| 27831. |
- Balfors, Berit, et al.
(författare)
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Towards a climate resilient society : tools for impact assessment of infrastructure and urban development
- 2010
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- During recent years, climate change aspects have received increased attention in urban planning and infrastructure development. In order to effectively address impacts on climate change and measures towards energy efficiency, a strategic approach in the planning process is required. To enable an early appraisal of alternative climate change adaptation scenarios, SEA could provide a suitable framework. The application of SEA in urban planning and infrastructure development entail various challenges so as to address, e.g., cumulative impacts, transboundary and multi-scalar issues. The incorporation of strategic issues related to climate change, call for analytical tools and methodological approaches that facilitate the planning and decision-making process. In this study we focus on the development of prediction tools and decision support systems in order to assist a comprehensive comparison of alternative strategies and identify innovative energy efficient solutions for a climate resilient society.
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| 27832. |
- Balgi, Sourabh, 1991-, et al.
(författare)
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Contradistinguisher : A Vapnik’s Imperative to Unsupervised Domain Adaptation
- 2022
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - Piscataway, NJ, United States : Institute of Electrical and Electronics Engineers (IEEE). - 0162-8828 .- 1939-3539. ; 44:9, s. 4730-4747
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Tidskriftsartikel (refereegranskat)abstract
- Recent domain adaptation works rely on an indirect way of first aligning the source and target domain distributions and then train a classifier on the labeled source domain to classify the target domain. However, the main drawback of this approach is that obtaining a near-perfect domain alignment in itself might be difficult/impossible (e.g., language domains). To address this, inspired by how humans use supervised-unsupervised learning to perform tasks seamlessly across multiple domains or tasks, we follow Vapnik’s imperative of statistical learning that states any desired problem should be solved in the most direct way rather than solving a more general intermediate task and propose a direct approach to domain adaptation that does not require domain alignment. We propose a model referred to as Contradistinguisher that learns contrastive features and whose objective is to jointly learn to contradistinguish the unlabeled target domain in an unsupervised way and classify in a supervised way on the source domain. We achieve the state-of-the-art on Office-31, Digits and VisDA-2017 datasets in both single-source and multi-source settings. We demonstrate that performing data augmentation results in an improvement in the performance over vanilla approach. We also notice that the contradistinguish-loss enhances performance by increasing the shape bias.
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| 27833. |
- Balgi, Sourabh, 1991-, et al.
(författare)
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CUDA : Contradistinguisher for Unsupervised Domain Adaptation
- 2019
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Ingår i: 2019 IEEE International Conference on Data Mining (ICDM). - New York, NY, United States : IEEE. ; , s. 21-30
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Konferensbidrag (refereegranskat)abstract
- Humans are very sophisticated in learning new information on a completely unknown domain because humans can contradistinguish, i.e., distinguish by contrasting qualities. We learn on a new unknown domain by jointly using unsupervised information directly from unknown domain and supervised information previously acquired knowledge from some other domain. Motivated by this supervised-unsupervised joint learning, we propose a simple model referred as Contradistinguisher (CTDR) for unsupervised domain adaptation whose objective is to jointly learn to contradistinguish on unlabeled target domain in a fully unsupervised manner along with prior knowledge acquired by supervised learning on an entirely different domain. Most recent works in domain adaptation rely on an indirect way of first aligning the source and target domain distributions and then learn a classifier on labeled source domain to classify target domain. This approach of indirect way of addressing the real task of unlabeled target domain classification has three main drawbacks. (i) The sub-task of obtaining a perfect alignment of the domain in itself might be impossible due to large domain shift (e.g., language domains). (ii) The use of multiple classifiers to align the distributions, unnecessarily increases the complexity of the neural networks leading to over-fitting in many cases. (iii) Due to distribution alignment, the domain specific information is lost as the domains get morphed. In this work, we propose a simple and direct approach that does not require domain alignment. We jointly learn CTDR on both source and target distribution for unsupervised domain adaptation task using contradistinguish loss for the unlabeled target domain in conjunction with supervised loss for labeled source domain. Our experiments show that avoiding domain alignment by directly addressing the task of unlabeled target domain classification using CTDR achieves state-of-the-art results on eight visual and four language benchmark domain adaptation datasets.
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| 27834. |
- Balgi, Sourabh, 1991-, et al.
