek to improve information retrieval (IR) using neural retrievers (NR) in the biomedical domain, and achieve this goal using a three-pronged approach. First, to tackle the relative lack of data in the biomedical domain, we propose a template-based question generation method that can be leveraged to train neural retriever models. Second, we develop two novel pre-training tasks that are closely aligned to the downstream task of information retrieval. Third, we introduce the ``Poly-DPR'' model which encodes each context into multiple context vectors. Extensive experiments and analysis on the BioASQ challenge suggest that our proposed method leads to large gains over existing neural approaches and beats BM25 in the small-corpus setting. We show that BM25 and our method can complement each other, and a simple hybrid model leads to further gains in the large corpus setting.
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Boiocchi, S., L. Vezzoni, V. Bronzo, F. Rossi, and A. Vezzoni. "Radiographic changes of the pelvis in Labrador and Golden Retrievers after juvenile pubic symphysiodesis." Veterinary and Comparative Orthopaedics and Traumatology 26, no. 03 (2013): 218–25. http://dx.doi.org/10.3415/vcot-12-06-0074.
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SummaryObjectives: The hypothesis of this study was that juvenile pubic symphysiodesis (JPS) results in pelvic changes that can be identified radiographically in adult dogs.Methods: The medical records at the Clinica Veterinaria Vezzoni were searched for standard ventro-dorsal views of the pelvis of adult Labrador and Golden Retrievers that had undergone JPS or had not undergone surgery. The objective assessment of radiographs included the analysis of various pelvic measurements. Subjective evaluation of radiographs was undertaken by 18 specialists and 21 general practitioners and was based on five criteria relating to 1) the acetabular fossae, 2) the pubic symphysis, 3) the margin of the cranial pubic area, 4) the pubic rami, and 5) the obturator foramen.Results: The radiographs of 42 Labrador Retrievers and 16 Golden Retrievers were evaluated. The most useful criteria were the radiographic measurement of the shape of the obturator foramen and two different ratios of length to width of the pubic rami; these values were significantly smaller in dogs after JPS. The pelvic canal width was the same in both groups. All objective measurements were repeatable within and between evaluators. The most reliable subjective criterion was number 4, followed by number 5 in Golden Retrievers and by 2 in Labrador Retrievers.Conclusion: Our objective and subjective evaluations were simple and yielded useful and repeatable results. There was no significant difference between general practitioners and specialists with regard to subjective evaluation, which indicates that these evaluation criteria can be used by small animal clinicians after minimal training.
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Miller, Benjamin F., Sarah E. Ehrlicher, Joshua C. Drake, Frederick F. Peelor, Laurie M. Biela, Shannon Pratt-Phillips, Michael Davis, and Karyn L. Hamilton. "Assessment of protein synthesis in highly aerobic canine species at the onset and during exercise training." Journal of Applied Physiology 118, no. 7 (April 1, 2015): 811–17. http://dx.doi.org/10.1152/japplphysiol.00982.2014.
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Canis lupus familiaris, the domesticated dog, is capable of extreme endurance performance. The ability to perform sustained aerobic exercise is dependent on a well-developed mitochondrial reticulum. In this study we examined the cumulative muscle protein and DNA synthesis in groups of athletic dogs at the onset of an exercise training program and following a strenuous exercise training program. We hypothesized that both at the onset and during an exercise training program there would be greater mitochondrial protein synthesis rates compared with sedentary control with no difference in mixed or cytoplasmic protein synthesis rates. Protein synthetic rates of three protein fractions and DNA synthesis were determined over 1 wk using 2H2O in competitive Alaskan Huskies and Labrador Retrievers trained for explosive device detection. Both groups of dogs had very high rates of skeletal muscle protein synthesis in the sedentary state [Alaskan Huskies: Mixed = 2.28 ± 0.12, cytoplasmic (Cyto) = 2.91 ± 0.10, and mitochondrial (Mito) = 2.62 ± 0.07; Labrador Retrievers: Mixed = 3.88 ± 0.37, Cyto = 3.85 ± 0.06, and Mito = 2.92 ± 0.20%/day]. Mitochondrial (Mito) protein synthesis rates did not increase at the onset of an exercise training program. Exercise-trained dogs maintained Mito protein synthesis during exercise training when mixed (Mixed) and cytosolic (Cyto) fractions decreased, and this coincided with a decrease in p-RpS6 but also a decrease in p-ACC signaling. Contrary to our hypothesis, canines did not have large increases in mitochondrial protein synthesis at the onset or during an exercise training program. However, dogs have a high rate of protein synthesis compared with humans that perhaps does not necessitate an extra increase in protein synthesis at the onset of aerobic exercise training.
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Zhou, Yucheng, Tao Shen, Xiubo Geng, Chongyang Tao, Jianbing Shen, Guodong Long, Can Xu, and Daxin Jiang. "Fine-Grained Distillation for Long Document Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (March 24, 2024): 19732–40. http://dx.doi.org/10.1609/aaai.v38i17.29947.
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Long document retrieval aims to fetch query-relevant documents from a large-scale collection, where knowledge distillation has become de facto to improve a retriever by mimicking a heterogeneous yet powerful cross-encoder. However, in contrast to passages or sentences, retrieval on long documents suffers from the \textit{scope hypothesis} that a long document may cover multiple topics. This maximizes their structure heterogeneity and poses a granular-mismatch issue, leading to an inferior distillation efficacy. In this work, we propose a new learning framework, fine-grained distillation (FGD), for long-document retrievers. While preserving the conventional dense retrieval paradigm, it first produces global-consistent representations crossing different fine granularity and then applies multi-granular aligned distillation merely during training. In experiments, we evaluate our framework on two long-document retrieval benchmarks, which show state-of-the-art performance.
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Long, Xinwei, Jiali Zeng, Fandong Meng, Zhiyuan Ma, Kaiyan Zhang, Bowen Zhou, and Jie Zhou. "Generative Multi-Modal Knowledge Retrieval with Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (March 24, 2024): 18733–41. http://dx.doi.org/10.1609/aaai.v38i17.29837.
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Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-intensive multi-modal applications. However, existing methods face challenges in terms of their effectiveness and training efficiency, especially when it comes to training and integrating multiple retrievers to handle multi-modal queries. In this paper, we propose an innovative end-to-end generative framework for multi-modal knowledge retrieval. Our framework takes advantage of the fact that large language models (LLMs) can effectively serve as virtual knowledge bases, even when trained with limited data. We retrieve knowledge via a two-step process: 1) generating knowledge clues related to the queries, and 2) obtaining the relevant document by searching databases using the knowledge clue. In particular, we first introduce an object-aware prefix-tuning technique to guide multi-grained visual learning. Then, we align multi-grained visual features into the textual feature space of the LLM, employing the LLM to capture cross-modal interactions. Subsequently, we construct instruction data with a unified format for model training. Finally, we propose the knowledge-guided generation strategy to impose prior constraints in the decoding steps, thereby promoting the generation of distinctive knowledge clues. Through experiments conducted on three benchmarks, we demonstrate significant improvements ranging from 3.0% to 14.6% across all evaluation metrics when compared to strong baselines.
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Affenzeller, Nadja. "Dog–Human Play, but Not Resting Post-Learning Improve Re-Training Performance up to One Year after Initial Task Acquisition in Labrador Retriever Dogs: A Follow-On Study." Animals 10, no. 7 (July 21, 2020): 1235. http://dx.doi.org/10.3390/ani10071235.
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Arousing and emotional situations can improve cognitive performance and the memorability of events. Recently, the enhancement of training performance in Labrador Retriever dogs through 30 min of dog–human play immediately after acquiring a novel task, when compared to a resting period, was demonstrated. This follow-on study used the same pseudo-randomized, counterbalanced, between-subject study design, and 11 Labrador Retrievers were re-trained in the identical two-choice discrimination paradigm after a period of 1 year. The playful activities group needed significantly less trials and made significantly less errors to successfully reach the re-training criterion (Mann–Whitney U test, critical value of U at p < 0.05 is 5, U = 5, Z = 1.73, p = 0.04 and U = 4.5, Z = 1.8, p = 0.03, respectively). Following model simplification of a multiple factor/covariate general linear model analysis, the type of intervention, the number of trials needed to re-learn the task after 24 h, the average heart rate during the intervention a year ago, and age were significantly correlated to the number of trials and errors needed to resolve the task. A significant difference due to intervention allocation (heart rate during the intervention, trials needed to re-learn the task after 24 h) between the groups was confirmed. Age did not significantly differ between the groups; nevertheless, the effects of ageing cannot be fully excluded, given the low sample size. No effects of the trainer and of the cortisol concentrations (of the previous year) were observed. This is the first evidence that post-training activity may influence memory up to 1 year after task acquisition.
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Riser, Molly M., Eldin Leighton, Jane Russenberger, Breno O. Fragomeni, and Caroline Moser. "189 Single-Step Gblup Evaluation for Behavioral Traits in Labrador Retrievers Used as Guide Dogs." Journal of Animal Science 101, Supplement_3 (November 6, 2023): 96–97. http://dx.doi.org/10.1093/jas/skad281.118.
