805 research outputs found
Extreme self-organization in networks constructed from gene expression data
We study networks constructed from gene expression data obtained from many
types of cancers. The networks are constructed by connecting vertices that
belong to each others' list of K-nearest-neighbors, with K being an a priori
selected non-negative integer. We introduce an order parameter for
characterizing the homogeneity of the networks. On minimizing the order
parameter with respect to K, degree distribution of the networks shows
power-law behavior in the tails with an exponent of unity. Analysis of the
eigenvalue spectrum of the networks confirms the presence of the power-law and
small-world behavior. We discuss the significance of these findings in the
context of evolutionary biological processes.Comment: 4 pages including 3 eps figures, revtex. Revisions as in published
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Assessment of Poisson's Ratio for Hydroxy-terminated Polybutadine-based Solid Rocket Propellants
Poisson's ratio of hydroxy-terminated polybutadine (HTPB)-based composite propellant is estimated from uni-axial tensile testing. Double dumbbell specimens as per ASTM D638 type IV standard were used and Poisson's ratio at break, obtained by change in volume of specimen, was calculated as approximately 0.25. It was also observed that Poisson's ratio is different along different lateral directions of the propellant specimen. Poisson's ratios in two orthogonal directions perpendicular to longitudinal axis were calculated as 0.17 and 0.30. As ASTM specimen has rectangular cross-section of approximate size 6 mm x 4 mm, the directional behaviour of Poisson's ratio may be attributed to initial dimensions. Prismatic propellant specimen with square cross-section and of 115 mm x 6 mm x 6 mm dimension do not show any variation wrt Young's modulus,tensile strength, and percentage elongation as compared to ASTM specimen. Directional behaviour of Poisson's ratio with almost similar numerical value was again observed, thus ruling out dependence of this behaviour on different initial dimensions of propellant cross-section. Further, Poisson's ratio varies linearly with strain even in linear portion of stress-strain curve in uni-axial tensile testing. The rate of reduction of Poisson's ratio with increase in strain is slower in linear region and it accelerates after dewetting due to formation of vacuoles. Variation of Poisson's ratio with strain has two different slopes in linear (slope = 0.3165) and nonlinear regions (slope = 0.61364). Numerical value of slope for variation of Poisson's ratio with strain almost doubles after dewetting. It must be noted that no change in volume does not necessarily indicate constant Poisson's ratioequal to 0.5. Composite propellants behave as compressible material in most of the regions and near-failure region or at higher strains; Poisson's ratio is not anywhere near to 0.5, instead it is near 0.25.Defence Science Journal, 2010, 60(5), pp.497-501, DOI:http://dx.doi.org/10.14429/dsj.60.57
Physics-Informed Polynomial Chaos Expansions
Surrogate modeling of costly mathematical models representing physical
systems is challenging since it is typically not possible to create a large
experimental design. Thus, it is beneficial to constrain the approximation to
adhere to the known physics of the model. This paper presents a novel
methodology for the construction of physics-informed polynomial chaos
expansions (PCE) that combines the conventional experimental design with
additional constraints from the physics of the model. Physical constraints
investigated in this paper are represented by a set of differential equations
and specified boundary conditions. A computationally efficient means for
construction of physically constrained PCE is proposed and compared to standard
sparse PCE. It is shown that the proposed algorithms lead to superior accuracy
of the approximation and does not add significant computational burden.
Although the main purpose of the proposed method lies in combining data and
physical constraints, we show that physically constrained PCEs can be
constructed from differential equations and boundary conditions alone without
requiring evaluations of the original model. We further show that the
constrained PCEs can be easily applied for uncertainty quantification through
analytical post-processing of a reduced PCE filtering out the influence of all
deterministic space-time variables. Several deterministic examples of
increasing complexity are provided and the proposed method is applied for
uncertainty quantification
Efficiency of Indian Commodity Market: A Survey of Brokers’ Perception
The present study documents the finding of a survey of brokers’ perception pertaining to the recently introduced commodity derivatives market in India. The survey results show the brokers’ assessment about trading/marketing activities and their perception of the benefits and concerns about commodity derivatives. It also throw some light on the perception of brokers about the efficiency of Indian commodity derivatives in performing the functions of price discovery, hedging effectiveness and volatility dynamics. The survey results show that high net worth individual are contributing significantly in the trade volume of commodity derivatives. Interestingly, retail investors are also emerged as the significant contributor in total turnover of brokers. Survey results exhibit that price discovery and hedging effectiveness functions are well performed by all the commodity futures except the energy commodities futures. Energy commodities, being the most volatile commodities, are perceived as having less hedging effectiveness as compared to others. Brokers are assenting on the high to moderate impact of open interest, volume and time to maturity on the volatility of the commodity futures derivatives
Cross-lingual Editing in Multilingual Language Models
The training of large language models (LLMs) necessitates substantial data
and computational resources, and updating outdated LLMs entails significant
efforts and resources. While numerous model editing techniques (METs) have
emerged to efficiently update model outputs without retraining, their
effectiveness in multilingual LLMs, where knowledge is stored in diverse
languages, remains an underexplored research area. This research paper
introduces the cross-lingual model editing (\textbf{XME}) paradigm, wherein a
fact is edited in one language, and the subsequent update propagation is
observed across other languages. To investigate the XME paradigm, we conducted
experiments using BLOOM, mBERT, and XLM-RoBERTa using the two writing scripts:
\textit{Latin} (English, French, and Spanish) and \textit{Indic} (Hindi,
Gujarati, and Bengali). The results reveal notable performance limitations of
state-of-the-art METs under the XME setting, mainly when the languages involved
belong to two distinct script families. These findings highlight the need for
further research and development of XME techniques to address these challenges.
For more comprehensive information, the dataset used in this research and the
associated code are publicly available at the following
URL\url{https://github.com/lingo-iitgn/XME}.Comment: Accepted at EACL 202
Learning thermodynamically constrained equations of state with uncertainty
Numerical simulations of high energy-density experiments require equation of
state (EOS) models that relate a material's thermodynamic state variables --
specifically pressure, volume/density, energy, and temperature. EOS models are
typically constructed using a semi-empirical parametric methodology, which
assumes a physics-informed functional form with many tunable parameters
calibrated using experimental/simulation data. Since there are inherent
uncertainties in the calibration data (parametric uncertainty) and the assumed
functional EOS form (model uncertainty), it is essential to perform uncertainty
quantification (UQ) to improve confidence in the EOS predictions. Model
uncertainty is challenging for UQ studies since it requires exploring the space
of all possible physically consistent functional forms. Thus, it is often
neglected in favor of parametric uncertainty, which is easier to quantify
without violating thermodynamic laws. This work presents a data-driven machine
learning approach to constructing EOS models that naturally captures model
uncertainty while satisfying the necessary thermodynamic consistency and
stability constraints. We propose a novel framework based on physics-informed
Gaussian process regression (GPR) that automatically captures total uncertainty
in the EOS and can be jointly trained on both simulation and experimental data
sources. A GPR model for the shock Hugoniot is derived and its uncertainties
are quantified using the proposed framework. We apply the proposed model to
learn the EOS for the diamond solid state of carbon, using both density
functional theory data and experimental shock Hugoniot data to train the model
and show that the prediction uncertainty reduces by considering the
thermodynamic constraints.Comment: 26 pages, 7 figure
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