1,777 research outputs found
Data-driven discovery of dimensionless numbers and scaling laws from experimental measurements
Dimensionless numbers and scaling laws provide elegant insights into the
characteristic properties of physical systems. Classical dimensional analysis
and similitude theory fail to identify a set of unique dimensionless numbers
for a highly-multivariable system with incomplete governing equations. In this
study, we embed the principle of dimensional invariance into a two-level
machine learning scheme to automatically discover dominant and unique
dimensionless numbers and scaling laws from data. The proposed methodology,
called dimensionless learning, can reduce high-dimensional parametric spaces
into descriptions involving just a few physically-interpretable dimensionless
parameters, which significantly simplifies the process design and optimization
of the system. We demonstrate the algorithm by solving several challenging
engineering problems with noisy experimental measurements (not synthetic data)
collected from the literature. The examples include turbulent Rayleigh-Benard
convection, vapor depression dynamics in laser melting of metals, and porosity
formation in 3D printing. We also show that the proposed approach can identify
dimensionally-homogeneous differential equations with minimal parameters by
leveraging sparsity-promoting techniques
Could mergers become more sustainable? A study of the stock exchange mergers of NASDAQ and OMX
This study investigates whether the merger of NASDAQ and OMX could reduce the portfolio diversification possibilities for stock market investors and whether it is necessary to implement national policies and international treaties for the sustainable development of financial markets. Our study is very important because some players in the stock markets have not yet realized that stock exchanges, during the last decades, have moved from government-owned or mutually-owned organizations to private companies, and, with several mergers having occurred, the market is tending gradually to behave like a monopoly. From our analysis, we conclude that increased volatility and reduced diversification opportunities are the results of an increase in the long-run comovement between each pair of indices in Nordic and Baltic stock markets (Denmark, Sweden, Finland, Estonia, Latvia, and Lithuania) and NASDAQ after the merger. We also find that the merger tends to improve the error-correction mechanism for NASDAQ so that it Granger-causes OMX, but OMX loses predictive power on NASDAQ after the merger. We conclude that the merger of NASDAQ and OMX reduces the diversification possibilities for stock market investors and our findings provide evidence to support the argument that it is important to implement national policies and international treaties for the sustainable development of financial markets
Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission
Multi-node communication, which refers to the interaction among multiple
devices, has attracted lots of attention in many Internet-of-Things (IoT)
scenarios. However, its huge amounts of data flows and inflexibility for task
extension have triggered the urgent requirement of communication-efficient
distributed data transmission frameworks. In this paper, inspired by the great
superiorities on bandwidth reduction and task adaptation of semantic
communications, we propose a federated learning-based semantic communication
(FLSC) framework for multi-task distributed image transmission with IoT
devices. Federated learning enables the design of independent semantic
communication link of each user while further improves the semantic extraction
and task performance through global aggregation. Each link in FLSC is composed
of a hierarchical vision transformer (HVT)-based extractor and a task-adaptive
translator for coarse-to-fine semantic extraction and meaning translation
according to specific tasks. In order to extend the FLSC into more realistic
conditions, we design a channel state information-based multiple-input
multiple-output transmission module to combat channel fading and noise.
Simulation results show that the coarse semantic information can deal with a
range of image-level tasks. Moreover, especially in low signal-to-noise ratio
and channel bandwidth ratio regimes, FLSC evidently outperforms the traditional
scheme, e.g. about 10 peak signal-to-noise ratio gain in the 3 dB channel
condition.Comment: This paper has been accepted by IEEE Internet of Things Journa
Parameterized Unit Testing in the Open Source Wild
With recent advances in test generation research, powerful test generation tools are now at the fingertips of developers in software industry. For example, Microsoft Research Pex, a state-of-the-art tool based on dynamic symbolic execution, has been shipped as IntelliTest in Visual Studio 2015. For test inputs automatically generated by such tool, to supply test oracles (beyond just uncaught runtime exceptions or crashes), developers can write formal specifications such as code contracts in the form of preconditions, postconditions, and class invariants. However, just like writing other types of formal specifications, writing code contracts, especially postconditions, is challenging. In the past decade, parameterized unit testing has emerged as a promising alternative to specify program behaviors under test in the form of unit tests. Developers can write parameterized unit tests (PUTs), unit-test methods with parameters, in contrast to conventional unit tests, without parameters. PUTs have been popularly supported by various unit testing frameworks for .NET along with the recent JUnit framework. However, there exists no study to offer insights on how PUTs are written by developers in either proprietary or open source development practices, posing barriers for various stakeholders to bring PUTs to widely
adopted practices in software industry. To fill this gap, in this paper, we present the first empirical study of parameterized unit testing conducted on open source projects. We study hundreds of parameterized unit tests that open source developers wrote for these open source projects. Our study findings provide valuable insights for various stakeholders such as current or prospective PUT writers (e.g., developers), PUT framework designers, test-generation tool vendors, testing researchers, and testing educators.Ope
JAX-FEM: A differentiable GPU-accelerated 3D finite element solver for automatic inverse design and mechanistic data science
This paper introduces JAX-FEM, an open-source differentiable finite element
method (FEM) library. Constructed on top of Google JAX, a rising machine
learning library focusing on high-performance numerical computing, JAX-FEM is
implemented with pure Python while scalable to efficiently solve problems with
moderate to large sizes. For example, in a 3D tensile loading problem with 7.7
million degrees of freedom, JAX-FEM with GPU achieves around 10