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Abstract The objective of this study was to evaluate the accuracy of prediction in a genomic selection program for behavior traits in a population of Labrador Retrievers used as service dogs. Phenotypic data were collected in 4841 Labrador Retrievers with ages ranging from 3 months to 2.5 years, for 17 phenotypes from the International Working Dog Registry behavior checklist. The behavior checklist is a document that standardizes a scoring system for the reaction of an individual dog to environmental stimulus. Such scores are used to assess behavior and suitability of a dog for training. Pedigree contained 23,593 animals with birth dates ranging from 1991 to 2019. Genomic data were available for 457 individuals and obtained by low-pass whole genome sequences and reduced to a 250K SNP chip. Breeding values were calculated using a single trait animal model that included the fixed effects of sex, year of birth, and a contemporary group that included month/year of behavior test and organization that hosted the test. Variance components were estimated using AIREML. Genomic information was included in the model under a single-step GBLUP (ssGBLUP) approach by substituting the pedigree numerator relationship matrix with a matrix that combined pedigree and genomic relationships. Additionally, the genomic relationship matrix was modified under a weighted ssGBLUP (wssGBLUP) approach that allowed SNPs to have different distributions. Accuracies were evaluated in a 5-fold cross-validation that simulated a forward-in-time prediction. Heritabilities were low to moderate on all traits and varied from 0.025 to 0.37. Prediction based solely on pedigree information averaged 0.49 and ranged from 0.34 to 0.69. ssGBLUP increased the average accuracy to 0.55 and ranged from 0.34 to 0.72. Genomic estimated breeding values were more accurate than those computed with pedigrees for most traits. Gains in accuracy were limited by the small number of genotyped animals and are expected to increase as more animals are genotyped. The differences seen between the ssGBLUP approach and the wssGBLUP were minimal, and accuracies decreased after the second iteration. Those results indicate that behavior traits in this population are likely highly polygenic and would not benefit from weighted approaches. However, interpretation may change, as the limitations of the current study are due to the small number of genotyped individuals. Better SNP weight estimates may occur with more animals enrolled in the program, and with that a better description of the genetic architecture of the traits. The gains in accuracy show that genomic selection can help with improvement by identifying which young dogs have the highest genetic merit for the desired traits and are the best choices to keep as replacement breeders. The use of ssGBLUP is adequate for this canine data where not all animals are genotyped, and its use is recommended in selection programs focused on service dogs.
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Sakurama, Mayu, Miki Ito, Yumiko Nakanowataru, and Takanori Kooriyama. "Selection of Appropriate Dogs to Be Therapy Dogs Using the C-BARQ." Animals 13, no. 5 (February 24, 2023): 834. http://dx.doi.org/10.3390/ani13050834.
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In recent years, therapy dogs in medical and assisted living facilities have become popular in Japan, and the demand for such dogs has increased. However, some owners have their dogs take this test, which evaluates the dog’s talent, without understanding what is required of the test. The system needs to teach owners in an understandable way whether their dog is suitable to become a therapy dog so that the owners can determine if their dog is ready to be tested. Therefore, we suggest that easy at-home testing is likely to encourage dog owners to apply for their dog to take the aptitude test. If more dogs take the test, more therapy dogs can be born. The purpose of this study was to identify the personality traits of therapy dogs that pass the aptitude test by using the Canine Behavior Assessment and Research Questionnaire (C-BARQ). The C-BARQ was administered to dogs that previously passed the aptitude test for therapy training at the Hokkaido Volunteer Dog Association, assessing their behavioural displays. A factor analysis was conducted for each questionnaire item, and a total of 98 items were analyzed. Data were collected from the results of 110 dogs encompassing 30 dog breeds, with the most common breeds including Labrador Retrievers, Golden Retrievers, and Toy Poodles. Factor analysis revealed that 14 extracted factors should be evaluated. Given these personality traits and the fact that breed and age did not influence aptitude, we believe that a variety of dogs have the potential to become therapy dogs.
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Leighton, Eldin A. "Genetics of canine hip dysplasia." Journal of the American Veterinary Medical Association 210, no. 10 (May 15, 1997): 1474–79. http://dx.doi.org/10.2460/javma.1997.210.10.1474.
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Objective— To document genetic progress in improving hip quality of dogs maintained in a closed breeding colony to produce dogs for training as guides for blind people. Design— Prospective analysis of hip quality records from a breeding trial that encompassed 3 full generations and included some dogs born into the fourth and fifth generations. Animals— Hip quality was assessed for 2,037 German Shepherd Dogs and 1,821 Labrador Retrievers from 1980 to 1996. Procedure— A subjective hip score assigned by 1 radiologist was used to assess hip quality during the study period. In the past 8 years, the distraction Index was also used. Genetic change was produced by selecting a small percentage of dogs to be parents of the next generation. Dogs were selected to become parents of the next generation on the basis of estimated breeding values. These were calculated by combining observed values of individual dogs with known relationships in the population pedigrees to predict which dogs were the best candidates for selection as parents. Results— In < 5 generations of selection, the percentage of German Shepherd Dogs with canine hip dysplasia at 12 to 16 months of age decreased from 55 to 24 %. Among Labrador Retrievers, the percentage decreased from 30 to 10%. Clinical Implications— This report gives practitioners documented proof that genetic selection will work to improve hip quality. Dog breeders must be advised to be patient, however, to allow enough generations to elapse to make meaningful genetic change. (J Am Vet Med Assoc 1997;210:1474-1479)
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Chiofalo, Biagina, Esterina Fazio, Salvatore Cucinotta, and Cristina Cravana. "Thyroid and Lipid Status in Guide Dogs During Training: Effects of Dietary Protein and Fat Content." Animals 9, no. 9 (August 23, 2019): 597. http://dx.doi.org/10.3390/ani9090597.
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Nutrition plays a leading role that most influences thyroid response and energetic metabolism. Aim was to compare the effect of diet on thyroid and lipid status in guide dogs during a 12-weeks training period. Eight Labrador Retrievers were divided into two groups homogeneous for sex, age, body weight, and Body Condition Score (BCS) and fed two commercial diets one, HPF, characterized by low-carbohydrate/high-protein/high-fat (29%:39%:19% as-fed) and the other, LPF, by high-carbohydrate/low-protein/low-fat (50%:24%:12% as-fed) content. The serum thriiodothyronine (T3), thyroxine (T4), cholesterol (CHOL), triglycerides (TAGs) and non-esterified fatty acids (NEFA) were determined at Day 0, 28, 56, and 84, before the daily training. Statistical model included the effects of Diet (HPF vs. LPF) and Time (Day 0 to Day 84), and their interaction. In the HPF group, Diet significantly (p < 0.01) increased T4, CHOL, and TAGs and decreased NEFA. In both groups, Time significantly (p < 0.05) increased T4 and TAGs, CHOL at Day 28, and NEFA at Day 56. The interaction did not influence serum hormones and lipid pattern. The adjustments in thyroid and lipid responses to moderate exercise in HPF group were driven mainly by the nutrient composition of the diet in relation to the involvement of metabolic homeostasis.
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Shaffer, Lisa G., Christina J. Ramirez, Patricia Phelps, Maya Aviram, Marta Walczak, Gila Kahila Bar-Gal, and Blake C. Ballif. "An International Genetic Survey of Breed-Specific Diseases in Working Dogs from the United States, Israel, and Poland." Cytogenetic and Genome Research 153, no. 4 (2017): 198–204. http://dx.doi.org/10.1159/000486774.
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Genetic diseases occur in breeds used for law enforcement. As important team members, dogs are expected to operate at peak performance for several years and are significant investments for both the initial purchase and extensive, specialized training. Previous studies have not focused on causes for retirement or euthanasia as genetic (inherited) versus acquired (environmental). We performed direct mutational analysis for breed-specific conditions on samples from 304 dogs including 267 law enforcement (122 US, 87 Israeli, and 58 Polish) and 37 search and rescue dogs. Genetic testing identified 29% (n = 89) of the dogs tested to be carriers of a genetic mutation and 6% (n = 19) to be at risk for a debilitating inherited condition that may eventually impair the dog's ability to work. At-risk dogs included Labrador Retrievers (n = 4) with exercise-induced collapse, Bloodhounds (n = 2) with degenerative myelopathy (DM), and German Shepherd dogs with DM (n = 12) or leukocyte adhesion deficiency, type III (n = 1). A substantial number of working dogs were shown to be at risk for genetic conditions that may shorten the dog's career. The loss of dogs, due to early retirement or euthanasia, as a result of preventable genetic conditions has an emotional cost to handlers and financial cost to service organizations that can be avoided with genetic screening prior to breeding, buying, or training.
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Ram, Ori, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. "In-Context Retrieval-Augmented Language Models." Transactions of the Association for Computational Linguistics 11 (2023): 1316–31. http://dx.doi.org/10.1162/tacl_a_00605.
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Abstract Retrieval-Augmented Language Modeling (RALM) methods, which condition a language model (LM) on relevant documents from a grounding corpus during generation, were shown to significantly improve language modeling performance. In addition, they can mitigate the problem of factually inaccurate text generation and provide natural source attribution mechanism. Existing RALM approaches focus on modifying the LM architecture in order to facilitate the incorporation of external information, significantly complicating deployment. This paper considers a simple alternative, which we dub In-Context RALM: leaving the LM architecture unchanged and prepending grounding documents to the input, without any further training of the LM. We show that In-Context RALM that builds on off-the-shelf general purpose retrievers provides surprisingly large LM gains across model sizes and diverse corpora. We also demonstrate that the document retrieval and ranking mechanism can be specialized to the RALM setting to further boost performance. We conclude that In-Context RALM has considerable potential to increase the prevalence of LM grounding, particularly in settings where a pretrained LM must be used without modification or even via API access.1
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Riser, Molly M., Eldin Leighton, Jane Russenberger, Caroline Moser, and Breno O. Fragomeni. "PSVI-11 Genome-Wide Association Study for 4 Behavior Traits in a Population of Labrador Retrievers Bred as Guide Dogs." Journal of Animal Science 101, Supplement_3 (November 6, 2023): 408–9. http://dx.doi.org/10.1093/jas/skad281.485.
