Tootfinder

Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports
Xinyu Zhao, Hao Yan, Yongming Liu
https://arxiv.org/abs/2403.17914

Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports
A large volume of accident reports is recorded in the aviation domain, which greatly values improving aviation safety. To better use those reports, we need to understand the most important events or impact factors according to the accident reports. However, the increasing number of accident reports requires large efforts from domain experts to label those reports. In order to make the labeling process more efficient, many researchers have started developing algorithms to identify the underlying…

Conformal Shield: A Novel Adversarial Attack Detection Framework for Automatic Modulation Classification
Tailai Wen, Da Ke, Xiang Wang, Zhitao Huang
https://arxiv.org/abs/2402.17450

Conformal Shield: A Novel Adversarial Attack Detection Framework for Automatic Modulation Classification
Deep learning algorithms have become an essential component in the field of cognitive radio, especially playing a pivotal role in automatic modulation classification. However, Deep learning also present risks and vulnerabilities. Despite their outstanding classification performance, they exhibit fragility when confronted with meticulously crafted adversarial examples, posing potential risks to the reliability of modulation recognition results. Addressing this issue, this letter pioneers the dev…

Computer classification of linear codes based on lattice point enumeration and integer linear programming
Sascha Kurz
https://arxiv.org/abs/2403.17509 http…

Computer classification of linear codes based on lattice point enumeration and integer linear programming
Linear codes play a central role in coding theory and have applications in several branches of mathematics. For error correction purposes the minimum Hamming distance should be as large as possible. Linear codes related to applications in Galois Geometry often require a certain divisibility of the occurring weights. In this paper we present an algorithmic framework for the classification of linear codes over finite fields with restricted sets of weights. The underlying algorithms are based on l…

Evaluating Classification Algorithms: Exoplanet Detection using Kepler Time Series Data
Fatemeh Fazel Hesar, Bernard Foing
https://arxiv.org/abs/2402.15874

Evaluating Classification Algorithms: Exoplanet Detection using Kepler Time Series Data
This study presents a comprehensive evaluation of various classification algorithms used for the detection of exoplanets using labeled time series data from the Kepler mission. The study investigates the performance of six commonly employed algorithms, namely Random Forest, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Naive Bayes, and Decision Tree. The evaluation process involves analyzing a dataset that consists of time series measurements of star brightness, accompanied …

This https://arxiv.org/abs/2308.11138 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

NLP-based detection of systematic anomalies among the narratives of consumer complaints
We develop an NLP-based procedure for detecting systematic nonmeritorious consumer complaints, simply called systematic anomalies, among complaint narratives. While classification algorithms are used to detect pronounced anomalies, in the case of smaller and frequent systematic anomalies, the algorithms may falter due to a variety of reasons, including technical ones as well as natural limitations of human analysts. Therefore, as the next step after classification, we convert the complaint narr…

Robust Containment Queries over Collections of Rational Parametric Curves via Generalized Winding Numbers
Jacob Spainhour, David Gunderman, Kenneth Weiss
https://arxiv.org/abs/2403.17371

Robust Containment Queries over Collections of Rational Parametric Curves via Generalized Winding Numbers
Point containment queries for regions bound by watertight geometric surfaces, i.e. closed and without self-intersections, can be evaluated straightforwardly with a number of well-studied algorithms. However, when such assumptions on domain geometry are not met, these methods are theoretically unfounded at best and practically unusable at worst. More robust classification schemes utilize generalized winding numbers, a mathematical construction that is indifferent to imperfections in the often hu…

J-PLUS: Bayesian object classification with a strum of BANNJOS
A. del Pino, C. L\'opez-Sanjuan, A. Hern\'an-Caballero, H. Dom\'inguez-S\'anchez, R. von Marttens, J. A. Fern\'andez-Ontiveros, P. R. T. Coelho, A. Lumbreras-Calle, J. Vega-Ferrero, F. Jimenez-Esteban, P. Cruz, V. Marra, M. Quartin, C. A. Galarza, R. E. Angulo, A. J. Cenarro, D. Crist\'obal-Hornillos, R. A. Dupke, A. Ederoclite, C. Hern\'andez-Monteagudo, A. Mar\'in-Franch, M. Moles, L. Sodr\…

