Investigating the Effects of Word Substitution Errors on Sentence Embeddings

A key initial step in several natural language processing (NLP) tasks involves embedding phrases of text to vectors of real numbers that preserve semantic meaning. To that end, several methods have been recently proposed with impressive results on semantic similarity tasks. However, all of these approaches assume that perfect transcripts are available when generating the embeddings. While this is a reasonable assumption for analysis of written text, it is limiting for analysis of transcribed text. In this paper we investigate the effects of word substitution errors, such as those coming from automatic speech recognition errors (ASR), on several state-of-the-art sentence embedding methods. To do this, we propose a new simulator that allows the experimenter to induce ASR-plausible word substitution errors in a corpus at a desired word error rate. We use this simulator to evaluate the robustness of several sentence embedding methods. Our results show that pre-trained neural sentence encoders are both robust to ASR errors and perform well on textual similarity tasks after errors are introduced. Meanwhile, unweighted averages of word vectors perform well with perfect transcriptions, but their performance degrades rapidly on textual similarity tasks for text with word substitution errors.Comment: 4 Pages, 2 figures. Copyright IEEE 2019. Accepted and to appear in the Proceedings of the 44th International Conference on Acoustics, Speech, and Signal Processing 2019 (IEEE-ICASSP-2019), May 12-17 in Brighton, U.K. Personal use of this material is permitted. However, permission to reprint/republish this material must be obtained from the IEE