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Abstract The objective of this study was to assess the genetic architecture and investigate genomic regions associated with Body Handling, Harness sensitivity, Self-modulation, and Noise sensitivity in Labrador Retrievers used as guide dogs. A single-step genome-wide association study (ssGWAS) was used for this purpose. Phenotypic data were collected in 4,841 Labrador Retrievers with ages ranging from 3 months to 2.5 years, for all four traits. Phenotypes were selected from the Behavior Checklist, which is a scoring tool used by a skilled scorer to describe aspects of behavior observed during a variety of assessments including formal tests and while training or observing another handler working with the dog. The pedigree file contained 23,593 animals with birth years ranging from 1991 to 2019 from two populations with related ancestors. Genomic data were available for 457 individuals and were obtained by selecting 250K SNPs from whole genome sequences. Associations were calculated as the percentage of variance explained by windows of 80 adjacent SNPs. SNP variances were calculated based on SNP effects that were obtained by back-solving genomic estimated breeding values obtained with the single-step GBLUP method in a genomic selection program with the same population. Additionally, the genomic relationship matrix was modified under a weighted ssGBLUP (wssGBLUP) approach that allowed SNPs to have different distributions. Manhattan plots were generated with the variance explained by SNP windows for 3 iterations of wssGBLUP. After visual inspection of the Manhattan plots, the R package GALLO was used to annotate the genes located within the peaks that explained 0.6% or more of the genetic variance. The maximum amount of variance explained by a window of 80 SNPs was 1.1% for Body Handling. This trait presented three other regions explaining more than 0.6% of the genetic variance. Harness sensitivity presented four peaks above the threshold, with the major one explaining 0.75% of the genetic variance. Self-modulation and Noise sensitivity both had their major peak explaining 0.95% of the genetic variance and presented four and three regions above the threshold, respectively. The genes annotated in the candidate regions were not specifically related to behavior, nor were they involved in any pathway with biological relevance to the phenotypes. Moreover, some of the regions were not annotated, and no associated genes were found. The traits studied in this project were shown to be polygenic and complex, and that explains the lack of major genes. It is expected that the quality of associations will improve as the sample size increases. The next steps of this project are to increase the number of genotyped animals, increase the number of markers, and adopt alternative annotation and enrichment tools for the post-GWAS analysis.
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Smith, Gail K., Elizabeth LaFond, Thomas P. Gregor, Dennis F. Lawler, and Robert C. Nie. "Within- and between-examiner repeatability of distraction indices of the hip joints in dogs." American Journal of Veterinary Research 58, no. 10 (October 1, 1997): 1076–77. http://dx.doi.org/10.2460/ajvr.1997.58.10.1076.
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Abstract Objective To evaluate in vivo repeatability of the distraction index method of evaluating hip joint laxity in dogs. Animals 31 two-year-old Labrador Retrievers. Procedure Each dog was anesthetized and radiographically evaluated for hip joint laxity 4 times: twice by an experienced examiner and twice by an examiner who had no previous knowledge of or training in the technique prior to the first day of testing. Distraction indices (DI) were determined from the radiographs and intraclass correlation coefficients were calculated to evaluate the repeatability of DI measurements between and within examiners. Results Intraclass correlation coefficients were high (range, 0.85 to 0.94). Lower limits of the 95% confidence intervals for the intraclass correlation coefficients ranged from 0.75 to 0.89. Conclusions Between- and within-examiner repeatabilities of DI measurements were high, suggesting that the technique is clinically reliable. Clinical Relevance Distraction index is a reliable measure of hip joint laxity and a good predictor of the risk of development of degenerative joint disease associated with hip dysplasia in dogs. Establishment of high repeatability of DI measurements suggests that the stress-radiographic method may be used by multiple examiners with the expectation of comparable and consistent results. (Am J Vet Res 1997;58:1076–1077)
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Fedkin, Nikita M., Can Li, Nickolay A. Krotkov, Pascal Hedelt, Diego G. Loyola, Russell R. Dickerson, and Robert Spurr. "Volcanic SO<sub>2</sub> effective layer height retrieval for the Ozone Monitoring Instrument (OMI) using a machine-learning approach." Atmospheric Measurement Techniques 14, no. 5 (May 20, 2021): 3673–91. http://dx.doi.org/10.5194/amt-14-3673-2021.
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Abstract. Information about the height and loading of sulfur dioxide (SO2) plumes from volcanic eruptions is crucial for aviation safety and for assessing the effect of sulfate aerosols on climate. While SO2 layer height has been successfully retrieved from backscattered Earthshine ultraviolet (UV) radiances measured by the Ozone Monitoring Instrument (OMI), previously demonstrated techniques are computationally intensive and not suitable for near-real-time applications. In this study, we introduce a new OMI algorithm for fast retrievals of effective volcanic SO2 layer height. We apply the Full-Physics Inverse Learning Machine (FP_ILM) algorithm to OMI radiances in the spectral range of 310–330 nm. This approach consists of a training phase that utilizes extensive radiative transfer calculations to generate a large dataset of synthetic radiance spectra for geophysical parameters representing the OMI measurement conditions. The principal components of the spectra from this dataset in addition to a few geophysical parameters are used to train a neural network to solve the inverse problem and predict the SO2 layer height. This is followed by applying the trained inverse model to real OMI measurements to retrieve the effective SO2 plume heights. The algorithm has been tested on several major eruptions during the OMI data record. The results for the 2008 Kasatochi, 2014 Kelud, 2015 Calbuco, and 2019 Raikoke eruption cases are presented here and compared with volcanic plume heights estimated with other satellite sensors. For the most part, OMI-retrieved effective SO2 heights agree well with the lidar measurements of aerosol layer height from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and thermal infrared retrievals of SO2 heights from the infrared atmospheric sounding interferometer (IASI). The errors in OMI-retrieved SO2 heights are estimated to be 1–1.5 km for plumes with relatively large SO2 signals (>40 DU). The algorithm is very fast and retrieves plume height in less than 10 min for an entire OMI orbit.
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Calbet, X., and P. Schlüssel. "Technical note: analytical estimation of the optimal parameters for the EOF retrievals of the IASI Level 2 Product Processing Facility and its application using AIRS and ECMWF data." Atmospheric Chemistry and Physics Discussions 5, no. 5 (October 10, 2005): 9691–730. http://dx.doi.org/10.5194/acpd-5-9691-2005.
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Abstract. The Empirical Orthogonal Function (EOF) retrieval technique consists of calculating the eigenvectors of the spectra to later perform a linear regression between these and the atmospheric states, this first step is known as training. At a later stage, known as performing the retrievals, atmospheric profiles are derived from measured atmospheric radiances. When EOF retrievals are trained with a statistically different data set than the one used for retrievals two basic problems arise: significant biases appear in the retrievals and differences between the covariances of the training data set and the measured data set degrade them. The retrieved profiles will show a bias with respect to the real profiles which comes from the combined effect of the mean difference between the training and the real spectra projected into the atmospheric state space and the mean difference between the training and the atmospheric profiles. The standard deviations of the difference between the retrieved profiles and the real ones show different behavior depending on whether the covariance of the training spectra is bigger, equal or smaller than the covariance of the measured spectra with which the retrievals are performed. The procedure to correct for these effects is shown both analytically and with a measured example. It consists of first calculating the average and standard deviation of the difference between real observed spectra and the calculated spectra obtained from the real atmospheric state and the radiative transfer model used to create the training spectra. In a later step, measured spectra must be bias corrected with this average before performing the retrievals and the linear regression of the training must be performed adding noise to the spectra corresponding to the aforementioned calculated standard deviation. This procedure is optimal in the sense that to improve the retrievals one must resort to using a different training data set or a different algorithm.
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Calbet, X., and P. Schlüssel. "Technical note: analytical estimation of the optimal parameters for the EOF retrievals of the IASI Level 2 Product Processing Facility and its application using AIRS and ECMWF data." Atmospheric Chemistry and Physics 6, no. 3 (March 16, 2006): 831–46. http://dx.doi.org/10.5194/acp-6-831-2006.
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Abstract. The Empirical Orthogonal Function (EOF) retrieval technique consists of calculating the eigenvectors of the spectra to later perform a linear regression between these and the atmospheric states, this first step is known as training. At a later stage, known as performing the retrievals, atmospheric profiles are derived from measured atmospheric radiances. When EOF retrievals are trained with a statistically different data set than the one used for retrievals two basic problems arise: significant biases appear in the retrievals and differences between the covariances of the training data set and the measured data set degrade them. The retrieved profiles will show a bias with respect to the real profiles which comes from the combined effect of the mean difference between the training and the real spectra projected into the atmospheric state space and the mean difference between the training and the atmospheric profiles. The standard deviations of the difference between the retrieved profiles and the real ones show different behavior depending on whether the covariance of the training spectra is bigger, equal or smaller than the covariance of the measured spectra with which the retrievals are performed. The procedure to correct for these effects is shown both analytically and with a measured example. It consists of first calculating the average and standard deviation of the difference between real observed spectra and the calculated spectra obtained from the real atmospheric state and the radiative transfer model used to create the training spectra. In a later step, measured spectra must be bias corrected with this average before performing the retrievals and the linear regression of the training must be performed adding noise to the spectra corresponding to the aforementioned calculated standard deviation. This procedure is optimal in the sense that to improve the retrievals one must resort to using a different training data set or a different algorithm.
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Srinivasan, Visish M., Stephen R. Chen, Kevin M. Camstra, Gouthami Chintalapani, and Peter Kan. "Development of a recalcitrant, large clot burden, bifurcation occlusion model for mechanical thrombectomy." Neurosurgical Focus 42, no. 4 (April 2017): E6. http://dx.doi.org/10.3171/2017.1.focus16501.