J-PLUS: Bayesian object classification with a strum of BANNJOS
With its 12 optical filters, the Javalambre-Photometric Local Universe Survey (J-PLUS) provides an unprecedented multicolor view of the local Universe. The third data release (DR3) covers 3,192 deg$^2$ and contains 47.4 million objects. However, the classification algorithms currently implemented in its pipeline are deterministic and based solely on the sources morphology. Our goal is classify the sources identified in the J-PLUS DR3 images into stars, quasi-stellar objects (QSOs), and galaxies…

This https://arxiv.org/abs/2403.12452 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

On the Salient Limitations of `On the Salient Limitations of the Methods of Assembly Theory and their Classification of Molecular Biosignatures'
Assembly Theory (AT) is a theory that explains how to determine if a complex object is the product of evolution. Here we explain why attempts to compare AT to compression algorithms, ref 1, does not help identify if the object is the product of selection or not. Specifically, we show why aims to perform benchmark comparisons of different compression schemes to compare the performance of Molecular Assembly Indices against standard compression schemes in determining living vs. non-living samples …

This https://arxiv.org/abs/2308.04956 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Improved cryo-EM Pose Estimation and 3D Classification through Latent-Space Disentanglement
Due to the extremely low signal-to-noise ratio (SNR) and unknown poses (projection angles and image shifts) in cryo-electron microscopy (cryo-EM) experiments, reconstructing 3D volumes from 2D images is very challenging. In addition to these challenges, heterogeneous cryo-EM reconstruction requires conformational classification. In popular cryo-EM reconstruction algorithms, poses and conformation classification labels must be predicted for every input cryo-EM image, which can be computationally…

On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition
Igor Sokolov
https://arxiv.org/abs/2403.16230

On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition
Atomic force microscopy (AFM or SPM) imaging is one of the best matches with machine learning (ML) analysis among microscopy techniques. The digital format of AFM images allows for direct utilization in ML algorithms without the need for additional processing. Additionally, AFM enables the simultaneous imaging of distributions of over a dozen different physicochemical properties of sample surfaces, a process known as multidimensional imaging. While this wealth of information can be challenging …

Routing Algorithms
Ujjwal Sinha, Vikas Kumar, Shubham Kumar Singh
https://arxiv.org/abs/2403.11228 https://arxiv.org/pdf/2403.11228…

Routing Algorithms
Routing algorithms play a crucial role in the efficient transmission of data within computer networks by determining the optimal paths for packet forwarding. This paper presents a comprehensive exploration of routing algorithms, focusing on their fundamental principles, classification, challenges, recent advancements, and practical applications. Beginning with an overview of the significance of routing in modern communication networks, the paper delves into the historical evolution of routing a…

Multi-Level Explanations for Generative Language Models
Lucas Monteiro Paes, Dennis Wei, Hyo Jin Do, Hendrik Strobelt, Ronny Luss, Amit Dhurandhar, Manish Nagireddy, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Werner Geyer, Soumya Ghosh
https://arxiv.org/abs/2403.14459

Multi-Level Explanations for Generative Language Models
Perturbation-based explanation methods such as LIME and SHAP are commonly applied to text classification. This work focuses on their extension to generative language models. To address the challenges of text as output and long text inputs, we propose a general framework called MExGen that can be instantiated with different attribution algorithms. To handle text output, we introduce the notion of scalarizers for mapping text to real numbers and investigate multiple possibilities. To handle long …

Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification
Alberto Carlevaro, Teodoro Alamo Cantarero, Fabrizio Dabbene, Maurizio Mongelli
https://arxiv.org/abs/2403.10368

Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification
Conformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an algorithm is good enough to be used in practice. To define a reliable learning framework for classification from the very beginning of its design, the concept of scalable classifier was introduced to generalize the concept of classical classifier by linking it to statistical order theory and probabilistic learning theory. In this paper, we analyze…

This https://arxiv.org/abs/2308.04956 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Improved cryo-EM Pose Estimation and 3D Classification through Latent-Space Disentanglement
Due to the extremely low signal-to-noise ratio (SNR) and unknown poses (projection angles and image shifts) in cryo-electron microscopy (cryo-EM) experiments, reconstructing 3D volumes from 2D images is very challenging. In addition to these challenges, heterogeneous cryo-EM reconstruction requires conformational classification. In popular cryo-EM reconstruction algorithms, poses and conformation classification labels must be predicted for every input cryo-EM image, which can be computationally…

This https://arxiv.org/abs/2010.02848 has been replaced.
link: https://scholar.google.com/scholar?q=a