Conditional generative modeling for images, 3D animations, and video

Generative modeling for computer vision has shown immense progress in the last few years, revolutionizing the way we perceive, understand, and manipulate visual data. This rapidly evolving field has witnessed advancements in image generation, 3D animation, and video prediction that unlock diverse applications across multiple fields including entertainment, design, healthcare, and education. As the demand for sophisticated computer vision systems continues to grow, this dissertation attempts to drive innovation in the field by exploring novel formulations of conditional generative models, and innovative applications in images, 3D animations, and video. Our research focuses on architectures that offer reversible transformations of noise and visual data, and the application of encoder-decoder architectures for generative tasks and 3D content manipulation. In all instances, we incorporate conditional information to enhance the synthesis of visual data, improving the efficiency of the generation process as well as the generated content. Prior successful generative techniques which are reversible between noise and data include normalizing flows and denoising diffusion models. The continuous variant of normalizing flows is powered by Neural Ordinary Differential Equations (Neural ODEs), and have shown some success in modeling the real image distribution. However, they often involve huge number of parameters, and high training time. Denoising diffusion models have recently gained huge popularity for their generalization capabilities especially in text-to-image applications. In this dissertation, we introduce the use of Neural ODEs to model video dynamics using an encoder-decoder architecture, demonstrating their ability to predict future video frames despite being trained solely to reconstruct current frames. In our next contribution, we propose a conditional variant of continuous normalizing flows that enables higher-resolution image generation based on lower-resolution input. This allows us to achieve comparable image quality to regular normalizing flows, while significantly reducing the number of parameters and training time. Our next contribution focuses on a flexible encoder-decoder architecture for accurate estimation and editing of full 3D human pose. We present a comprehensive pipeline that takes human images as input, automatically aligns a user-specified 3D human/non-human character with the pose of the human, and facilitates pose editing based on partial input information. We then proceed to use denoising diffusion models for image and video generation. Regular diffusion models involve the use of a Gaussian process to add noise to clean images. In our next contribution, we derive the relevant mathematical details for denoising diffusion models that use non-isotropic Gaussian processes, present non-isotropic noise, and show that the quality of generated images is comparable with the original formulation. In our final contribution, devise a novel framework building on denoising diffusion models that is capable of solving all three video tasks of prediction, generation, and interpolation. We perform ablation studies using this framework, and show state-of-the-art results on multiple datasets. Our contributions are published articles at peer-reviewed venues. Overall, our research aims to make a meaningful contribution to the pursuit of more efficient and flexible generative models, with the potential to shape the future of computer vision.La modélisation générative pour la vision par ordinateur a connu d’immenses progrès ces dernières années, révolutionnant notre façon de percevoir, comprendre et manipuler les données visuelles. Ce domaine en constante évolution a connu des avancées dans la génération d’images, l’animation 3D et la prédiction vidéo, débloquant ainsi diverses applications dans plusieurs domaines tels que le divertissement, le design, la santé et l’éducation. Alors que la demande de systèmes de vision par ordinateur sophistiqués ne cesse de croître, cette thèse s’efforce de stimuler l’innovation dans le domaine en explorant de nouvelles formulations de modèles génératifs conditionnels et des applications innovantes dans les images, les animations 3D et la vidéo. Notre recherche se concentre sur des architectures offrant des transformations réversibles du bruit et des données visuelles, ainsi que sur l’application d’architectures encodeur-décodeur pour les tâches génératives et la manipulation de contenu 3D. Dans tous les cas, nous incorporons des informations conditionnelles pour améliorer la synthèse des données visuelles, améliorant ainsi l’efficacité du processus de génération ainsi que le contenu généré. Les techniques génératives antérieures qui sont réversibles entre le bruit et les données et qui ont connu un certain succès comprennent les flux de normalisation et les modèles de diffusion de débruitage. La variante continue des flux de normalisation est alimentée par les équations différentielles ordinaires neuronales (Neural ODEs) et a montré une certaine réussite dans la modélisation de la distribution d’images réelles. Cependant, elles impliquent souvent un grand nombre de paramètres et un temps d’entraînement élevé. Les modèles de diffusion de débruitage ont récemment gagné énormément en popularité en raison de leurs capacités de généralisation, notamment dans les applications de texte vers image. Dans cette thèse, nous introduisons l’utilisation des Neural ODEs pour modéliser la dynamique vidéo à l’aide d’une architecture encodeur-décodeur, démontrant leur capacité à prédire les images vidéo futures malgré le fait d’être entraînées uniquement à reconstruire les images actuelles. Dans notre prochaine contribution, nous proposons une variante conditionnelle des flux de normalisation continus qui permet une génération d’images à résolution supérieure à partir d’une entrée à résolution inférieure. Cela nous permet d’obtenir une qualité d’image comparable à celle des flux de normalisation réguliers, tout en réduisant considérablement le nombre de paramètres et le temps d’entraînement. Notre prochaine contribution se concentre sur une architecture encodeur-décodeur flexible pour l’estimation et l’édition précises de la pose humaine en 3D. Nous présentons un pipeline complet qui prend des images de personnes en entrée, aligne automatiquement un personnage 3D humain/non humain spécifié par l’utilisateur sur la pose de la personne, et facilite l’édition de la pose en fonction d’informations partielles. Nous utilisons ensuite des modèles de diffusion de débruitage pour la génération d’images et de vidéos. Les modèles de diffusion réguliers impliquent l’utilisation d’un processus gaussien pour ajouter du bruit aux images propres. Dans notre prochaine contribution, nous dérivons les détails mathématiques pertinents pour les modèles de diffusion de débruitage qui utilisent des processus gaussiens non isotropes, présentons du bruit non isotrope, et montrons que la qualité des images générées est comparable à la formulation d’origine. Dans notre dernière contribution, nous concevons un nouveau cadre basé sur les modèles de diffusion de débruitage, capable de résoudre les trois tâches vidéo de prédiction, de génération et d’interpolation. Nous réalisons des études d’ablation en utilisant ce cadre et montrons des résultats de pointe sur plusieurs ensembles de données. Nos contributions sont des articles publiés dans des revues à comité de lecture. Dans l’ensemble, notre recherche vise à apporter une contribution significative à la poursuite de modèles génératifs plus efficaces et flexibles, avec le potentiel de façonner l’avenir de la vision par ordinateur

Effects of Low Concentrations of Carbon Nanotubes on Growth and Gas Exchange in Arabidopsis Thaliana