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OBJECTIVE Stroke is a major cause of disability and death in adults. Several large randomized clinical trials have shown the significant benefit of mechanical thrombectomy with modern stent retrievers in the treatment of large-vessel occlusions. However, large clots located at bifurcations remain challenging to treat. An in vivo model of these recalcitrant clots needs to be developed to test future generations of devices. METHODS Autologous blood was drawn from anesthetized swine via a femoral sheath. Blood was then mixed with thrombin, calcium chloride, and saline, and injected into silicone tubing to form cylindrical clots in the standard fashion. Matured clots were then delivered in an unfragmented fashion directly into the distal extracranial vasculature, at branch points where vessel sizes mimic the human middle cerebral artery, by using Penumbra aspiration tubing and the Penumbra ACE68 reperfusion catheter. RESULTS A total of 5 adult swine were used to develop the model. The techniques evolved during experiments in the first 3 animals, and the last 2 were used to establish the final model. In these 2 swine, a total of 8 autologous clots, 15–20 mm, were injected directly into 8 distal extracranial vessels at branch points to mimic a bifurcation occlusion in a human. All clots were delivered directly at a distal bifurcation or trifurcation in an unfragmented fashion to cause an occlusion. Ten revascularization attempts were made, and none of the branch-point occlusions were fully revascularized on the first attempt. CONCLUSIONS Using novel large-bore distal access catheters, large unfragmented clots can be delivered into distal extracranial vessels in a swine occlusion model. The model mimics the clinical situation of a recalcitrant bifurcation occlusion and will be valuable in the study of next-generation stroke devices and in training settings.
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Hu, Bo, Xingying Zhang, Rui Sun, and Xianchun Zhu. "Retrieval of Horizontal Visibility Using MODIS Data: A Deep Learning Approach." Atmosphere 10, no. 12 (November 25, 2019): 740. http://dx.doi.org/10.3390/atmos10120740.
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Horizontal visibility (HVIS) is a primary index used for assessing air quality. Although satellite images provide information regarding atmospheric aerosols, atmospheric visibility is not directly measured. In this paper, a deep learning approach is proposed to retrieve HVIS using moderate-resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, the European Centre for Medium-Range Weather Forecasts reanalysis dataset, and ground-based visibility observations. The deep neural network model comprises a multi-layer unsupervised restricted Boltzmann machine (RBM) and a layer for supervised learning. The dropout mechanism was used in the training process to overcome the errors caused by over-fitting. The results demonstrate that the correlation coefficient values between HVIS observations and retrievals during training, pre-validating, and evaluation were 0.74, 0.723, and 0.697, respectively. The retrieved HVIS in Eastern China exhibited a north-to-south increasing trend, increasing and decreasing in summer and winter, respectively. In conclusion, the proposed model presents an effective and more reliable method for HVIS retrieval. However, the small samples, low AOD, low albedo, high total column water, high longitude, and the low vertical wind component at 10 m likely cause HVIS bias.
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Ayaz, A. Salman, Jaya A. Venkat, and Zameer Gulzar. "Recommendation-Based Meta-Search Engine for Suggesting Relevant Documents Links." International Journal of Information and Communication Technology Education 16, no. 4 (October 2020): 86–99. http://dx.doi.org/10.4018/ijicte.2020100106.
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The information available online is mostly present in an unstructured form and search engines are indispensable tools especially in higher education organizations for obtaining information from the Internet. Various search engines were developed to help learners to retrieve the information but unfortunately, most of the information retrieved is not relevant. The main objective of this research is to provide relevant document links to the learners using a three-layered meta-search architecture. The first layer retrieves information links from the web based on the learner query, which is then fed to the second layer where filtering and clustering of document links are done based on semantics. The third layer, with the help of a reasoner, categorizes information into relevant and irrelevant information links in the repository. The experimental study was conducted on a training data set using web queries related to the domain of sports, entertainment, and academics. The results indicate that the proposed meta-search engine performs well as compared to another stand-alone search engine with better recall.
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Aksoy, Mehmet Emin. "Comparing Basic Life Support Serious Gaming Scores With Hands-on Training Platform Performance Scores: Pilot Simulation Study for Basic Life Support Training." JMIR Serious Games 8, no. 4 (November 25, 2020): e24166. http://dx.doi.org/10.2196/24166.
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Background Serious games enrich simulation-based health care trainings and improve knowledge, skills, and self-confidence of learners while entertaining them. Objective A platform which can combine performance data from a basic life support (BLS) serious game app and hands-on data based on the same scoring system is not available in the market. The aim of this study was to create such a platform and investigate whether performance evaluation of BLS trainings would be more objective compared to conventional Objective Structured Clinical Examination (OSCE) examinations if these evaluations were carried out with the platform which combines OSCE scoring criteria with sensor data retrieved from the simulator’s sensors. Methods Participants were 25 volunteers (11 men [44.0%] and 14 [56.0] women) among Acıbadem Mehmet Ali Aydınlar University students without prior knowledge of the BLS protocol. A serious game module has been created for teaching learners the European Resuscitation Council Basic Life Support 2015 protocol. A second module called the hands-on module was designed for educators. This module includes a checklist used for BLS OSCE examinations and can retrieve sensor data such as compression depth, compression frequency, and ventilation volume from the manikin (CPR Lilly; 3B Scientific GmbH) via Bluetooth. Data retrieved from the sensors of the manikin enable educators to evaluate learners in a more objective way. Performance data retrieved from the serious gaming module have been combined with the results of the hands-on module. Data acquired from the hands-on module have also been compared with the results of conventional OSCE scores of the participants, which were obtained by watching the videos of the same trainings. Results Participants were considered successful in the game if they scored 80/100 or above. Overall, participants scored 80 or above in an average of 1.4 (SD 0.65) trials. The average BLS serious game score was 88.3/100 (SD 5.17) and hands-on average score was 70.7/100 (SD 17.3), whereas the OSCE average score was 84.4/100 (SD 12.9). There was no statistically significant correlation between success on trials (score ≥80/100), serious game, hands-on training app, and OSCE scores (Spearman rho test, P>.05). The mean BLS serious game score of the participants was 88.3/100 (SD 5.17), whereas their mean hands-on training app score was 70.7/100 (SD 17.3) and OSCE score was 84.4/100 (SD 12.9). Conclusions Although scoring criteria for OSCE and hands-on training app were identical, OSCE scores were 17% higher than hands-on training app scores. After analyzing the difference of scores between hands-on training app and OSCE, it has been revealed that these differences originate from scoring parameters such as compression depth, compression frequency, and ventilation volume. These data suggest that evaluation of BLS trainings would be more objective if these evaluations were carried out with the modality, which combines visual OSCE scoring criteria with sensor data retrieved from the simulator’s sensors. Trial Registration ClinicalTrials.gov NCT04533893; https://clinicaltrials.gov/ct2/show/NCT04533893
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Hu, Jingjing, Yansong Bao, Jian Liu, Hui Liu, George P. Petropoulos, Petros Katsafados, Liuhua Zhu, and Xi Cai. "Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/HIRAS in the Arctic Region." Remote Sensing 13, no. 10 (May 11, 2021): 1884. http://dx.doi.org/10.3390/rs13101884.
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The acquisition of real-time temperature and relative humidity (RH) profiles in the Arctic is of great significance for the study of the Arctic’s climate and Arctic scientific research. However, the operational algorithm of Fengyun-3D only takes into account areas within 60°N, the innovation of this work is that a new technique based on Neural Network (NN) algorithm was proposed, which can retrieve these parameters in real time from the Fengyun-3D Hyperspectral Infrared Radiation Atmospheric Sounding (HIRAS) observations in the Arctic region. Considering the difficulty of obtaining a large amount of actual observation (such as radiosonde) in the Arctic region, collocated ERA5 data from European Centre for Medium-Range Weather Forecasts (ECMWF) and HIRAS observations were used to train the neural networks (NNs). Brightness temperature and training targets were classified using two variables: season (warm season and cold season) and surface type (ocean and land). NNs-based retrievals were compared with ERA5 data and radiosonde observations (RAOBs) independent of the NN training sets. Results showed that (1) the NNs retrievals accuracy is generally higher on warm season and ocean; (2) the root-mean-square error (RMSE) of retrieved profiles is generally slightly higher in the RAOB comparisons than in the ERA5 comparisons, but the variation trend of errors with height is consistent; (3) the retrieved profiles by the NN method are closer to ERA5, comparing with the AIRS products. All the results demonstrated the potential value in time and space of NN algorithm in retrieving temperature and relative humidity profiles of the Arctic region from HIRAS observations under clear-sky conditions. As such, the proposed NN algorithm provides a valuable pathway for retrieving reliably temperature and RH profiles from HIRAS observations in the Arctic region, providing information of practical value in a wide spectrum of practical applications and research investigations alike.All in all, our work has important implications in broadening Fengyun-3D’s operational implementation range from within 60°N to the Arctic region.
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Jing, Chenchen, Yukun Li, Hao Chen, and Chunhua Shen. "Retrieval-Augmented Primitive Representations for Compositional Zero-Shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (March 24, 2024): 2652–60. http://dx.doi.org/10.1609/aaai.v38i3.28043.
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Compositional zero-shot learning (CZSL) aims to recognize unseen attribute-object compositions by learning from seen compositions. Composing the learned knowledge of seen primitives, i.e., attributes or objects, into novel compositions is critical for CZSL. In this work, we propose to explicitly retrieve knowledge of seen primitives for compositional zero-shot learning. We present a retrieval-augmented method, which augments standard multi-path classification methods with two retrieval modules. Specifically, we construct two databases storing the attribute and object representations of training images, respectively. For an input training/testing image, we use two retrieval modules to retrieve representations of training images with the same attribute and object, respectively. The primitive representations of the input image are augmented by using the retrieved representations, for composition recognition. By referencing semantically similar images, the proposed method is capable of recalling knowledge of seen primitives for compositional generalization. Experiments on three widely-used datasets show the effectiveness of the proposed method.
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Niu, Zeyi, Xiaolei Zou, and Wei Huang. "Typhoon Warm-Core Structures Derived from FY-3D MWTS-2 Observations." Remote Sensing 13, no. 18 (September 17, 2021): 3730. http://dx.doi.org/10.3390/rs13183730.