Unified Robust Estimation
Robust estimation is primarily concerned with providing reliable parameter estimates in the presence of outliers. Numerous robust loss functions have been proposed in regression and classification, along with various computing algorithms. In modern penalised generalised linear models (GLM), however, there is limited research on robust estimation that can provide weights to determine the outlier status of the observations. This article proposes a unified framework based on a large family of loss…

This https://arxiv.org/abs/2309.07136 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Masked Transformer for Electrocardiogram Classification
Electrocardiogram (ECG) is one of the most important diagnostic tools in clinical applications. With the advent of advanced algorithms, various deep learning models have been adopted for ECG tasks. However, the potential of Transformers for ECG data is not yet realized, despite their widespread success in computer vision and natural language processing. In this work, we present a useful masked Transformer method for ECG classification referred to as MTECG, which expands the application of maske…

This https://arxiv.org/abs/2403.19867 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Finding Decision Tree Splits in Streaming and Massively Parallel Models
In this work, we provide data stream algorithms that compute optimal splits in decision tree learning. In particular, given a data stream of observations $x_i$ and their labels $y_i$, the goal is to find the optimal split point $j$ that divides the data into two sets such that the mean squared error (for regression) or misclassification rate (for classification) is minimized. We provide various fast streaming algorithms that use sublinear space and a small number of passes for these problems. T…

This https://arxiv.org/abs/2305.09744 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors
The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by conventional classification algorithms. Machine learning (ML) techniques give their best in these types of classification problems. In this paper, we evaluate their performance against conventional (deterministic) algorithms. We demonstrate that both Convolutional Neural Networks (CNN) and Transformer-Encoder methods o…

Routing Algorithms
Ujjwal Sinha, Vikas Kumar, Shubham Kumar Singh
https://arxiv.org/abs/2403.11228 https://arxiv.org/pdf/2403.11228…

Routing Algorithms
Routing algorithms play a crucial role in the efficient transmission of data within computer networks by determining the optimal paths for packet forwarding. This paper presents a comprehensive exploration of routing algorithms, focusing on their fundamental principles, classification, challenges, recent advancements, and practical applications. Beginning with an overview of the significance of routing in modern communication networks, the paper delves into the historical evolution of routing a…

Indicators for characterising online hate speech and its automatic detection
Erica Forzinetti, Marco L. Della Vedova, Stefano Pasta, Milena Santerini
https://arxiv.org/abs/2402.08462

Indicators for characterising online hate speech and its automatic detection
We examined four case studies in the context of hate speech on Twitter in Italian from 2019 to 2020, aiming at comparing the classification of the 3,600 tweets made by expert pedagogists with the automatic classification made by machine learning algorithms. Pedagogists used a novel classification scheme based on seven indicators that characterize hate. These indicators are: the content is public, it affects a target group, it contains hate speech in explicit verbal form, it will not redeem, it …

An Adversarial Approach to Evaluating the Robustness of Event Identification Models
Obai Bahwal, Oliver Kosut, Lalitha Sankar
https://arxiv.org/abs/2402.12338

An Adversarial Approach to Evaluating the Robustness of Event Identification Models
Intelligent machine learning approaches are finding active use for event detection and identification that allow real-time situational awareness. Yet, such machine learning algorithms have been shown to be susceptible to adversarial attacks on the incoming telemetry data. This paper considers a physics-based modal decomposition method to extract features for event classification and focuses on interpretable classifiers including logistic regression and gradient boosting to distinguish two types…

A New Random Forest Ensemble of Intuitionistic Fuzzy Decision Trees
Yingtao Ren, Xiaomin Zhu, Kaiyuan Bai, Runtong Zhang
https://arxiv.org/abs/2403.07363 h…

A New Random Forest Ensemble of Intuitionistic Fuzzy Decision Trees
Classification is essential to the applications in the field of data mining, artificial intelligence, and fault detection. There exists a strong need in developing accurate, suitable, and efficient classification methods and algorithms with broad applicability. Random forest is a general algorithm that is often used for classification under complex conditions. Although it has been widely adopted, its combination with diverse fuzzy theory is still worth exploring. In this paper, we propose the i…

