Tootfinder

{dtrack} makes documentation of data wrangling part of the analysis and creates pretty flow charts: #rstats

in Sachen #OpenAccess-#Verlagsverträge/-#Rahmenverträge:
"Are Transformative Agreements Worth It? An Analysis of Open Access Publication Data at the Universi…

Data analysis from the hydroacoustic stations of the Comprehensive Nuclear-Test-Ban Treaty Organization
has unveiled distinctive pressure signals linked to aircraft crashes of varying sizes in the ocean.
Notably, these signals were detected at distances ranging from two to five thousand kilometres,
highlighting the efficacy of underwater acoustic technology in event identification and classification in marine environments.
In this study, we investigate the plausibil…

Underwater acoustic analysis reveals unique pressure signals associated with aircraft crashes in the sea: revisiting MH370 - Scientific Reports
Data analysis from the hydroacoustic stations of the Comprehensive Nuclear-Test-Ban Treaty Organization has unveiled distinctive pressure signals linked to aircraft crashes of varying sizes in the ocean. Notably, these signals were detected at distances ranging from two to five thousand kilometres, highlighting the efficacy of underwater acoustic technology in event identification and classification in marine environments. In this study, we investigate the plausibility of an aircraft, such as M…

This https://arxiv.org/abs/2401.05507 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks
In this paper, we introduce InfiAgent-DABench, the first benchmark specifically designed to evaluate LLM-based agents on data analysis tasks. These tasks require agents to end-to-end solving complex tasks by interacting with an execution environment. This benchmark contains DAEval, a dataset consisting of 257 data analysis questions derived from 52 CSV files, and an agent framework which incorporates LLMs to serve as data analysis agents for both serving and evaluation. Since data analysis ques…

A Methodology for Questionnaire Analysis: Insights through Cluster Analysis of an Investor Competition Data
Carlos Henrique Q. Forster, Paulo Andr\'e Lima de Castro, Andrei Ramalho
https://arxiv.org/abs/2402.06759

A Methodology for Questionnaire Analysis: Insights through Cluster Analysis of an Investor Competition Data
In this paper, we propose a methodology for the analysis of questionnaire data along with its application on discovering insights from investor data motivated by a day trading competition. The questionnaire includes categorical questions, which are reduced to binary questions, 'yes' or 'no'. The methodology reduces dimensionality by grouping questions and participants with similar responses using clustering analysis. Rule discovery was performed by using a conversion rate metric. Innovative vis…

Distributed Record Linkage in Healthcare Data with Apache Spark
Mohammad Heydari, Reza Sarshar, Mohammad Ali Soltanshahi
https://arxiv.org/abs/2404.07939 h…

Distributed Record Linkage in Healthcare Data with Apache Spark
Healthcare data is a valuable resource for research, analysis, and decision-making in the medical field. However, healthcare data is often fragmented and distributed across various sources, making it challenging to combine and analyze effectively. Record linkage, also known as data matching, is a crucial step in integrating and cleaning healthcare data to ensure data quality and accuracy. Apache Spark, a powerful open-source distributed big data processing framework, provides a robust platform …

Toward an Android Static Analysis Approach for Data Protection
Mugdha Khedkar, Eric Bodden
https://arxiv.org/abs/2402.07889 https://a…

Toward an Android Static Analysis Approach for Data Protection
Android applications collecting data from users must protect it according to the current legal frameworks. Such data protection has become even more important since the European Union rolled out the General Data Protection Regulation (GDPR). Since app developers are not legal experts, they find it difficult to write privacy-aware source code. Moreover, they have limited tool support to reason about data protection throughout their app development process. This paper motivates the need for a s…

My evening's entertainment: re-doing an analysis I did over 35 years ago, but with more recent data (and taking an hour, not a week, using #Stata, not #fortran ): birth rates in Ireland by parity and year of age, 2008-2021, using the most recent data from

Novel definition and quantitative analysis of branch structure with topological data analysis
Haruhisa Oda, Mayuko Kida, Yoichi Nakata, Hiroki Kurihara
https://arxiv.org/abs/2402.07436