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In this study, the three-dimensional (3D) warm-core structures of the Northwest Pacific typhoons Francisco, Lekima, and Krosa in August 2019 are retrieved from the Fengyun-3D (FY-3D) microwave temperature sounder-2 (MWTS-2) observations of brightness temperature. Due to the lack of two window channels at 23.8 GHz and 31.4 GHz, an empirical cloud detection algorithm based on 50.3 GHz bias-corrected observations-minus-backgrounds is applied to obtain clear-sky observations for the multiple linear regression retrieval algorithm. The MWTS-2 cloud-affected channels 3–5 are not used to retrieve temperatures under cloudy conditions to eliminate low-tropospheric cold anomalies. The multiple linear regression coefficients are obtained based on MWTS-2 brightness temperatures and the temperatures from the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5) in the training period of three weeks before the month of targeted typhoons. The proposed MWTS-2 warm-core retrieval can well capture the radial and vertical temporal evolutions of the temperature anomalies of the typhoons Francisco, Lekima, and Krosa. The sizes of the warm-core anomalies of typhoons Lekima and Krosa retrieved by the MWTS-2 are horizontally and vertically similar to and stronger than those of the ERA5. Compared with the ERA5 reanalysis in August 2019, the biases for MWTS-2 temperature retrievals are smaller than ±0.25 K, with root-mean-square errors (RMSEs) smaller than and 2.0 K at all altitudes. Additionally, the location of the 250-hPa maximum temperature anomaly retrieved by the MWTS-2 is closer to the best track than that of the ERA5. A weak warm-core around 200 hPa and a cold-core anomaly in the middle troposphere are also found in the outer rain bands region due to the effect of evaporation of rainfall.
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Yarmohammadi, Zahra, and Raham Hosseiny. "210: UTILIZATION OF MEDICAL IMAGES OF PACS FOR EVIDENCE BASED TRAINING." BMJ Open 7, Suppl 1 (February 2017): bmjopen—2016–015415.210. http://dx.doi.org/10.1136/bmjopen-2016-015415.210.
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Background and aims:The more evidence the better decision making. Diagnosis is one of the medical determinants that seriously depend upon the evidence. Treatment and care as the other significant arms of medical practice relies on the right diagnosis. Medical education system tries to track whole capacity of health system to bring more evidence for education, comparison and critical thinking. Picture archiving and communication system (PACS) is used as a tool for providing, processing transferring and displaying the digital images in hospitals information system. Once the medical images stored in the PACS can be retrieved and used several times for different purposes by adding value with no more cost for system.Methods:The PACS system consists of a main PACS server which is used for storing the pictures and multiple clients in different places that can communicate with PACS central database through DICOM protocol. Clients can query the system, retrieve the images stored in the database, and show the images by using medical imaging software like DICOM viewer. Open sources software enable the user add/change the source for internal utilization purposes. If medical images is stored by additional metadata i.e. ICD codes and the Mesh /USML keywords along with the patients personal information, the system will be used as a source of evidence for educational propose. DICOM software will query the main database using the keywords and ICD codes that already have been attributed to the images to retrieve all the related images in the system for specific case. For ethical issues the personal information can be concealed when it is retrieved for education purpose.Results and Conclusion:By adding metadata through mesh/USML and ICD the images will become a source of evidence instead of being just a temporary diagnoses tool archived in patient's personal records. This method is a simple and cost less way of use of medical images in health system and medical education. It also makes the investments return to the system by multiple use of images
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Qi, Lin, Ronggao Liu, and Yang Liu. "Retrieval of Aerosol Single-Scattering Albedo from MODIS Data Using an Artificial Neural Network." Remote Sensing 14, no. 24 (December 14, 2022): 6341. http://dx.doi.org/10.3390/rs14246341.
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Aerosol single-scattering albedo (SSA) is one of the largest sources of uncertainty in the evaluation of the aerosol radiative forcing effect. The SSA signal, coupled with aerosol optical depth (AOD) and surface reflectance in satellite images, is difficult to retrieve by the look-up table approach. In this study, we proposed an artificial neural network- (ANN) based approach that retrieves SSA over land based on MODIS (moderate resolution imaging spectroradiometer) visible (red band) reflectance variations among nearby pixels that have different surface reflectivities. Using the training dataset generated by the radiative transfer model, the ANN model was trained to establish the relationship among SSA, surface reflectance, and top of atmosphere (TOA) reflectance. Then, based on the trained ANN model, SSA can be retrieved using the surface and apparent reflectance of several heterogeneous pixels. According to sensitivity analysis, this method works well on nonuniform land surfaces with high AODs. The root mean square error (RMSE) of retrieved and measured SSA (from 28 sites of AErosol RObotic NETwork, AERONET) was 0.042, of which the results with an error less than 0.03 accounted for 51%. In addition, the SSA retrieval method was applied to several thick aerosol layer events over different areas (South Asia, South America, and North China Plain) and compared with the ozone monitoring instrument near-UV aerosol data product (OMAERUV). The comparison results of the images show that the retrieval method of visible wavelength proposed in this study has similar outcomes to those from the ultraviolet wavelengths in these regions. The retrieval algorithm we propose provides an effective way to produce an SSA product in visible wavelength and might help to better estimate the aerosol radiative and optical properties over high heterogeneous areas, which is important for the aerosol radiative impact estimate at a regional scale.
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Pfreundschuh, Simon, Paula J. Brown, Christian D. Kummerow, Patrick Eriksson, and Teodor Norrestad. "GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm." Atmospheric Measurement Techniques 15, no. 17 (September 2, 2022): 5033–60. http://dx.doi.org/10.5194/amt-15-5033-2022.
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Abstract. The Global Precipitation Measurement (GPM) mission measures global precipitation at a temporal resolution of a few hours to enable close monitoring of the global hydrological cycle. GPM achieves this by combining observations from a spaceborne precipitation radar, a constellation of passive microwave (PMW) sensors, and geostationary satellites. The Goddard Profiling Algorithm (GPROF) is used operationally to retrieve precipitation from all PMW sensors of the GPM constellation. Since the resulting precipitation rates serve as input for many of the level 3 retrieval products, GPROF constitutes an essential component of the GPM processing pipeline. This study investigates ways to improve GPROF using modern machine learning methods. We present two neural-network-based, probabilistic implementations of GPROF: GPROF-NN 1D, which (just like the current GPROF implementation) processes pixels individually, and GPROF-NN 3D, which employs a convolutional neural network to incorporate structural information into the retrieval. The accuracy of the retrievals is evaluated using a test dataset consistent with the data used in the development of the GPROF and GPROF-NN retrievals. This allows for assessing the accuracy of the retrieval method isolated from the representativeness of the training data, which remains a major source of uncertainty in the development of precipitation retrievals. Despite using the same input information as GPROF, the GPROF-NN 1D retrieval improves the accuracy of the retrieved surface precipitation for the GPM Microwave Imager (GMI) from 0.079 to 0.059 mm h−1 in terms of mean absolute error (MAE), from 76.1 % to 69.5 % in terms of symmetric mean absolute percentage error (SMAPE) and from 0.797 to 0.847 in terms of correlation. The improvements for the Microwave Humidity Sounder (MHS) are from 0.085 to 0.061 mm h−1 in terms of MAE, from 81 % to 70.1 % for SMAPE, and from 0.724 to 0.804 in terms of correlation. Comparable improvements are found for the retrieved hydrometeor profiles and their column integrals, as well as the detection of precipitation. Moreover, the ability of the retrievals to resolve small-scale variability is improved by more than 40 % for GMI and 29 % for MHS. The GPROF-NN 3D retrieval further improves the MAE to 0.043 mm h−1; the SMAPE to 48.67 %; and the correlation to 0.897 for GMI and 0.043 mm h−1, 63.42 %, and 0.83 for MHS. Application of the retrievals to GMI observations of Hurricane Harvey shows moderate improvements when compared to co-located GPM-combined and ground-based radar measurements indicating that the improvements at least partially carry over to assessment against independent measurements. Similar retrievals for MHS do not show equally clear improvements, leaving the validation against independent measurements for future investigation. Both GPROF-NN algorithms make use of the same input and output data as the original GPROF algorithm and thus may replace the current implementation in a future update of the GPM processing pipeline. Despite their superior accuracy, the single-core runtime required for the operational processing of an orbit of observations is lower than that of GPROF. The GPROF-NN algorithms promise to be a simple and cost-efficient way to increase the accuracy of the PMW precipitation retrievals of the GPM constellation and thus improve the monitoring of the global hydrological cycle.
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Sachan, Devendra Singh, Mike Lewis, Dani Yogatama, Luke Zettlemoyer, Joelle Pineau, and Manzil Zaheer. "Questions Are All You Need to Train a Dense Passage Retriever." Transactions of the Association for Computational Linguistics 11 (2023): 600–616. http://dx.doi.org/10.1162/tacl_a_00564.
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Abstract We introduce ART, a new corpus-level autoencoding approach for training dense retrieval models that does not require any labeled training data. Dense retrieval is a central challenge for open-domain tasks, such as Open QA, where state-of-the-art methods typically require large supervised datasets with custom hard-negative mining and denoising of positive examples. ART, in contrast, only requires access to unpaired inputs and outputs (e.g., questions and potential answer passages). It uses a new passage-retrieval autoencoding scheme, where (1) an input question is used to retrieve a set of evidence passages, and (2) the passages are then used to compute the probability of reconstructing the original question. Training for retrieval based on question reconstruction enables effective unsupervised learning of both passage and question encoders, which can be later incorporated into complete Open QA systems without any further finetuning. Extensive experiments demonstrate that ART obtains state-of-the-art results on multiple QA retrieval benchmarks with only generic initialization from a pre-trained language model, removing the need for labeled data and task-specific losses.1 Our code and model checkpoints are available at: https://github.com/DevSinghSachan/art.