acceleration compared to a commercial FEM code depending on platform. Beyond
efficiently solving forward problems, JAX-FEM employs the automatic
differentiation technique so that inverse problems are solved in a fully
automatic manner without the need to manually derive sensitivities. Examples of
3D topology optimization of nonlinear materials are shown to achieve optimal
compliance. Finally, JAX-FEM is an integrated platform for machine
learning-aided computational mechanics. We show an example of data-driven
multi-scale computations of a composite material where JAX-FEM provides an
all-in-one solution from microscopic data generation and model training to
macroscopic FE computations. The source code of the library and these examples
are shared with the community to facilitate computational mechanics research
A Characteristic Study of Parameterized Unit Tests in .NET Open Source Projects
In the past decade, parameterized unit testing has emerged as a promising method to specify program behaviors under test in the form of unit tests. Developers can write parameterized unit tests (PUTs), unit-test methods with parameters, in contrast to conventional unit tests, without parameters. The use of PUTs can enable powerful test generation tools such as Pex to have strong test oracles to check against, beyond just uncaught runtime exceptions. In addition, PUTs have been popularly supported by various unit testing frameworks for .NET and the JUnit framework for Java. However, there exists no study to offer insights on how PUTs are written by developers in either proprietary or open source development practices, posing barriers for various stakeholders to bring PUTs to widely adopted practices in software industry. To fill this gap, we first present categorization results of the Microsoft MSDN Pex Forum posts (contributed primarily by industrial practitioners) related to PUTs. We then use the categorization results to guide the design of the first characteristic study of PUTs in .NET open source projects. We study hundreds of PUTs that open source developers wrote for these open source projects. Our study findings provide valuable insights for various stakeholders such as current or prospective PUT writers (e.g., developers), PUT framework designers, test-generation tool vendors, testing researchers, and testing educators
DiffComplete: Diffusion-based Generative 3D Shape Completion
We introduce a new diffusion-based approach for shape completion on 3D range
scans. Compared with prior deterministic and probabilistic methods, we strike a
balance between realism, multi-modality, and high fidelity. We propose
DiffComplete by casting shape completion as a generative task conditioned on
the incomplete shape. Our key designs are two-fold. First, we devise a
hierarchical feature aggregation mechanism to inject conditional features in a
spatially-consistent manner. So, we can capture both local details and broader
contexts of the conditional inputs to control the shape completion. Second, we
propose an occupancy-aware fusion strategy in our model to enable the
completion of multiple partial shapes and introduce higher flexibility on the
input conditions. DiffComplete sets a new SOTA performance (e.g., 40% decrease
on l_1 error) on two large-scale 3D shape completion benchmarks. Our completed
shapes not only have a realistic outlook compared with the deterministic
methods but also exhibit high similarity to the ground truths compared with the
probabilistic alternatives. Further, DiffComplete has strong generalizability
on objects of entirely unseen classes for both synthetic and real data,
eliminating the need for model re-training in various applications.Comment: Project Page: https://ruihangchu.com/diffcomplete.htm
Statistical Parameterized Physics-Based Machine Learning Digital Twin Models for Laser Powder Bed Fusion Process
A digital twin (DT) is a virtual representation of physical process, products
and/or systems that requires a high-fidelity computational model for continuous
update through the integration of sensor data and user input. In the context of
laser powder bed fusion (LPBF) additive manufacturing, a digital twin of the
manufacturing process can offer predictions for the produced parts, diagnostics
for manufacturing defects, as well as control capabilities. This paper
introduces a parameterized physics-based digital twin (PPB-DT) for the
statistical predictions of LPBF metal additive manufacturing process. We
accomplish this by creating a high-fidelity computational model that accurately
represents the melt pool phenomena and subsequently calibrating and validating
it through controlled experiments. In PPB-DT, a mechanistic reduced-order
method-driven stochastic calibration process is introduced, which enables the
statistical predictions of the melt pool geometries and the identification of
defects such as lack-of-fusion porosity and surface roughness, specifically for
diagnostic applications. Leveraging data derived from this physics-based model
and experiments, we have trained a machine learning-based digital twin
(PPB-ML-DT) model for predicting, monitoring, and controlling melt pool
geometries. These proposed digital twin models can be employed for predictions,
control, optimization, and quality assurance within the LPBF process,
ultimately expediting product development and certification in LPBF-based metal
additive manufacturing.Comment: arXiv admin note: text overlap with arXiv:2208.0290
Mathematical modelling of genipin-bovine serum albumin interaction using fluorescence intensity measurements
The interaction between genipin and a model protein bovine serum albumin (BSA), with and without the addition of acetic acid, has been studied experimentally and by modelling. The number of amino groups available to react was determined to be 5.6 % of the total number of amino acid building blocks on BSA. Fluorescence intensity was used to record the progress of the reaction over the 24 h, while the modelling study focused on capturing the kinetic profiles of the reaction. The experiments revealed a slow start to the BSA and genipin interaction, that subsequently accelerated in an S-shaped curve which the modelling study linked with the existence of the feedback cycle for both reactive amino groups and genipin. At BSA concentrations ≥30 mg/mL the reaction was accelerated in the presence of acid, while below 30 mg/mL the acidified conditions delayed the onset of the reaction. Contrary to the reaction mechanisms previously proposed, a degree of breakdown of the fluorescent links in the products formed was denoted both experimentally and in a modelling study. This indicated the reversibility of the processes forming fluorescent product/s and suggested feasibility of the successful release of the protein following prospective encapsulation within the genipin-crosslinked hydrogel structure.</p
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