Novel definition and quantitative analysis of branch structure with topological data analysis
While branching network structures abound in nature, their objective analysis is more difficult than expected because existing quantitative methods often rely on the subjective judgment of branch structures. This problem is particularly pronounced when dealing with images comprising discrete particles. Here we propose an objective framework for quantitative analysis of branching networks by introducing the mathematical definitions for internal and external structures based on topological data a…

in Sachen #OpenAccess-#Verlagsverträge/-#Rahmenverträge:
"Are Transformative Agreements Worth It? An Analysis of Open Access Publication Data at the Universi…

Pulling back symmetric Riemannian geometry for data analysis
Willem Diepeveen
https://arxiv.org/abs/2403.06612 https://arxiv.org/pdf/…

Pulling back symmetric Riemannian geometry for data analysis
Data sets tend to live in low-dimensional non-linear subspaces. Ideal data analysis tools for such data sets should therefore account for such non-linear geometry. The symmetric Riemannian geometry setting can be suitable for a variety of reasons. First, it comes with a rich mathematical structure to account for a wide range of non-linear geometries that has been shown to be able to capture the data geometry through empirical evidence from classical non-linear embedding. Second, many standard d…

This https://arxiv.org/abs/2403.04403 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPL_…

Conjugate operators for transparent, explorable research outputs
Charts, figures, and text derived from data play an important role in decision making, from data-driven policy development to day-to-day choices informed by online articles. Making sense of, or fact-checking, outputs means understanding how they relate to the underlying data. Even for domain experts with access to the source code and data sets, this poses a significant challenge. In this paper we introduce a new program analysis framework which supports interactive exploration of fine-grained I…

Semantic Data for Humanities and Social Sciences (SDHSS): an Ecosystem of CIDOC CRM Extensions for Research Data Production and Reuse
Francesco BerettaLARHRA, LARHRA PHN
https://arxiv.org/abs/2402.07531

Semantic Data for Humanities and Social Sciences (SDHSS): an Ecosystem of CIDOC CRM Extensions for Research Data Production and Reuse
Given the challenge of giant knowledge graphs created by major eco-nomic actors, which could virtually replace research in the Humani-ties and Social Sciences (HSS) in responding to public concerns, thequestion arises of how to increase the value of research data throughtheir publication and networking, applying the FAIR principles. Bothan epistemological and a semantic analysis show that the most rel-evant part of research data is factual information, understood as arepresentation of the objec…

This https://arxiv.org/abs/2404.05696 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

BOLD v4: A Centralized Bioinformatics Platform for DNA-based Biodiversity Data
BOLD, the Barcode of Life Data System, supports the acquisition, storage, validation, analysis, and publication of DNA barcodes, activities requiring the integration of molecular, morphological, and distributional data. Its pivotal role in curating the reference library of DNA barcodes, coupled with its data management and analysis capabilities, make it a central resource for biodiversity science. It enables rapid, accurate identification of specimens and also reveals patterns of genetic divers…

Logistic Multidimensional Data Analysis for Ordinal Response Variables using a Cumulative Link function
Mark de Rooij, Ligaya Breemer, Dion Woestenburg, Frank Busing
https://arxiv.org/abs/2402.07629

Logistic Multidimensional Data Analysis for Ordinal Response Variables using a Cumulative Link function
We present a multidimensional data analysis framework for the analysis of ordinal response variables. Underlying the ordinal variables, we assume a continuous latent variable, leading to cumulative logit models. The framework includes unsupervised methods, when no predictor variables are available, and supervised methods, when predictor variables are available. We distinguish between dominance variables and proximity variables, where dominance variables are analyzed using inner product models, …

This https://arxiv.org/abs/2402.01276 has been replaced.
link: https://scholar.google.com/scholar?q=a

Federated Unlearning: a Perspective of Stability and Fairness
This paper explores the multifaceted consequences of federated unlearning (FU) with data heterogeneity. We introduce key metrics for FU assessment, concentrating on verification, global stability, and local fairness, and investigate the inherent trade-offs. Furthermore, we formulate the unlearning process with data heterogeneity through an optimization framework. Our key contribution lies in a comprehensive theoretical analysis of the trade-offs in FU and provides insights into data heterogenei…