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Chen, Lijuan, Haishan Chen, Xinguan Du, and Ren Wang. "Retrieval of Surface Energy Fluxes Considering Vegetation Changes and Aerosol Effects." Remote Sensing 16, no. 4 (February 13, 2024): 668. http://dx.doi.org/10.3390/rs16040668.
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The exchange of moisture and energy between the land and the atmosphere plays a crucial role in terrestrial hydrological cycle and climate change. However, existing studies on the retrieval of surface water and heat flux tend to overlook the dynamic changes in surface vegetation and atmospheric aerosols, which directly affect surface energy and indirectly alter various meteorological factors, including cloud, precipitation, and temperature. In this study, we assess the machine-learning retrieval method for surface fluxes that takes into account vegetation changes and aerosol effects, using FLUXNET observations and remote sensing data to retrieve latent heat flux (LE) and sensible heat flux (H). We constructed four sets of deep neural network models: (a) The first set considers only meteorological factors, (b) the second set considers meteorological factors and aerosols, (c) the third set considers meteorological factors and vegetation changes, and (d) the fourth set comprehensively considers meteorological factors, aerosols, and vegetation changes. All model performances were evaluated using statistical indicators. ERA5 reanalysis and remote sensing data were used to drive the models and retrieve daily H and LE. The retrieved results were validated against ground observation sites that were not involved in model training or the FLUXCOM product. The results show that the model that considers meteorological factors, aerosols, and vegetation changes has the smallest errors and highest correlation for retrieving H and LE (RH = 0.85, RMSEH = 24.88; RLE = 0.88, RMSELE = 22.25). The ability of the four models varies under different vegetation types. In terms of seasons, the models that consider meteorological factors and vegetation changes, as well as those that comprehensively consider meteorological factors, aerosols, and vegetation changes, perform well in retrieving the surface fluxes. As for spatial distribution, when atmospheric aerosols are present in the region, the model that considers both meteorological factors and aerosols retrieves higher values of H compared to the model that considers only meteorological factors, while the LE values are relatively lower. The model that considers meteorological factors and vegetation changes, as well as the model that comprehensively considers meteorological factors, aerosols, and vegetation changes, retrieves lower values in most regions. Through the validation of independent observation sites and FLUXCOM products, we found that the model, considering meteorological factors, aerosols, and vegetation changes, was generally more accurate in the retrieval of surface fluxes. This study contributes to improving the retrieval and future prediction accuracy of surface fluxes in a changing environment.
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Li, Juan, Zhiqiang Xiao, Rui Sun, and Jinling Song. "Retrieval of the Leaf Area Index from Visible Infrared Imaging Radiometer Suite (VIIRS) Surface Reflectance Based on Unsupervised Domain Adaptation." Remote Sensing 14, no. 8 (April 10, 2022): 1826. http://dx.doi.org/10.3390/rs14081826.
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Several global leaf area index (LAI) products were generated using neural networks, but the training dataset for the neural networks was sensor specific, and the construction of the training dataset was time consuming. In this paper, an unsupervised domain adaptation-based method was proposed to estimate LAI from the Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance dataset based on a training dataset constructed from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance dataset. A transfer component analysis (TCA) algorithm was first utilized to map the MODIS and VIIRS surface reflectance into the same subspace to reduce the distribution discrepancies between the MODIS and VIIRS surface reflectance. Then, the embedded data obtained from MODIS surface reflectance dataset, along with the LAI values produced by fusing the MODIS and the Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) products, were employed to train general regression neural networks (GRNNs). Finally, for retrieving the LAI values, the embedded data acquired from the VIIRS surface reflectance dataset was input into the trained GRNNs. For multiple field sites with different biome types, we used this developed method to retrieve LAI values based on the VIIRS surface reflectance dataset. The results indicate that, based on the training dataset built from MODIS surface reflectance dataset, the domain adaptation-based retrieval method can effectively estimate LAI values from VIIRS surface reflectance dataset. By comparison with the VIIRS and MODIS LAI products, the retrieved LAI values with TCA are more consistent with the reference LAI values acquired from high-resolution remote sensing images. The coefficient of determination (R2) and root mean square error (RMSE) of the retrieved LAI values with TCA at all selected sites are 0.88 and 0.68, respectively. Furthermore, the accuracy of the retrieved LAI values with TCA is higher than the retrieved LAI values without TCA with the R2 0.81 and the RMSE 0.79.
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Strandgren, Johan, Luca Bugliaro, Frank Sehnke, and Leon Schröder. "Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks." Atmospheric Measurement Techniques 10, no. 9 (September 29, 2017): 3547–73. http://dx.doi.org/10.5194/amt-10-3547-2017.
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Abstract. Cirrus clouds play an important role in climate as they tend to warm the Earth–atmosphere system. Nevertheless their physical properties remain one of the largest sources of uncertainty in atmospheric research. To better understand the physical processes of cirrus clouds and their climate impact, enhanced satellite observations are necessary. In this paper we present a new algorithm, CiPS (Cirrus Properties from SEVIRI), that detects cirrus clouds and retrieves the corresponding cloud top height, ice optical thickness and ice water path using the SEVIRI imager aboard the geostationary Meteosat Second Generation satellites. CiPS utilises a set of artificial neural networks trained with SEVIRI thermal observations, CALIOP backscatter products, the ECMWF surface temperature and auxiliary data. CiPS detects 71 and 95 % of all cirrus clouds with an optical thickness of 0.1 and 1.0, respectively, that are retrieved by CALIOP. Among the cirrus-free pixels, CiPS classifies 96 % correctly. With respect to CALIOP, the cloud top height retrieved by CiPS has a mean absolute percentage error of 10 % or less for cirrus clouds with a top height greater than 8 km. For the ice optical thickness, CiPS has a mean absolute percentage error of 50 % or less for cirrus clouds with an optical thickness between 0.35 and 1.8 and of 100 % or less for cirrus clouds with an optical thickness down to 0.07 with respect to the optical thickness retrieved by CALIOP. The ice water path retrieved by CiPS shows a similar performance, with mean absolute percentage errors of 100 % or less for cirrus clouds with an ice water path down to 1.7 g m−2. Since the training reference data from CALIOP only include ice water path and optical thickness for comparably thin clouds, CiPS also retrieves an opacity flag, which tells us whether a retrieved cirrus is likely to be too thick for CiPS to accurately derive the ice water path and optical thickness. By retrieving CALIOP-like cirrus properties with the large spatial coverage and high temporal resolution of SEVIRI during both day and night, CiPS is a powerful tool for analysing the temporal evolution of cirrus clouds including their optical and physical properties. To demonstrate this, the life cycle of a thin cirrus cloud is analysed.
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Alokla, Anas, Walaa Gad, Waleed Nazih, Mustafa Aref, and Abdel-Badeeh Salem. "Retrieval-Based Transformer Pseudocode Generation." Mathematics 10, no. 4 (February 16, 2022): 604. http://dx.doi.org/10.3390/math10040604.
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The comprehension of source code is very difficult, especially if the programmer is not familiar with the programming language. Pseudocode explains and describes code contents that are based on the semantic analysis and understanding of the source code. In this paper, a novel retrieval-based transformer pseudocode generation model is proposed. The proposed model adopts different retrieval similarity methods and neural machine translation to generate pseudocode. The proposed model handles words of low frequency and words that do not exist in the training dataset. It consists of three steps. First, we retrieve the sentences that are similar to the input sentence using different similarity methods. Second, pass the source code retrieved (input retrieved) to the deep learning model based on the transformer to generate the pseudocode retrieved. Third, the replacement process is performed to obtain the target pseudo code. The proposed model is evaluated using Django and SPoC datasets. The experiments show promising performance results compared to other language models of machine translation. It reaches 61.96 and 50.28 in terms of BLEU performance measures for Django and SPoC, respectively.
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Barrett, James, Jamie Douglas, Christopher Premanandan, and Robyn Wilborn. "Premature germ cells in the ejaculate of a 4-year-old male Labrador Retriever." Clinical Theriogenology 14, no. 1 (March 1, 2022): 26–30. http://dx.doi.org/10.58292/ct.v14.9296.
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A healthy, 4-year-old, Labrador Retriever was presented for routine semen collection for cryopreservation. Ejaculate had an abundanceof atypical round cells along with substantial morphologically abnormal sperm. Atypical round cells were identified aspremature germ cells, indicating testicular insult or damage of unknown etiology. After multiple visits and a period (3 months) ofrest from heavy training, premature germ cells were not observed and noticeable improvement in sperm morphology was evident.Apparently, changes observed were induced by heat stress given the location and intensity of training, season of the year, historyand signalment of the patient, and the fact that all parameters improved following temperature management changes and restfrom training. This case illustrated the importance of recognizing an unusual cell type during a routine semen analysis and toarrive at a possible etiology and resolution of the clinical problem.
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Affenzeller, Nadja, Rupert Palme, and Helen Zulch. "Playful activity post-learning improves training performance in Labrador Retriever dogs ( Canis lupus familiaris )." Physiology & Behavior 168 (January 2017): 62–73. http://dx.doi.org/10.1016/j.physbeh.2016.10.014.
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Piscini, A., M. Picchiani, M. Chini, S. Corradini, L. Merucci, F. Del Frate, and S. Stramondo. "A neural network approach for the simultaneous retrieval of volcanic ash parameters and SO<sub>2</sub> using MODIS data." Atmospheric Measurement Techniques Discussions 7, no. 4 (April 4, 2014): 3349–95. http://dx.doi.org/10.5194/amtd-7-3349-2014.
