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Astrophysics of Galaxies

Hierarchical Formation in Young Solar Clusters

3D Morphology of Open Clusters in the Solar Neighborhood with Gaia EDR3 II: Hierarchical Star Formation Revealed by Spatial and Kinematic Substructures

We identify members of 65 open clusters in the solar neighborhood using the machine-learning algorithm stargo based on the data of the open cluster survey (edr3).

We classify the substructures outside the tidal radius into four categories : filamentary(f1) and fractal (f2) for clusters myr, and halo (h) and tidal-tail (t) for clusters myr.

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Astrophysics of Galaxies

Hierarchical structures in the Vela OB2 complex and the cluster pair Collinder 135 and UBC 7 with Gaia EDR3

Disruption of Hierarchical Clustering in the Vela OB2 Complex and the Cluster Pair Collinder 135 and UBC7 with Gaia EDR3: Evidence of Supernova Quenching

Hierarchical structures in the complex and the cluster pair are identified with the neural network machine learning algorithm stargo.

Five second-level substructures are disentangled in the complex, which are referred to as huluwa 1 (gamma velorum), huluwa 2, huluwa 3, huluwa 4 and huluwa 5. the 3d morphology of huluwa 1-5 resembles a shell-like structure, right along the rim of the vela iras shell.

Data Structures and Algorithms

Replace or Retrieve Keywords In Documents at Scale

In this paper we introduce, the FlashText algorithm for replacing keywords or finding keywords in a given text. FlashText can search or replace keywords in one pass over a document. The time complexit

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Machine Learning

Neural and Evolutionary Computing

Stats Machine Learning

Vulnerability Under Adversarial Machine Learning: Bias or Variance?

Prior studies have unveiled the vulnerability of the deep neural networks in the context of adversarial machine learning, leading to great recent attention into this area. One interesting question tha

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Liked byJuan Carlos Martin

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Machine Learning

Quantum Physics

Quantum Machine Learning

Quantum Machine Learning: Fad or Future?

This paper will look forth to test and verify the aspects in which quantum machine learning can help improve over classical machine learning approaches while also shedding light on the likely limitations that have prevented quantum approaches to become the mainstream.

To observe the efficacy of using quantum computing for certain machine learning tasks and explore the improved potential of convergence, errorreduction and robustness to noisy data, this paper will look forth to test and verify the aspects in which quantum machine learning can help improve overclassical machine learning approaches while also shedding light on the likely limitations that have prevented quantum approaches to become the mainstream.

Data Structures and Algorithms

Machine Learning

Stats Machine Learning

An $O(N)$ Sorting Algorithm: Machine Learning Sort

We propose an $O(N\cdot M)$ sorting algorithm by Machine Learning method, which shows a huge potential sorting big data. This sorting algorithm can be applied to parallel sorting and is suitable for G

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Machine Learning

Stats Machine Learning

Cyclic Boosting -- an explainable supervised machine learning algorithm

Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep learning a

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Machine Learning

Artificial Intelligence

On the Convergence of Tsetlin Machines for the AND and the OR Operators

The Tsetlin Machine (TM) is a novel machine-learning algorithm based on propositional logic, which has obtained state-of-the-art performance on several pattern recognition problems. In previous studie

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Machine Learning

Quantum Physics

Differentiable programming and gradient descent to find unitary matrices that can be used in the period finding algorithm

Machine Learning the period finding algorithm

We use differentiable programming and gradient descent to find unitarymatrices that can be used in the period finding algorithm to extract periodinformation from the state of a quantum computer post application of the oracle.

Our findings suggest that that this is not the only unitary matrix appropriatefor the period finding algorithm, there exist several unitary matrices that canaffect out the same transformation and they are significantly different from each other as well.neural networks can be applied to differentiate such unitary matrices from randomly generated ones indicating that these unitaries do have characteristic features that can otherwise be discerned easily.

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Machine Learning

Computers and Society

Artificial Intelligence

A Machine Learning Algorithm for Historical Trustworthiness Ratings

Measuring Trustworthiness or Automating Physiognomy? A Comment on Safra, Chevallier, Grèzes, and Baumard (2020)

Safra, chevallier, gr\`ezeses, and baumard (2020) studied the historical progression of interpersonal trust by training a machine learning(ml) algorithm to generate trustworthiness ratings of historical portraits based on facial features.

They reported that trustworthiness ratings of portraits dated between 1500--2000ce increased with time, claiming that thisevidenced a broader increase in interpersonal trust coinciding with several metrics of societal progress.

Machine Learning

Statistical Mechanics

Stats Machine Learning

Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate

Many modern machine learning models are trained to achieve zero or near-zero training error in order to obtain near-optimal (but non-zero) test error. This phenomenon of strong generalization performa

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Astrophysics of Galaxies

Solar and Stellar Astrophysics

Analyzing the 3D morphology and kinematics of open clusters located within 500 pc of the Sun

3D Morphology of Open Clusters in the Solar Neighborhood with Gaia EDR3: its Relation to Cluster Dynamics

We analyze the 3d morphology and kinematics of 13 open clusters (ocs) located within 500 pc of the sun, using the unsupervised machine learning method stargo and kinematic data from literature.

We determine the 3d morphology of the ocs in our sample and fit the spatial distribution of stars within the tidal radius in each cluster with an ellipsoid model.

Artificial Intelligence

Causal DAG extraction from a library of books or videos/movies

Determining a causal DAG (directed acyclic graph) for a problem under consideration, is a major roadblock when doing Judea Pearl's Causal Inference (CI) in Statistics. The same problem arises when doi

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Machine Learning

General Economics

The Impact of Payday Loans on the Real Estate Market

Payday loans -- blessing or growth suppressor? Machine Learning Analysis

This research paper draws a first-hand experience of obtaining the index for the concentration of real estate in an area of reference by virtue of payday loans in toronto, ontario in particular, which sets out an ideology to create, evaluate and demonstrate the scenario through research analysis.

The purpose of this indexing via payday loans is the basic-debt: income ratio which states that when the income of the personbound to pay the interest of payday loans increases, his debt goes downmarginally which hence infers that the person invests in fixed assets like realestate which hikes up its growth.

Machine Learning

Information Theory

Artificial Intelligence

Stats Machine Learning

An Information-Theoretic Perspective on Overfitting and Underfitting

We present an information-theoretic framework for understanding overfitting and underfitting in machine learning and prove the formal undecidability of determining whether an arbitrary classification

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Machine Learning

To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning

Federated learning (FL) facilitates collaboration between a group of clients who seek to train a common machine learning model without directly sharing their local data. Although there is an abundance

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Machine Learning

Artificial Intelligence

Stats Machine Learning

Two-stage Algorithm for Fairness-aware Machine Learning

Algorithmic decision making process now affects many aspects of our lives. Standard tools for machine learning, such as classification and regression, are subject to the bias in data, and thus direct

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Machine Learning

Signal Processing

To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?

Machine learning has become successful in solving wireless interference management problems. Different kinds of deep neural networks (DNNs) have been trained to accomplish key tasks such as power cont

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Stats Machine Learning

A Nonasymptotic Debiased Machine Learning Theorem

A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees

We provide a nonasymptotic debiased machine learning theorem that encompasses any global or local functional of any machine learning algorithm that satisfies a few simple, interpretable conditions.

The conditions reveal a general double robustness property for ill posed inverse problems.

Quantum Physics

Communication-efficient Quantum Algorithm for Distributed Machine Learning

The growing demands of remote detection and increasing amount of training data make distributed machine learning under communication constraints a critical issue. This work provides a communication-ef

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