This https://arxiv.org/abs/2309.07136 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Masked Transformer for Electrocardiogram Classification
Electrocardiogram (ECG) is one of the most important diagnostic tools in clinical applications. With the advent of advanced algorithms, various deep learning models have been adopted for ECG tasks. However, the potential of Transformers for ECG data is not yet realized, despite their widespread success in computer vision and natural language processing. In this work, we present a useful masked Transformer method for ECG classification referred to as MTECG, which expands the application of maske…

Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics
Heitor P. Casagrande, Bo Xing, William J. Munro, Chu Guo, Dario Poletti
https://arxiv.org/abs/2404.11768

Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics
Algorithms developed to solve many-body quantum problems, like tensor networks, can turn into powerful quantum-inspired tools to tackle problems in the classical domain. In this work, we focus on matrix product operators, a prominent numerical technique to study many-body quantum systems, especially in one dimension. It has been previously shown that such a tool can be used for classification, learning of deterministic sequence-to-sequence processes and of generic quantum processes. We further …

A classification of well-behaved graph clustering schemes
Vilhelm Agdur
https://arxiv.org/abs/2404.03332 https://arxiv.org/pdf/2404.0…

A classification of well-behaved graph clustering schemes
Community detection in graphs is a problem that is likely to be relevant whenever network data appears, and consequently the problem has received much attention with many different methods and algorithms applied. However, many of these methods are hard to study theoretically, and they optimise for somewhat different goals. A general and rigorous account of the problem and possible methods remains elusive. We study the class of all clustering methods that are monotone under addition of vertice…

A New Random Forest Ensemble of Intuitionistic Fuzzy Decision Trees
Yingtao Ren, Xiaomin Zhu, Kaiyuan Bai, Runtong Zhang
https://arxiv.org/abs/2403.07363 h…

A New Random Forest Ensemble of Intuitionistic Fuzzy Decision Trees
Classification is essential to the applications in the field of data mining, artificial intelligence, and fault detection. There exists a strong need in developing accurate, suitable, and efficient classification methods and algorithms with broad applicability. Random forest is a general algorithm that is often used for classification under complex conditions. Although it has been widely adopted, its combination with diverse fuzzy theory is still worth exploring. In this paper, we propose the i…

On the Salient Limitations of `On the Salient Limitations of the Methods of Assembly Theory and their Classification of Molecular Biosignatures'
Leroy Cronin
https://arxiv.org/abs/2403.12452

On the Salient Limitations of `On the Salient Limitations of the Methods of Assembly Theory and their Classification of Molecular Biosignatures'
Assembly Theory (AT) is a theory that explains how to determine if a complex object is the product of evolution. Here we explain why attempts to compare AT to compression algorithms, ref 1, does not help identify if the object is the product of selection or not. Specifically, we show why aims to perform benchmark comparisons of different compression schemes to compare the performance of Molecular Assembly Indices against standard compression schemes in determining living vs. non-living samples …

Tightening big Ms in integer programming formulations for support vector machines with ramp loss
Marta Baldomero-Naranjo, Luisa I. Mart\'inez-Merino, Antonio M. Rodr\'iguez-Ch\'ia
https://arxiv.org/abs/2403.07736

Tightening big Ms in integer programming formulations for support vector machines with ramp loss
This paper considers various models of support vector machines with ramp loss, these being an efficient and robust tool in supervised classification for the detection of outliers. The exact solution approaches for the resulting optimization problem are of high demand for large datasets. Hence, the goal of this paper is to develop algorithms that provide efficient methodologies to exactly solve these optimization problems. These approaches are based on three strategies for obtaining tightened va…

Indicators for characterising online hate speech and its automatic detection
Erica Forzinetti, Marco L. Della Vedova, Stefano Pasta, Milena Santerini
https://arxiv.org/abs/2402.08462

Indicators for characterising online hate speech and its automatic detection
We examined four case studies in the context of hate speech on Twitter in Italian from 2019 to 2020, aiming at comparing the classification of the 3,600 tweets made by expert pedagogists with the automatic classification made by machine learning algorithms. Pedagogists used a novel classification scheme based on seven indicators that characterize hate. These indicators are: the content is public, it affects a target group, it contains hate speech in explicit verbal form, it will not redeem, it …

Comparison of Deep Learning Techniques on Human Activity Recognition using Ankle Inertial Signals
Farhad Nazari, Darius Nahavandi, Navid Mohajer, Abbas Khosravi
https://arxiv.org/abs/2403.04387