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Abstract. In this work neural networks have been used for the retrieval of volcanic ash and SO2 parameters based on Moderate Resolution Imaging Spectroradiometer (MODIS) multispectral measurements. Different neural networks were built for each parameter to be retrieved, experimenting different topologies and evaluating their performances. As test case the May 2010 Eyjafjallajokull eruption has been considered. A set of six MODIS images have been used for the training and validation phases. In order to estimate of the parameters associated with volcanic eruption such as ash mass, effective radius, aerosol optical depth and sulphur dioxide columnar abundance, the neural networks have been trained by using the retrievals obtained from well known algorithms based on simulated radiances at the top of the atmosphere estimated from radiative transfer models. Three neural network's topologies with a different number of inputs have been compared: (a) only three MODIS TIR channels, (b) all multispectral MODIS channels and (c) only the channels that were selected by a pruning procedure applied to all MODIS channels. Results show that the neural network approach is able to reproduce very well the results obtained from the standard algorithms for all retrieved parameters, showing a root mean square error (RMSE) computed from the validation sets below the target data standard deviation (STD). In particular the network built considering all the MODIS channels gives a better performance in terms of specialization, mainly on images close in time to the training ones, while, as expected, the networks with less inputs reveals a better generalization performance when applied to independent datasets. In order to increase the network generalization capability, a pruning algorithm has been also implemented. Such a procedure permits to operate a features selection, extracting only the most significant MODIS channels from images. The results of pruning revealed that obtained inputs, for all the retrieved parameters, correspond to the TIR channels sensitive to ash, plus some other channels in the visible and mid-infrared spectral ranges. The artificial neural network approach proved to be effective in addressing the inversion problem for the estimation of volcanic ash and SO2 cloud parameters, providing fast and reliable retrievals, which are important requirements during the volcanic crisis.
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SINGH, DEVENDRA, Y. V. RAMA RAO, R. C. BHATIA, S. K. SRIVASTAV, SANT PRASAD, and S. K. MUKHARJEE. "Operational use of improved profiles by using neural network technique derived from NOAA satellites microwave data in NWP model over Indian region." MAUSAM 56, no. 2 (January 20, 2022): 357–66. http://dx.doi.org/10.54302/mausam.v56i2.937.
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India Meteorological Department, New Delhi receives and process NOAA TOVS and ATOVS data in real time. The physical and neural network approaches have been used to retrieve atmospheric temperature and moisture profiles from NOAA-16 & 17 satellites AMSU data over Indian region. The earlier training data set based on global data only for two seasons used in neural network technique has been replaced by new training data set based on regional data over land and ocean for all the seasons. The new training data set has improved the temperature and moisture profiles accuracy retrieved using neural network approach compared to physical method. The detail validation and inter comparisons of temperature and moisture profiles have also been carried out with ECMWF analysis over sea and land separately for different seasons for the year 2002-2003. The performance of neural network technique is found to be superior compared to physical method.
Recently, temperature and moisture profiles retrieved from NOAA-16 ATOVS data over Indian region have been used in regional NWP model for the impact study. The operational NWP system of India Meteorological Department is based on a Limited Area Analysis and Forecasting System (LAFS), which consists of real time processing of data received on Global Telecommunication System (GTS), objective analysis by 3-D multivariate optimum interpolation (OI) scheme and a multi-layer primitive equation model. Several experiments were performed using temperature and moisture profiles retrieved from NOAA-16 ATOVS data. Using this data several experiments were undertaken to examine the impact of these data sets on some of the important weather systems such as monsoon depression, active monsoon conditions during monsoon 2003. The preliminary studies reveal that these additional data have a positive impact on rainfall prediction of the limited area model. Results of specific cases of impact studies are presented in the paper.
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Dimov, Petar, Jennie Hurtig, Konstantinos Georgiou, Dimitrios Theodorou, Blagoi Marinov, and Lars Enochsson. "Effect of video games playing on surgical simulation training: a systematic review." Folia Medica 63, no. 5 (October 31, 2021): 647–56. http://dx.doi.org/10.3897/folmed.63.e67296.
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Introduction: Video games have a positive impact on the skills required for laparoscopic surgery. Several studies have assessed the impact of video games on laparoscopic skills. Aim: This study aims to systematically review the existing evidence. Materials and methods: A search strategy was implemented to retrieve relevant articles from MEDLINE and SCOPUS databases. The retrieved articles were reviewed for further evaluation according to the predetermined inclusion/exclusion criteria. Results: Twenty-six studies were included in this systematic review. These included prospective (n=9), retrospective (n=5) and interventional (n=12). Other review papers were cited in the discussion section. Studies with positive outcomes significantly outweighed the negative ones (21 vs. 5, respectively). Conclusions: Although there is some evidence that video game experience could give some advantage in laparoscopy no firm conclusions could be drawn yet. The reasons for that lay in the various aims, approaches and results of different study reports. Gaming could be used as a daily warm-up or as a tool to speed-up mastering new skills. A standardized protocol is needed for answering the different questions regarding the impact of video game exposure to laparoscopic skills development and progression.
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Lai, Wendian, Zhongping Lee, Junwei Wang, Yongchao Wang, Rodrigo Garcia, and Huaguo Zhang. "A Portable Algorithm to Retrieve Bottom Depth of Optically Shallow Waters from Top-Of-Atmosphere Measurements." Journal of Remote Sensing 2022 (February 3, 2022): 1–16. http://dx.doi.org/10.34133/2022/9831947.
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Bottom depth (H) of optically shallow waters can be retrieved from multiband imagery, where remote sensing reflectance (Rrs) are commonly used as the input. Because of the difficulties of removing the atmospheric effects in coastal areas, quite often, there are no valid Rrs from satellites for the retrieval of H. More importantly, the empirical algorithms for H are hardly portable to new measurements. In this study, using data from Landsat-8 and ICESat-2 as examples, we present an approach to retrieve H directly from the top-of-atmosphere (TOA) data. It not only bypasses the requirement to correct the effects of aerosols but also shows promising portability to areas not included in algorithm development. Specifically, we use Rayleigh-corrected TOA reflectance (ρrc) in the 443–2300 nm range as input, along with a multilayer perceptron (MLPHρrc), for the retrieval of H. More than 78,000 matchup points of ρrc and H (0–25 m) were used to train MLPHρrc, which resulted in a Mean Absolute Percentage Difference (MARD) of 8.8% and a coefficient of determination (R2) of 0.96. This MLPHρrc was further applied to Landsat-8 data of six regions not included in the training phase, generating MARD and R2 values of 8.3% and 0.98, respectively. In contrast, a conventional two-band ratio algorithm with Rrs as the input generated MARD and R2 values of 31.6% and 0.68 and significantly fewer H retrievals due to failures in atmospheric correction. These results indicate a breakthrough of algorithm portability of MLPHρrc in sensing H of optically shallow waters.
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Wu, Chenxiao, Wenjun Ke, Peng Wang, Zhizhao Luo, Guozheng Li, and Wanyi Chen. "ConsistNER: Towards Instructive NER Demonstrations for LLMs with the Consistency of Ontology and Context." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (March 24, 2024): 19234–42. http://dx.doi.org/10.1609/aaai.v38i17.29892.
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Named entity recognition (NER) aims to identify and classify specific entities mentioned in textual sentences. Most existing superior NER models employ the standard fully supervised paradigm, which requires a large amount of annotated data during training. In order to maintain performance with insufficient annotation resources (i.e., low resources), in-context learning (ICL) has drawn a lot of attention, due to its plug-and-play nature compared to other methods (e.g., meta-learning and prompt learning). In this manner, how to retrieve high-correlated demonstrations for target sentences serves as the key to emerging ICL ability. For the NER task, the correlation implies the consistency of both ontology (i.e., generalized entity type) and context (i.e., sentence semantic), which is ignored by previous NER demonstration retrieval techniques. To address this issue, we propose ConsistNER, a novel three-stage framework that incorporates ontological and contextual information for low-resource NER. Firstly, ConsistNER employs large language models (LLMs) to pre-recognize potential entities in a zero-shot manner. Secondly, ConsistNER retrieves the sentence-specific demonstrations for each target sentence based on the two following considerations: (1) Regarding ontological consistency, demonstrations are filtered into a candidate set based on ontology distribution. (2) Regarding contextual consistency, an entity-aware self-attention mechanism is introduced to focus more on the potential entities and semantic-correlated tokens. Finally, ConsistNER feeds the retrieved demonstrations for all target sentences into LLMs for prediction. We conduct experiments on four widely-adopted NER datasets, including both general and specific domains. Experimental results show that ConsistNER achieves a 6.01%-26.37% and 3.07%-21.18% improvement over the state-of-the-art baselines on Micro-F1 scores under 1- and 5-shot settings, respectively.
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Alemohammad, Seyed Hamed, Bin Fang, Alexandra G. Konings, Filipe Aires, Julia K. Green, Jana Kolassa, Diego Miralles, Catherine Prigent, and Pierre Gentine. "Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence." Biogeosciences 14, no. 18 (September 20, 2017): 4101–24. http://dx.doi.org/10.5194/bg-14-4101-2017.
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Abstract. A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H, and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H, and GPP from 2007 to 2015 at 1° × 1° spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events. Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events – such as the 2015 El Niño – on surface turbulent fluxes and GPP.
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Fang, He, Tao Xie, William Perrie, Guosheng Zhang, Jingsong Yang, and Yijun He. "Comparison of C-Band Quad-Polarization Synthetic Aperture Radar Wind Retrieval Models." Remote Sensing 10, no. 9 (September 11, 2018): 1448. http://dx.doi.org/10.3390/rs10091448.