This https://arxiv.org/abs/2402.19306 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepe…

New Pathways in Neutrino Physics via Quantum-Encoded Data Analysis
Ever-increasing amount of data is produced by particle detectors in their quest to unveil the laws of Nature. The large data rate requires the use of specialized triggers that promptly reduce the data rate to a manageable level; however, in doing so, unexpected new phenomena may escape detection. Additionally, the large data rate is increasingly difficult to analyze effectively, which has led to a recent revolution on machine learning techniques. Here, we present a methodology based on recent q…

Grr I let my data analysis run for 2h, just for it to blow up at the *last* crucial saving step with a stupid ImportError because I forgot to 'import pickle' 🤦 I should run #mypy more often... 😅
#Python #dataAnalysis

Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification
Yuning Huang, Jingchen Zou, Lanxi Meng, Xin Yue, Qing Zhao, Jianqiang Li, Changwei Song, Gabriel Jimenez, Shaowu Li, Guanghui Fu
https://arxiv.org/abs/2402.07595

Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification
Medical image analysis frequently encounters data scarcity challenges. Transfer learning has been effective in addressing this issue while conserving computational resources. The recent advent of foundational models like the DINOv2, which uses the vision transformer architecture, has opened new opportunities in the field and gathered significant interest. However, DINOv2's performance on clinical data still needs to be verified. In this paper, we performed a glioma grading task using three clin…

Using Mathlink Cubes to Introduce Data Wrangling with Examples in R
Lucy D'Agostino McGowan
https://arxiv.org/abs/2402.07029 https://

Using Mathlink Cubes to Introduce Data Wrangling with Examples in R
This paper explores an innovative approach to teaching data wrangling skills to students through hands-on activities before transitioning to coding. Data wrangling, a critical aspect of data analysis, involves cleaning, transforming, and restructuring data. We introduce the use of a physical tool, mathlink cubes, to facilitate a tangible understanding of data sets. This approach helps students grasp the concepts of data wrangling before implementing them in coding languages such as R. We detail…

Signed graphs in data sciences via communicability geometry
Fernando Diaz-Diaz, Ernesto Estrada
https://arxiv.org/abs/2403.07493 https://

Signed graphs in data sciences via communicability geometry
Signed graphs are an emergent way of representing data in a variety of contexts were conflicting interactions exist. These include data from biological, ecological, and social systems. Here we propose the concept of communicability geometry for signed graphs, proving that metrics in this space, such as the communicability distance and angles, are Euclidean and spherical. We then apply these metrics to solve several problems in data analysis of signed graphs in a unified way. They include the pa…

An analysis of parameter compression and full-modeling techniques with Velocileptors for DESI 2024 and beyond
M. Maus, S. Chen, M. White, J. Aguilar, S. Ahlen, A. Aviles, S. Brieden, D. Brooks, T. Claybaugh, S. Cole, A. de la Macorra, Arjun Dey, P. Doel, S. Ferraro, N. Findlay, J. E. Forero-Romero, E. Gazta\~naga, H. Gil-Mar\'in, S. Gontcho A Gontcho, C. Hahn, K. Honscheid, C. Howlett, M. Ishak, S. Juneau, A. Kremin, Y. Lai, M. Landriau, M. E. Levi, M. Manera, R. Miquel, E. Mueller…

An analysis of parameter compression and full-modeling techniques with Velocileptors for DESI 2024 and beyond
In anticipation of forthcoming data releases of current and future spectroscopic surveys, we present the validation tests and analysis of systematic effects within \texttt{velocileptors} modeling pipeline when fitting mock data from the \texttt{AbacusSummit} N-body simulations. We compare the constraints obtained from parameter compression methods to the direct fitting (Full-Modeling) approaches of modeling the galaxy power spectra, and show that the ShapeFit extension to the traditional templa…