(författare)
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Deep Learning With DAGs
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Social science theories often postulate causal relationships among a set of variables or events. Although directed acyclic graphs (DAGs) are increasingly used to represent these theories, their full potential has not yet been realized in practice. As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships. Nevertheless, to simplify the task of empirical evaluation, researchers tend to invoke such assumptions anyway, even though they are typically arbitrary and do not reflect any theoretical content or prior knowledge. Moreover, functional form assumptions can engender bias, whenever they fail to accurately capture the complexity of the causal system under investigation. In this article, we introduce causal-graphical normalizing flows (cGNFs), a novel approach to causal inference that leverages deep neural networks to empirically evaluate theories represented as DAGs. Unlike conventional approaches, cGNFs model the full joint distribution of the data according to a DAG supplied by the analyst, without relying on stringent assumptions about functional form. In this way, the method allows for flexible, semi-parametric estimation of any causal estimand that can be identified from the DAG, including total effects, conditional effects, direct and indirect effects, and path-specific effects. We illustrate the method with a reanalysis of Blau and Duncan’s (1967) model of status attainment and Zhou’s (2019) model of conditional versus controlled mobility. To facilitate adoption, we provide open-source software together with a series of online tutorials for implementing cGNFs. The article concludes with a discussion of current limitations and directions for future development.
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| 27835. |
- Balgi, Sourabh, 1991-, et al.
(författare)
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Personalized Public Policy Analysis in Social Sciences Using Causal-Graphical Normalizing Flows
- 2022
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Ingår i: Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence. - Palo Alto, California USA : AAAI Press. ; 36, s. 11810-11818
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Konferensbidrag (refereegranskat)abstract
- Structural Equation/Causal Models (SEMs/SCMs) are widely used in epidemiology and social sciences to identify and analyze the average causal effect (ACE) and conditional ACE (CACE). Traditional causal effect estimation methods such as Inverse Probability Weighting (IPW) and more recently Regression-With-Residuals (RWR) are widely used - as they avoid the challenging task of identifying the SCM parameters - to estimate ACE and CACE. However, much work remains before traditional estimation methods can be used for counterfactual inference, and for the benefit of Personalized Public Policy Analysis (P3A) in the social sciences. While doctors rely on personalized medicine to tailor treatments to patients in laboratory settings (relatively closed systems), P3A draws inspiration from such tailoring but adapts it for open social systems. In this article, we develop a method for counterfactual inference that we name causal-Graphical Normalizing Flow (c-GNF), facilitating P3A. A major advantage of c-GNF is that it suits the open system in which P3A is conducted. First, we show how c-GNF captures the underlying SCM without making any assumption about functional forms. This capturing capability is enabled by the deep neural networks that model the underlying SCM via observational data likelihood maximization using gradient descent. Second, we propose a novel dequantization trick to deal with discrete variables, which is a limitation of normalizing flows in general. Third, we demonstrate in experiments that c-GNF performs on-par with IPW and RWR in terms of bias and variance for estimating the ACE, when the true functional forms are known, and better when they are unknown. Fourth and most importantly, we conduct counterfactual inference with c-GNFs, demonstrating promising empirical performance. Because IPW and RWR, like other traditional methods, lack the capability of counterfactual inference, c-GNFs will likely play a major role in tailoring personalized treatment, facilitating P3A, optimizing social interventions - in contrast to the current `one-size-fits-all' approach of existing methods.
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| 27836. |
- Balgi, Sourabh, 1991-, et al.
(författare)
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ρ-GNF : A Novel Sensitivity Analysis Approach Under Unobserved Confounders
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- We propose a new sensitivity analysis model that combines copulas and normalizing flows for causal inference under unobserved confounding. We refer to the new model as ρ-GNF (ρ-Graphical Normalizing Flow), where ρ∈[−1,+1] is a bounded sensitivity parameter representing the backdoor non-causal association due to unobserved confounding modeled using the most well studied and widely popular Gaussian copula. Specifically, ρ-GNF enables us to estimate and analyse the frontdoor causal effect or average causal effect (ACE) as a function of ρ. We call this the ρcurve. The ρcurve enables us to specify the confounding strength required to nullify the ACE. We call this the ρvalue. Further, the ρcurve also enables us to provide bounds for the ACE given an interval of ρ values. We illustrate the benefits of ρ-GNF with experiments on simulated and real-world data in terms of our empirical ACE bounds being narrower than other popular ACE bounds.
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| 27837. |
- Balgobin, Neil
(författare)
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Studies on oligodeoxyribonucleotide (DNA) chemistry
- 1982
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This communication discusses some of the more common problems encountered in the chemical synthesis of oligodeoxyribonucleotides. Several new protecting groups are introduced, such as the 2-phenylsulphonylethyl and the fluoren-9-methyl, for terminal phosphodiester protection, during the block synthesis of oligodeoxyribonucleotides, following the Catlin-Cramer approach. The 2-phenylsulphonylethoxycarbonyl, and its 4-chlorophenyl derivative, have a- Iso been introduced in the present work, for the protection of the hydroxyl function. The applicabilities of these groups are demonstrated by the actual syntheses of several short oligodeoxyribonucleotides, and their characterizations are described. The present report also incorporates a method for the synthesis of oligodeoxyribonucleotides employing only base-labile protecting groups, and thus completely avoiding the use of acidic conditions, which invariably causes the cleavage of the sugar-purine linkage. Such a methodology has been demonstrated by the synthesis of dodecadeoxyadenylic acid.