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This work discusses the accuracy of C-2PO (C-band cross-polarized ocean backscatter) and CMOD4 (C-band model) geophysical model functions (GMF) for sea surface wind speed retrieval from satellite-born Synthetic Aperture Radar (SAR) images over in the Northwest Pacific off the coast of China. In situ observations are used for comparison of the retrieved wind speed using two established wind retrieval models: C-2PO model and CMOD4 GMF. Using 439 samples from 92 RADARSAT-2 fine quad-polarization SAR images and corresponding reference winds, we created two subset wind speed databases: the training and testing subsets. From the training data subset, we retrieve ocean surface wind speeds (OSWSs) from different models at each polarization and compare with reference wind speeds. The RMSEs of SAR-retrieved wind speeds are: 2.5 m/s: 2.11 m/s (VH-polarized), 2.13 m/s (HV-polarized), 1.86 m/s (VV-polarized) and 2.26 m/s (HH-polarized) and the correlation coefficients are 0.86 (VH-polarized), 0.85(HV-polarized), 0.87(VV-polarized) and 0.83 (HH-polarized), which are statistically significant at the 99.9% significance level. Moreover, we found that OSWSs retrieved using C-2PO model at VH-polarized are most suitable for moderate-to-high winds while CMOD4 GMF at VV-polarized tend to be best for low-to-moderate winds. A hybrid wind retrieval model is put forward composed of the two models, C-2PO and CMOD4 and sets of SAR test data are used in order to establish an appropriate wind speed threshold, to differentiate the wind speed range appropriate for one model from that of the other. The results show that the OSWSs retrieved using our hybrid method has RMSE of 1.66 m/s and the correlation coefficient are 0.9, thereby significantly outperforming both the C-2PO and CMOD4 models.
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Albougami, Abdulrhman. "Oral Health Literacy Levels of Nursing Professionals and Effectiveness of Integrating Oral Health Training into Nursing Curricula: A Systematic Review." Applied Sciences 13, no. 18 (September 17, 2023): 10403. http://dx.doi.org/10.3390/app131810403.
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This systematic review assessed the evidence for the oral health literacy levels (i.e., knowledge, attitudes, barriers, oral care and practices, and trainings and resources) of nursing professionals and the effectiveness of integrating oral health training into nursing training. Four electronic databases were searched; however, for relevance, only evidence published between 2013 and 2023 was considered. Overall, 70 studies that focused on five key themes, namely, (i) knowledge of oral healthcare among nurses; (ii) attitudes of nurses towards oral healthcare; (iii) barriers to oral healthcare promotion; (iv) oral care and practices; and (v) trainings and resources to promote oral healthcare, were retrieved. Nurses were found to have a lack of or suboptimal of knowledge regarding oral healthcare. Moreover, their attitudes and practices related to the provision of oral healthcare varied substantially. Key barriers that impeded oral healthcare promotion included a lack of knowledge, awareness, education, skills, and training. Integrating oral health training was considered effective for improving oral health literacy and nurses emphasized the inclusion of such training into their curricula for improving oral healthcare. In summary, nurses have an important role to play in promoting oral health. Furthermore, integration of oral health training into nursing curricula could be a feasible approach to improve the oral health literacy of nurses and reduce the burden of oral disease.
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Gholami, Sia, and Mehdi Noori. "You Don’t Need Labeled Data for Open-Book Question Answering." Applied Sciences 12, no. 1 (December 23, 2021): 111. http://dx.doi.org/10.3390/app12010111.
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Open-book question answering is a subset of question answering (QA) tasks where the system aims to find answers in a given set of documents (open-book) and common knowledge about a topic. This article proposes a solution for answering natural language questions from a corpus of Amazon Web Services (AWS) technical documents with no domain-specific labeled data (zero-shot). These questions have a yes–no–none answer and a text answer which can be short (a few words) or long (a few sentences). We present a two-step, retriever–extractor architecture in which a retriever finds the right documents and an extractor finds the answers in the retrieved documents. To test our solution, we are introducing a new dataset for open-book QA based on real customer questions on AWS technical documentation. In this paper, we conducted experiments on several information retrieval systems and extractive language models, attempting to find the yes–no–none answers and text answers in the same pass. Our custom-built extractor model is created from a pretrained language model and fine-tuned on the the Stanford Question Answering Dataset—SQuAD and Natural Questions datasets. We were able to achieve 42% F1 and 39% exact match score (EM) end-to-end with no domain-specific training.
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Goy, Alexandre, Girish Rughoobur, Shuai Li, Kwabena Arthur, Akintunde I. Akinwande, and George Barbastathis. "High-resolution limited-angle phase tomography of dense layered objects using deep neural networks." Proceedings of the National Academy of Sciences 116, no. 40 (September 16, 2019): 19848–56. http://dx.doi.org/10.1073/pnas.1821378116.
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We present a machine learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to ±10○. Whereas previous approaches to phase tomography generally require 2 steps, first to retrieve phase projections from intensity projections and then to perform tomographic reconstruction on the retrieved phase projections, in our work a physics-informed preprocessor followed by a deep neural network (DNN) conduct the 3-dimensional reconstruction directly from the intensity projections. We demonstrate this single-step method experimentally in the visible optical domain on a scaled-up integrated circuit phantom. We show that even under conditions of highly attenuated photon fluxes a DNN trained only on synthetic data can be used to successfully reconstruct physical samples disjoint from the synthetic training set. Thus, the need for producing a large number of physical examples for training is ameliorated. The method is generally applicable to tomography with electromagnetic or other types of radiation at all bands.
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Di Noia, A., P. Sellitto, F. Del Frate, and J. de Laat. "Global tropospheric ozone column retrievals from OMI data by means of neural networks." Atmospheric Measurement Techniques Discussions 5, no. 5 (October 22, 2012): 7675–727. http://dx.doi.org/10.5194/amtd-5-7675-2012.
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Abstract. In this paper, a new Neural Network (NN) algorithm to retrieve the tropospheric ozone column from Ozone Monitoring Instrument (OMI) Level 1b data is presented. Such algorithm further develops previous studies in order to improve: (i) the geographical coverage of the NN, by extending its training set to ozonesonde data from midlatitudes, tropics and poles; (ii) the definition of the output product, by using tropopause pressure information from reanalysis data; and (iii) the retrieval accuracy, by using ancillary data to better constrain the tropospheric ozone retrievals from OMI radiances. The results indicate that the algorithm is able to retrieve the tropospheric ozone column with a Root Mean Square Error (RMSE) of about 5–6 DU in all the latitude bands. The design of the new NN algorithm is extensively discussed, validation results against independent ozone soundings and Chemistry/Transport Model (CTM) simulations are shown, and other characteristics of the algorithm – i.e. its capability to detect nonclimatological tropospheric ozone situations and its sensitivity to the tropopause pressure – are discussed.
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Kang, Shin-Hoo, Tae-Young Goo, and Mi-Lim Ou. "Improvement of AERI T/q Retrievals and Their Validation at Anmyeon-Do, South Korea." Journal of Atmospheric and Oceanic Technology 30, no. 7 (July 1, 2013): 1433–46. http://dx.doi.org/10.1175/jtech-d-12-00029.1.
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Abstract An Atmospheric Emitted Radiance Interferometer (AERI), which measures downwelling radiances, has been in operation at Anmyeon-do, South Korea, since June 2010. Temperature and moisture (T/q) profiles with high temporal and vertical resolution can be retrieved from the measured AERI spectrum through the retrieval algorithm AERIPROF. In this work, AERIPROF has been optimized to improve the retrieval performance: 1) a bias spectrum was computed from the coincident radiosondes during the field experiments at Anmyeon-do and 2) regression coefficients were obtained from local radiosondes and associated simulated spectral radiances. An evaluation was performed in the lower troposphere (<700 hPa) with the radiosondes on clear-sky days during the field experiments at Anmyeon-do. The optimized statistical regression results in an improvement of ~0.6 K for temperature and ~0.6 g kg−1 for the mixing ratio on average, in comparison to the original statistical regression. In addition, the optimized AERI T/q retrievals are compared with the satellite [Aqua/Atmospheric Infrared Sounder (AIRS), Meteorological Operation (MetOp)/Infrared Atmospheric Sounding Interferometer (IASI)] T/q retrievals as well as with T/q profiles analyzed from the regional NWP model, the Korea Local Analysis and Prediction System (KLAPS) analysis. The RMS errors of the AERI retrievals are smaller than those of the satellite retrievals (the KLAPS analysis) by ~1.3 K (~0.2 K) for temperature and ~0.3 g kg−1 (~0.2 g kg−1) for the mixing ratio on average. Significant differences could be found between the AERI retrievals with the KLAPS and the satellite retrievals. The local climatic condition seems to be an important factor to bring about this improvement. Considering the training dataset made with spatially distant radiosondes, this is a significant finding. The AERI could bring new information about the lower troposphere.
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Dekemper, E., F. Vanhellemont, N. Mateshvili, G. Franssens, D. Pieroux, C. Bingen, C. Robert, and D. Fussen. "Zernike polynomials applied to apparent solar disk flattening for pressure profile retrievals." Atmospheric Measurement Techniques 6, no. 3 (March 27, 2013): 823–35. http://dx.doi.org/10.5194/amt-6-823-2013.
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Abstract. We present a passive method for the retrieval of atmospheric pressure profiles based on the measurement of the apparent flattening of the solar disk as observed through the atmosphere by a spaceborne imager. This method was applied to simulated sunsets. It relies on accurate representation of the solar disk, including its limb darkening, and how its image is affected by atmospheric refraction. The Zernike polynomials are used to quantify the flattening in the Sun images. The inversion algorithm relies on a transfer matrix providing the link between the atmospheric pressure profile and a sequence of Zernike moments computed on the sunset frames. The transfer matrix is determined by a training dataset of pressure profiles generated from a standard climatology. The performance and limitations of the method are assessed by two test cases. Pressure profiles similar to the training dataset show that retrieval error can be up to 10 times smaller than the natural variability in the lower mesosphere, and up to 500 times smaller in the upper troposphere. Tests with other independent profiles emphasize the need for better representativeness of the training dataset.