Time Series Analysis of Key Societal Events as Reflected in Complex Social Media Data Streams
Andy Skumanich, Han Kyul Kim
https://arxiv.org/abs/2403.07090

Time Series Analysis of Key Societal Events as Reflected in Complex Social Media Data Streams
Social media platforms hold valuable insights, yet extracting essential information can be challenging. Traditional top-down approaches often struggle to capture critical signals in rapidly changing events. As global events evolve swiftly, social media narratives, including instances of disinformation, become significant sources of insights. To address the need for an inductive strategy, we explore a niche social media platform GAB and an established messaging service Telegram, to develop metho…

This https://arxiv.org/abs/2304.13406 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Onset of a conceptual outline map to get a hold on the jungle of cluster analysis
The domain of cluster analysis is a meeting point for a very rich multidisciplinary encounter, with cluster-analytic methods being studied and developed in discrete mathematics, numerical analysis, statistics, data analysis, data science, and computer science (including machine learning, data mining, and knowledge discovery), to name but a few. The other side of the coin, however, is that the domain suffers from a major accessibility problem as well as from the fact that it is rife with divisio…

No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks
Gang Hu, Ke Qin, Chenhan Yuan, Min Peng, Alejandro Lopez-Lira, Benyou Wang, Sophia Ananiadou, Wanlong Yu, Jimin Huang, Qianqian Xie
https://arxiv.org/abs/2403.06249…

No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks
While the progression of Large Language Models (LLMs) has notably propelled financial analysis, their application has largely been confined to singular language realms, leaving untapped the potential of bilingual Chinese-English capacity. To bridge this chasm, we introduce ICE-PIXIU, seamlessly amalgamating the ICE-INTENT model and ICE-FLARE benchmark for bilingual financial analysis. ICE-PIXIU uniquely integrates a spectrum of Chinese tasks, alongside translated and original English datasets, …

This https://arxiv.org/abs/2111.00187 has been replaced.
link: https://scholar.google.com/scholar?q=a

Echopype: A Python library for interoperable and scalable processing of water column sonar data for biological information
High-frequency sonar systems deployed on a wide array of ocean observing platforms are creating a deluge of water column sonar data at an unprecedented speed from all corners of the ocean. Efficient and integrative analysis of these data, either across different sonar instruments or with other oceanographic datasets, holds the key to monitoring and understanding the response of marine ecosystems to the rapidly changing climate. In this paper we present Echopype, an open-source Python software l…

This https://arxiv.org/abs/2306.03889 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

The 95.4 GeV di-photon excess at ATLAS and CMS
The ATLAS collaboration has recently reported the results of a low-mass Higgs-boson search in the di-photon final state based on the full Run 2 data set. The results are based on an improved analysis w.r.t. the previous analysis, which included a part of the Run 2 data, with a substantially better sensitivity. The ``model-dependent'' search carried out by ATLAS shows an excess of events at a mass of about 95.4 GeV with a local significance of $1.7\,σ$. The results are compatible with a previou…

Virtual reassembling of 3D fragments for the data-driven analysis of fracture mechanisms in composite materials
Thomas WilhelmInstitute of Stochastics, Ulm University, 89069 Ulm, Germany, Trang Thu V\~oInstitute of Particle Technology and Mineral Processing, TU Bergakademie Freiberg, 09599 Freiberg, Germany, Orkun FuratInstitute of Stochastics, Ulm University, 89069 Ulm, Germany, Urs A. PeukerInstitute of Particle Technology and Mineral Processing, TU Bergakademie Freiberg, 09599 Freib…

Virtual reassembling of 3D fragments for the data-driven analysis of fracture mechanisms in composite materials
This paper introduces a novel method for characterizing fracture mechanisms in composite materials using 3D image data gained by computed tomography (CT) measurements. In mineral liberation, the understanding of these mechanisms is crucial, particularly whether fractures occur along the boundaries of mineral phases (intergranular fracture) and/or within mineral phases (transgranular fracture). Conventional techniques for analyzing fracture mechanisms are focused on globally comparing the surfac…

