936 research outputs found
Every Other Universe
No full textMaster of Fine Arts (MFA)Helen Well Writers' ProgramUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/155418/1/Annesha Sengupta Thesis.pd
Dataset to accompany "Deposition of brown carbon onto snow: changes of snow optical and radiative properties" by Beres et al., 2020
No full textThis dataset, organized in an Excel spreadsheet, accompanies:
Beres, N. D., Sengupta, D., Samburova, V., Khlystov, A. Y., and Moosmüller, H.: Deposition of brown carbon onto snow: changes in snow optical and radiative properties, Atmos. Chem. Phys., 20, 6095–6114, https://doi.org/10.5194/acp-20-6095-2020, 2020.
Each tab of the spreadsheet represents data presented in Tables and Figures of the manuscript, which allows for the replication of the figure or for use in calculations presented throughout the manuscript.
Any questions or comments should be forwarded to the corresponding author
Deep-learning architecture.
No full textImage shows the deep-learning convolutional neural network architecture used in this study.</p
Deep Learning the Functional Renormalization Group
No full textWe perform a data-driven dimensionality reduction of the scale-dependent
4-point vertex function characterizing the functional Renormalization Group
(fRG) flow for the widely studied two-dimensional Hubbard model on the
square lattice. We demonstrate that a deep learning architecture based on a
Neural Ordinary Differential Equation solver in a low-dimensional latent space
efficiently learns the fRG dynamics that delineates the various magnetic and
-wave superconducting regimes of the Hubbard model. We further present a
Dynamic Mode Decomposition analysis that confirms that a small number of modes
are indeed sufficient to capture the fRG dynamics. Our work demonstrates the
possibility of using artificial intelligence to extract compact representations
of the 4-point vertex functions for correlated electrons, a goal of utmost
importance for the success of cutting-edge quantum field theoretical methods
for tackling the many-electron problem.Comment: 6 pages, 5 figure
Towards Accurate Duplicate Bug Retrieval Using Deep Learning Techniques
No full textDuplicate Bug Detection is the problem of identifying whether a newly reported bug is a duplicate of an existing bug in the system and retrieving the original or similar bugs from the past. This is required to avoid costly rediscovery and redundant work. In typical software projects, the number of duplicate bugs reported may run into the order of thousands, making it expensive in terms of cost and time for manual intervention. This makes the problem of duplicate or similar bug detection an important one in Software Engineering domain. However, an automated solution for the same is not quite accurate yet in practice, in spite of many reported approaches using various machine learning techniques. In this work, we propose a retrieval and classification model using Siamese Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) for accurate detection and retrieval of duplicate and similar bugs. We report an accuracy close to 90% and recall rate close to 80%, which makes possible the practical use of such a system. We describe our model in detail along with related discussions from the Deep Learning domain. By presenting the detailed experimental results, we illustrate the effectiveness of the model in practical systems, including for repositories for which supervised training data is not available.</p
Numerical simulation of the insert chemistry of the hollow cathode from the deep space 1 ion engine 30,000 Hrs life test
No full textA model for the insert chemistry developed by the authors and based on the knowledge of the BaO – CaO – Al2O3 ternary system the ELT discharge cathode insert from the Deep Space 1 life test has been simulated. The computed data show a good agreement with the experimental one; the agreement increase with the imposition of boundary conditions closer to the experimental evidence. Tungsten deposition effect have been introduced into the model using experimental data and further improving the agreement between computed and measured data. The deposition trend found suggests the possibility of a link between barium depletion and tungsten deposition
Pricing Derivatives: The Financial Concepts Underlying the Mathematics of Pricing Derivatives
No full textA fresh, fundamentals-based approach for accurate derivative pricing
Pricing Derivatives presents a specialized approach to accurately pricing derivatives by stressing the conceptual foundations underlying the mathematics. Noted mathematics professor and investing consultant Ambar Sengupta provides a sound understanding of the essential topics of derivative pricing and outlines methodologies for arriving at exact pricing formulas based on the fundamental relationship between price and probability.
Short, to-the-point chapters present original ideas and approaches for pricing derivative products, supplying professional money managers and institutional investors with the foundation they need to: Integrate both the theoretical and mathematical foundations of pricing derivatives Establish optimal prices in terms of the no-arbitrage principle Derive model-independent pricing formulas for options, futures, forwards, and other key derivatives
Experience has shown that derivative traders must focus on conceptual, as opposed to trading, issues if they are to improve trading accuracy and profitability. Pricing Derivatives presents conceptually sound approaches for pricing derivatives and shows how to use them to compute specific pricing formulas.
Pricing Derivatives unveils a fundamentally clear-cut approach to accurate derivative pricing. Based upon author Ambar Sengupta\u27s years of consulting experience working with derivatives traders to hone their trading performance, it steers around the mechanics of popular financial models to focus on the conceptual foundations and underlying mathematics of pricing derivatives as well as other financial instruments.
Exploring the relationshipbetween price and probability, Pricing Derivatives demonstrates methods for determining model-independent pricing formulas and applying them to specific market models for more distinct and applicable pricing formulas.https://repository.lsu.edu/facultybooks/1593/thumbnail.jp
A sequence modeling approach for structured data extraction from unstructured text
No full textExtraction of structured information from unstructured text has always been a problem of interest for NLP community. Structured data is concise to store, search and retrieve; and it facilitates easier human & machine consumption. Traditionally, structured data extraction from text has been done by using various parsing methodologies, applying domain specific rules and heuristics. In this work, we leverage the developments in the space of sequence modeling for the problem of structured data extraction. Initially, we posed the problem as a machine translation problem and used the state-of-the-art machine translation model. Based on these initial results, we changed the approach to a sequence tagging one. We propose an extension of one of the attractive models for sequence tagging tailored and effective to our problem. This gave 4.4% improvement over the vanilla sequence tagging model. We also propose another variant of the sequence tagging model which can handle multiple labels of words. Experiments have been performed on Wikipedia Infobox Dataset of biographies and results are presented for both single and multi-label models. These models indicate an effective alternate deep learning technique based methods to extract structured data from raw text.</p
Assessment of dopaminergic neuron degeneration in a C. elegans model of Parkinson's disease
No full textTransgenic Caenorhabditis elegans that expresses the full-length wild-type human α-synuclein in dopaminergic neurons provides a well-established Parkinson's disease (PD) nematode model. Here, we present a detailed protocol to monitor and dissect the molecular underpinnings of age-associated neurodegeneration using this PD nematode model. This protocol includes preparation of nematode growth media and bacterial food sources, as well as procedures for nematode growth, synchronization, and treatment. We then describe procedures to assess dopaminergic neuronal death in vivo using fluorescence imaging. For complete details on the use and execution of this protocol, please refer to SenGupta et al. (2021). © 2022 The Author(s
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