The effect of pure single-walled carbon nanotubes (SWCNTs) on plant growth and gas exchange was investigated in Arabidopsis thaliana. To date there has been no research on the effects of SWCNTs on whole plant physiology. A. thaliana seeds were directly grown in growth medium containing SWCNTs concentrations of 24.93micrograms/ml and 53.55 micrograms/ml. control plants were grown in media containing distilled water. I determined growth by measuring dry mass of plants. I determined gas exchange by measuring photosynthetic rates, stomatal conductance, transpiration rates, and water use efficiency. I also examined the following physiological mechanisms that would limit plant growth: ATP and NADPH supply to light reactions through photosynthetic light response curves, and rubisco activity through photosynthetic CO2\u3eresponse curves. The presence of SWCNTs in the growth medium had no impact on the whole plant dry weight accumulation in any of the six experimental trials I carried out. Plants grown in growth media containing SWCNTs of a concentration of 24.93 micrograms/ml (4 experimental trials, n=12) and 53.55 micrograms/ml (1 trial, n=3) did not significantly influence any gas exchange variable after 21 days of growth. I also examined gas exchange variables after 7, 14, and 21 days of growth (1 trial, n=3). In this trial, there was a statistically significant treatment and time effect on photosaturated photosynthetic rate, photosynthetic efficiency and water use efficiency. My study illustrates that pure SWCNTs at realistic environmental conditions have no serious negative effects on plant growth and gas exchange; however, they may affect plant developmental rates. These findings have implications for plant and animal health, public awareness, and environmental remediation. KEYWORDS: Arabidopsis thaliana, Single-walled carbon nanotubes, growth, gas exchange, physiolog

Mechanism of Transcriptional Regulation of C-Reactive Protein Gene Expression.

C-reactive protein (CRP) is an acute phase protein produced by hepatocytes whose serum concentration increases in inflammatory conditions including cardiovascular complications. Statins that are used in the treatment of cardiovascular diseases to reduce cholesterol also lower serum CRP levels. In human hepatoma Hep3B cells, CRP is induced in response to cytokines IL-6 and IL-1β. The objective of the study was to determine the mechanism of regulation of CRP gene expression in Hep3B cells in response to cytokines and to determine the effect of statins on CRP expression. Key findings of our research were: 1. IL-1β-activated NF-κB p50/p65 acted synergistically with IL-6-activated C/EBPβ in inducing CRP transactivation through the proximal CRP promoter. 2. A NF-κB site was localized in the proximal CRP promoter centered at position -69 overlapping the known OCT-1/HNF-1/HNF-3 sites. 3. The synergy between IL-6 and IL-1β in inducing CRP gene expression was partially mediated through the NF-κB site. 4. In the absence of C/EBPβ, a complex containing C/EBPζ and RBP-Jκ was formed at the C/EBP-p50-site. 5. Overexpressed C/EBPζ repressed both (IL-6+IL-1β)-induced and C/EBPβ-induced CRP expression. 6. OCT-1 repressed (IL-6+IL-1β)-induced CRP transactivation through the proximal CRP promoter. 7. Statins reduce cytokine-induced CRP gene expression at the transcriptional level. These findings led us to conclude that: 1. CRP transcription is determined by the relative levels of various transcription factors such as C/EBPβ, C/EBPζ, NF-κB and OCT-1 and their interaction with the proximal CRP promoter. 2. Inhibition of CRP transcription by statins is not due to an anti-inflammatory effect but due to the direct effect on CRP gene expression

Experimental Studies of Vertical Mixing in an Open Channel Raceway for Algae Biofuel Production

Turbulent mixing plays an important role in the distribution of sunlight, carbon dioxide, and nutrients for algae in the raceway ponds. For large-scale raceway ponds the choice of mixing technology still needs to be evaluated in order to prevent algae sedimentation and to enhance light utilization efficiency. In open ponds, mixing the algae culture is of great significance in terms of input energy costs and particularly productivity. A very small amount of research has been performed previously using different vortex generators in the algal raceway ponds, but the quantification of mixing depth relationships is not defined well. By accepting the premise from the literature review that mixing increases algal production, delta wings were selected to study mixing characteristics in the raceway. The main objective of this research was to study algae-raceway hydrodynamics with an emphasis on increasing vertical mixing. A clear acrylic raceway was designed and constructed for flow visualization studies. Experimental investigations were performed to quantify the vertical mixing with and without delta wings in a lab-scale raceway at approximately the same power input to the paddle wheel. Velocity vector profiles and turbulence parameters were measured using an Acoustic Doppler Velocimeter (ADV) at various locations along the entire length of the raceway. The results indicated that the addition of delta wings increases the vertical mixing intensity or circulation of algae cells over the raceway depth. Vortices were observed in the raceway up to a distance of around 3 m downstream of the delta wing. This sort of systematic vertical mixing plays an important role to produce the flashing light effect (light-dark cycles) on algae mass culture. In addition, turbulence dissipation rates were evaluated to compare them with the published literature and to estimate the microscales using the Kolmogorov hypothesis. Also, an energy model was developed to operate the paddlewheel-driven raceway with the delta wing