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| 27838. |
- Balgoma, David, et al.
(författare)
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Anabolic androgenic steroids exert a selective remodeling of the plasma lipidome that mirrors the decrease of the de novo lipogenesis in the liver
- 2020
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Ingår i: Metabolomics. - : SPRINGER. - 1573-3882 .- 1573-3890. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: The abuse of anabolic androgenic steroids (AASs) is a source of public concern because of their adverse effects. Supratherapeutic doses of AASs are known to be hepatotoxic and regulate the lipoproteins in plasma by modifying the metabolism of lipids in the liver, which is associated with metabolic diseases. However, the effect of AASs on the profile of lipids in plasma is unknown.Objectives: To describe the changes in the plasma lipidome exerted by AASs and to discuss these changes in the light of previous research about AASs and de novo lipogenesis in the liver.Methods: We treated male Wistar rats with supratherapeutic doses of nandrolone decanoate and testosterone undecanoate. Subsequently, we isolated the blood plasma and performed lipidomics analysis by liquid chromatography-high resolution mass spectrometry.Results: Lipid profiling revealed a decrease of sphingolipids and glycerolipids with palmitic, palmitoleic, stearic, and oleic acids. In addition, lipid profiling revealed an increase in free fatty acids and glycerophospholipids with odd-numbered chain fatty acids and/or arachidonic acid.Conclusion: The lipid profile presented herein reports the imprint of AASs on the plasma lipidome, which mirrors the downregulation of de novo lipogenesis in the liver. In a broader perspective, this profile will help to understand the influence of androgens on the lipid metabolism in future studies of diseases with dysregulated lipogenesis (e.g. type 2 diabetes, fatty liver disease, and hepatocellular carcinoma).
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| 27839. |
- Balgoma, David, et al.
(författare)
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Anthracyclins Increase PUFAs : Potential Implications in ER Stress and Cell Death
- 2021
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Ingår i: Cells. - : MDPI. - 2073-4409. ; 10:5
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Tidskriftsartikel (refereegranskat)abstract
- Metabolic and personalized interventions in cancer treatment require a better understanding of the relationship between the induction of cell death and metabolism. Consequently, we treated three primary liver cancer cell lines with two anthracyclins (doxorubicin and idarubin) and studied the changes in the lipidome. We found that both anthracyclins in the three cell lines increased the levels of polyunsaturated fatty acids (PUFAs) and alkylacylglycerophosphoethanolamines (etherPEs) with PUFAs. As PUFAs and alkylacylglycerophospholipids with PUFAs are fundamental in lipid peroxidation during ferroptotic cell death, our results suggest supplementation with PUFAs and/or etherPEs with PUFAs as a potential general adjuvant of anthracyclins. In contrast, neither the markers of de novo lipogenesis nor cholesterol lipids presented the same trend in all cell lines and treatments. In agreement with previous research, this suggests that modulation of the metabolism of cholesterol could be considered a specific adjuvant of anthracyclins depending on the type of tumor and the individual. Finally, in agreement with previous research, we found a relationship across the different cell types between: (i) the change in endoplasmic reticulum (ER) stress, and (ii) the imbalance between PUFAs and cholesterol and saturated lipids. In the light of previous research, this imbalance partially explains the sensitivity to anthracyclins of the different cells. In conclusion, our results suggest that the modulation of different lipid metabolic pathways may be considered for generalized and personalized metabochemotherapies.
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| 27840. |
- Balgoma, David, et al.
(författare)
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Lipidomics Issues on Human Positive ssRNA Virus Infection : An Update
- 2020
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Ingår i: Metabolites. - : MDPI AG. - 2218-1989. ; 10:9
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Forskningsöversikt (refereegranskat)abstract
- The pathogenic mechanisms underlying the Biology and Biochemistry of viral infections are known to depend on the lipid metabolism of infected cells. From a lipidomics viewpoint, there are a variety of mechanisms involving virus infection that encompass virus entry, the disturbance of host cell lipid metabolism, and the role played by diverse lipids in regard to the infection effectiveness. All these aspects have currently been tackled separately as independent issues and focused on the function of proteins. Here, we review the role of cholesterol and other lipids in ssRNA+ infection.
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