Comparison of Deep Learning Techniques on Human Activity Recognition using Ankle Inertial Signals
Human Activity Recognition (HAR) is one of the fundamental building blocks of human assistive devices like orthoses and exoskeletons. There are different approaches to HAR depending on the application. Numerous studies have been focused on improving them by optimising input data or classification algorithms. However, most of these studies have been focused on applications like security and monitoring, smart devices, the internet of things, etc. On the other hand, HAR can help adjust and control…

Noise misleads rotation invariant algorithms on sparse targets
Manfred K. WarmuthGoogle Inc, Wojciech Kot{\l}owskiInstitute of Computing Science, Poznan University of Technology, Poznan, Poland, Matt JonesUniversity of Colorado Boulder, Colorado, USA, Ehsan AmidGoogle Inc
https://arxiv.org/abs/2403.02697

Noise misleads rotation invariant algorithms on sparse targets
It is well known that the class of rotation invariant algorithms are suboptimal even for learning sparse linear problems when the number of examples is below the "dimension" of the problem. This class includes any gradient descent trained neural net with a fully-connected input layer (initialized with a rotationally symmetric distribution). The simplest sparse problem is learning a single feature out of $d$ features. In that case the classification error or regression loss grows with $1-k/n$ wh…

This https://arxiv.org/abs/2401.11712 has been replaced.
link: https://scholar.google.com/scholar?q=a

A First Step Towards Runtime Analysis of Evolutionary Neural Architecture Search
Evolutionary neural architecture search (ENAS) employs evolutionary algorithms to find high-performing neural architectures automatically, and has achieved great success. However, compared to the empirical success, its rigorous theoretical analysis has yet to be touched. This work goes preliminary steps toward the mathematical runtime analysis of ENAS. In particular, we define a binary classification problem $\textsc{UNIFORM}$, and formulate an explicit fitness function to represent the relatio…

This https://arxiv.org/abs/2401.02897 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCG_…

Robust Bichromatic Classification using Two Lines
Given two sets $\mathit{R}$ and $\mathit{B}$ of at most $\mathit{n}$ points in the plane, we present efficient algorithms to find a two-line linear classifier that best separates the "red" points in $\mathit{R}$ from the "blue" points in $B$ and is robust to outliers. More precisely, we find a region $\mathit{W}_\mathit{B}$ bounded by two lines, so either a halfplane, strip, wedge, or double wedge, containing (most of) the blue points $\mathit{B}$, and few red points. Our running times vary bet…

Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics
Heitor P. Casagrande, Bo Xing, William J. Munro, Chu Guo, Dario Poletti
https://arxiv.org/abs/2404.11768

Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics
Algorithms developed to solve many-body quantum problems, like tensor networks, can turn into powerful quantum-inspired tools to tackle problems in the classical domain. In this work, we focus on matrix product operators, a prominent numerical technique to study many-body quantum systems, especially in one dimension. It has been previously shown that such a tool can be used for classification, learning of deterministic sequence-to-sequence processes and of generic quantum processes. We further …

Rahmani Sort: A Novel Variant of Insertion Sort Algorithm with O(nlogn) Complexity
Mohammad Khalid Imam Rahmani
https://arxiv.org/abs/2402.19107 https://…

Rahmani Sort: A Novel Variant of Insertion Sort Algorithm with O(nlogn) Complexity
Various decision support systems are available that implement Data Mining and Data Warehousing techniques for diving into the sea of data for getting useful patterns of knowledge (pearls). Classification, regression, clustering, and many other algorithms are used to enhance the precision and accuracy of the decision process. So, there is scope for increasing the response time of the decision process, especially in mission-critical operations. If data are ordered with suitable and efficient sort…

Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge
Jiancheng Yang, Rui Shi, Liang Jin, Xiaoyang Huang, Kaiming Kuang, Donglai Wei, Shixuan Gu, Jianying Liu, Pengfei Liu, Zhizhong Chai, Yongjie Xiao, Hao Chen, Liming Xu, Bang Du, Xiangyi Yan, Hao Tang, Adam Alessio, Gregory Holste, Jiapeng Zhang, Xiaoming Wang, Jianye He, Lixuan Che, Hanspeter Pfister, Ming Li, Bingbing Ni

Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge
Rib fractures are a common and potentially severe injury that can be challenging and labor-intensive to detect in CT scans. While there have been efforts to address this field, the lack of large-scale annotated datasets and evaluation benchmarks has hindered the development and validation of deep learning algorithms. To address this issue, the RibFrac Challenge was introduced, providing a benchmark dataset of over 5,000 rib fractures from 660 CT scans, with voxel-level instance mask annotations…