UTILIZING FEDERATED LEARNING AND META LEARNING FOR FEW-SHOT LEARNING ON EDGE DEVICES

The efficient and effective handling of few-shot learning tasks on mobile devices is challenging due to the small training set issue and the physical limitations in power and computational resources on these devices. In this thesis, we propose a solution that combines federated learning and meta-learning to handle independent few-shot learning tasks on multiple devices (or clients) and the server. In particular, we utilize the Prototypical Networks to perform meta-learning on all devices to learn multiple independent few-shot learning models and to combine the models in a centralized data distributed architecture using federated learning which can be reused by the clients subsequently. We perform extensive experiments to (1) compare three different federated learning approaches, namely Federated Averaging (FedAvg), Federated Proximal (FedProx), and Federated Personalization (FedPer) on our proposed framework, and (2) explore the effect of data heterogeneity issue on the few-shot learning performance. Our empirical results show that our proposed approach is feasible and is able to improve the devices\u27 individual prediction performance and improve significantly on the global model (on the server) using any of the federated learning approaches when the few-shot learning tasks are on the same datasets. However, the data heterogeneity problem still affects the prediction performance of our proposed solution no matter which federated learning approach we used

Patch Burn‐Grazing: An Annotated Bibliography

Patch burn‐grazing is a rangeland management strategy that exploits the attraction of grazing animals to recently burned areas in order to achieve management objectives. When fire is applied to a landscape in a patchy manner, leaving some patches unburned, the resulting grazing animal activity, forage utilization, and animal impact are patchily distributed within that landscape as well. Areas that have been recently burned tend to be characterized by the highest levels of grazing animal activity while areas that have gone the longest without burning tend to be characterized by the lowest levels of grazing animal activity. This can be advantageous for a multitude of reasons related to wildlife conservation, livestock productivity, herbaceous fuel management, invasive species management, and woody plant control. The following annotated bibliography lists resources about patch burn‐grazing in North America. The bibliography includes all citations known by us of research conducted within the context of patch burn‐grazing as an explicit management strategy. Included in the bibliography are papers representing original research, review and synthesis papers, theses (10), and a dissertation. In instances where the research in a thesis or dissertation was subsequently published, we include the citation for the published article(s) but not for the original thesis or dissertation. We did not include reports or extension publications although many valuable publications of this type exist on this topic. For additional resources such as extension publications, look at the Great Plains Fire Science Exchange website or university extension websites in the region

COMPARATIVE FORMULATION, EVALUATION AND OPTIMIZATION OF IMIDAPRIL MOUTH DISSOLVING TABLETS USING DIFFERENT SYNTHETIC SUPERDISINTEGRATING AGENTS

Objective: The aim of this work was preparing once daily mouth disintegrating tablets to handle easily for adult hypertensive patients who have difficulty in swallowing. Methods: Imidapril mouth dissolving tablets (MDTs) were prepared by direct compression method using different concentrations of synthetic super disintegrants namely Sodium starch glycolate, Croscarmellose sodium & Kyron T-314. The prepared tablets were evaluated for weight variation, thickness, hardness, friability, content uniformity, disintegration time and In-vitro dissolution studies.Results: The micropolitics study indicates that all formulations were of acceptable to good flowability. Tablet hardness and friability indicated that the prepared formulations were having good mechanical strength. The most satisfactory formulation F5 containing Sodium starch glycolate showed minimum disintegration time of 19 s and released a maximum amount of drug in 30 min in phosphate buffer 6.8pH, by an appropriate combination of excipients. The optimized F5 formulation containing Sodium starch glycolate was found to be stable during stability studies conducted for 3 mo as per International Conference on Harmonization (ICH) guidelines.Conclusion: The present study conclusively proved that Imidapril MDTs could be successfully developed using various super disintegrants. The prepared tablets gave promising results with respect to the faster release of Imidapril. Further clinical studies are suggested to confirm the ability of the best-achieved system to avoid the first pass metabolism of Imidapril and improve patient compliance.Â