Data-driven sparse modeling of oscillations in plasma space propulsion
B. Bay\'on-Buj\'an, M. Merino
https://arxiv.org/abs/2403.06809 https://

Data-driven sparse modeling of oscillations in plasma space propulsion
An algorithm to obtain data-driven models of oscillatory phenomena in plasma space propulsion systems is presented, based on sparse regression (SINDy) and Pareto front analysis. The algorithm can incorporate physical constraints, use data bootstrapping for additional robustness, and fine-tuning to different metrics. Standard, weak and integral SINDy formulations are discussed and compared. The scheme is benchmarked in the case of breathing-mode oscillations in Hall effect thrusters, using PIC/f…

Data driven approach to study the transition from dispersive to dissipative systems through dimensionality reduction techniques
Mairembam Kelvin Singh, A. Surjalal Sharma, N. Nimai Singh, Moirangthem Shubhakanta Singh
https://arxiv.org/abs/2403.06987

Data driven approach to study the transition from dispersive to dissipative systems through dimensionality reduction techniques
Complexity is often exhibited in dynamical systems, where certain parameters evolve with time in a strange and chaotic nature. These systems lack predictability and are common in the physical world. Dissipative systems are one of such systems where the volume of the phase space contracts with time. On the other hand, we employ dimensionality reduction techniques to study complicated and complex data, which are tough to analyse. The Principal Component Analysis (PCA) is a dimensionality reductio…

This https://arxiv.org/abs/2301.06136 has been replaced.
link: https://scholar.google.com/scholar?q=a

Quantitative Verification with Neural Networks
We present a data-driven approach to the quantitative verification of probabilistic programs and stochastic dynamical models. Our approach leverages neural networks to compute tight and sound bounds for the probability that a stochastic process hits a target condition within finite time. This problem subsumes a variety of quantitative verification questions, from the reachability and safety analysis of discrete-time stochastic dynamical models, to the study of assertion-violation and terminatio…

A Statistical and Multiwavelength Photometric Analysis of a Young Embedded Open Star Cluster: IC 1590
A. H. Sheikh, Biman J. Medhi
https://arxiv.org/abs/2402.07750

A Statistical and Multiwavelength Photometric Analysis of a Young Embedded Open Star Cluster: IC 1590
We present a statistical and multiwavelength photometric studies of young open cluster IC 1590. We identified 91 cluster members using $Gaia$ DR3 astrometry data using ensemble-based unsupervised machine learning techniques. From $Gaia$ EDR3 data, we estimate the best-fitted parameters for IC 1590 using the Automated Stellar Cluster Analysis package (ASteCA) yielding the distance $d$ $\sim$ 2.87 $\pm$ 0.02 Kpc, age $\sim$ 3.54 $\pm$ 0.05 Myr, metallicity $z$ $\sim$ 0.0212 $\pm$ 0.003, binarity …

This https://arxiv.org/abs/2105.15106 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Survey of Knowledge Tracing: Models, Variants, and Applications
Modern online education has the capacity to provide intelligent educational services by automatically analyzing substantial amounts of student behavioral data. Knowledge Tracing (KT) is one of the fundamental tasks for student behavioral data analysis, aiming to monitor students' evolving knowledge state during their problem-solving process. In recent years, a substantial number of studies have concentrated on this rapidly growing field, significantly contributing to its advancements. In this s…

Used ChatGPT to get some nifty @obsidianmd dataview queries, its ideal for regex and formatting stuff, the data is all there, but I normally don’t have the patience to write and finesse the queries, since I don’t do data analysis often enough to remember all the details

This https://arxiv.org/abs/2404.03936 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Deep Learning for Satellite Image Time Series Analysis: A Review
Earth observation (EO) satellite missions have been providing detailed images about the state of the Earth and its land cover for over 50 years. Long term missions, such as NASA's Landsat, Terra, and Aqua satellites, and more recently, the ESA's Sentinel missions, record images of the entire world every few days. Although single images provide point-in-time data, repeated images of the same area, or satellite image time series (SITS) provide information about the changing state of vegetation an…