Noise misleads rotation invariant algorithms on sparse targets
Manfred K. WarmuthGoogle Inc, Wojciech Kot{\l}owskiInstitute of Computing Science, Poznan University of Technology, Poznan, Poland, Matt JonesUniversity of Colorado Boulder, Colorado, USA, Ehsan AmidGoogle Inc
https://arxiv.org/abs/2403.02697

Noise misleads rotation invariant algorithms on sparse targets
It is well known that the class of rotation invariant algorithms are suboptimal even for learning sparse linear problems when the number of examples is below the "dimension" of the problem. This class includes any gradient descent trained neural net with a fully-connected input layer (initialized with a rotationally symmetric distribution). The simplest sparse problem is learning a single feature out of $d$ features. In that case the classification error or regression loss grows with $1-k/n$ wh…

Tightening big Ms in integer programming formulations for support vector machines with ramp loss
Marta Baldomero-Naranjo, Luisa I. Mart\'inez-Merino, Antonio M. Rodr\'iguez-Ch\'ia
https://arxiv.org/abs/2403.07736

Tightening big Ms in integer programming formulations for support vector machines with ramp loss
This paper considers various models of support vector machines with ramp loss, these being an efficient and robust tool in supervised classification for the detection of outliers. The exact solution approaches for the resulting optimization problem are of high demand for large datasets. Hence, the goal of this paper is to develop algorithms that provide efficient methodologies to exactly solve these optimization problems. These approaches are based on three strategies for obtaining tightened va…

The impact of void-finding algorithms on galaxy classification
Fatima Zaidouni, Dahlia Veyrat, Kelly A. Douglass, Segev BenZvi
https://arxiv.org/abs/2403.20008

The impact of void-finding algorithms on galaxy classification
We explore how the definition of a void influences the conclusions drawn about the impact of the void environment on galactic properties using two void-finding algorithms in the Void Analysis Software Toolkit: V2, a Python implementation of ZOBOV, and VoidFinder, an algorithm which grows and merges spherical void regions. Using the Sloan Digital Sky Survey Data Release 7, we find that galaxies found in VoidFinder voids tend to be bluer, fainter, and have higher (specific) star formation rates t…

RADIA -- Radio Advertisement Detection with Intelligent Analytics
Jorge \'Alvarez, Juan Carlos Armenteros, Camilo Torr\'on, Miguel Ortega-Mart\'in, Alfonso Ardoiz, \'Oscar Garc\'ia, Ignacio Arranz, \'I\~nigo Galdeano, Ignacio Garrido, Adri\'an Alonso, Fernando Bay\'on, Oleg Vorontsov
https://arxiv…

RADIA -- Radio Advertisement Detection with Intelligent Analytics
Radio advertising remains an integral part of modern marketing strategies, with its appeal and potential for targeted reach undeniably effective. However, the dynamic nature of radio airtime and the rising trend of multiple radio spots necessitates an efficient system for monitoring advertisement broadcasts. This study investigates a novel automated radio advertisement detection technique incorporating advanced speech recognition and text classification algorithms. RadIA's approach surpasses tr…

Finding Decision Tree Splits in Streaming and Massively Parallel Models
Huy Pham, Hoang Ta, Hoa T. Vu
https://arxiv.org/abs/2403.19867 https://

Finding Decision Tree Splits in Streaming and Massively Parallel Models
In this work, we provide data stream algorithms that compute optimal splits in decision tree learning. In particular, given a data stream of observations $x_i$ and their labels $y_i$, the goal is to find the optimal split point $j$ that divides the data into two sets such that the mean squared error (for regression) or misclassification rate (for classification) is minimized. We provide various fast streaming algorithms that use sublinear space and a small number of passes for these problems. T…

This https://arxiv.org/abs/2401.14287 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Modelling Micro-Doppler Signature of Drone Propellers in Distributed ISAC
Integrated Sensing and Communication (ISAC) comprises detection and analysis of non-cooperative targets by exploiting the resources of the mobile radio system. In this context, micro-Doppler is of great importance for target classification, in order to distinguish objects with local movements. For developing algorithms for target classification, it is necessary to have a large amount of target signatures. Aiming to generate these data, this paper proposes a mathematical model for the micro-Dopp…