This https://arxiv.org/abs/2210.03859 has been replaced.
link: https://scholar.google.com/scholar?q=a

Spectrally-Corrected and Regularized Linear Discriminant Analysis for Spiked Covariance Model
This paper proposes an improved linear discriminant analysis called spectrally-corrected and regularized LDA (SRLDA). This method integrates the design ideas of the sample spectrally-corrected covariance matrix and the regularized discriminant analysis. With the support of a large-dimensional random matrix analysis framework, it is proved that SRLDA has a linear classification global optimal solution under the spiked model assumption. According to simulation data analysis, the SRLDA classifier …

Low Cost Carriers induce specific and identifiable delay propagation patterns: an analysis of the EU and US systems
Sofia Gil-Rodrigo, Massimiliano Zanin
https://arxiv.org/abs/2402.07656

Low Cost Carriers induce specific and identifiable delay propagation patterns: an analysis of the EU and US systems
The impact of air transport delays and their propagation has long been studied, mainly from environmental and mobility viewpoints, using a wide range of data analysis tools and simulations. Less attention has nevertheless been devoted to how delays create meso-scale structures around each airport. In this work we tackle this issue by reconstructing functional networks of delay propagation centred at each airport, and studying their identifiability (i.e. how unique they are) using Deep Learning …

This https://arxiv.org/abs/2306.12690 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Uniform error bound for PCA matrix denoising
Principal component analysis (PCA) is a simple and popular tool for processing high-dimensional data. We investigate its effectiveness for matrix denoising. We consider the clean data are generated from a low-dimensional subspace, but masked by independent high-dimensional sub-Gaussian noises with standard deviation $σ$. Under the low-rank assumption on the clean data with a mild spectral gap assumption, we prove that the distance between each pair of PCA-denoised data point and the clean da…

Study of the Impact of the Big Data Era on Accounting and Auditing
Yuxiang Sun, Jingyi Li, Mengdie Lu, Zongying Guo
https://arxiv.org/abs/2403.07180 https:…

Study of the Impact of the Big Data Era on Accounting and Auditing
Big data revolutionizes accounting and auditing, offering deep insights but also introducing challenges like data privacy and security. With data from IoT, social media, and transactions, traditional practices are evolving. Professionals must adapt to these changes, utilizing AI and machine learning for efficient data analysis and anomaly detection. Key to overcoming these challenges are enhanced analytics tools, continuous learning, and industry collaboration. By addressing these areas, the ac…

Accelerating Sparse Tensor Decomposition Using Adaptive Linearized Representation
Jan Laukemann, Ahmed E. Helal, S. Isaac Geronimo Anderson, Fabio Checconi, Yongseok Soh, Jesmin Jahan Tithi, Teresa Ranadive, Brian J Gravelle, Fabrizio Petrini, Jee Choi
https://arxiv.org/abs/2403.06348

Accelerating Sparse Tensor Decomposition Using Adaptive Linearized Representation
High-dimensional sparse data emerge in many critical application domains such as cybersecurity, healthcare, anomaly detection, and trend analysis. To quickly extract meaningful insights from massive volumes of these multi-dimensional data, scientists employ unsupervised analysis tools based on tensor decomposition (TD) methods. However, real-world sparse tensors exhibit highly irregular shapes, data distributions, and sparsity, which pose significant challenges for making efficient use of moder…

Visualization for physics analysis improvement and applications in BESIII
Zhi-Jun Li, Ming-Kuan Yuan, Yun-Xuan Song, Yan-Gu Li, Jing-Shu Li, Sheng-Sen Sun, Xiao-Long Wang, Zheng-Yun You, Ya-Jun Mao
https://arxiv.org/abs/2404.07951

Visualization for physics analysis improvement and applications in BESIII
Modern particle physics experiments usually rely on highly complex and large-scale spectrometer devices. In high energy physics experiments, visualization helps detector design, data quality monitoring, offline data processing, and has great potential for improving physics analysis. In addition to the traditional physics data analysis based on statistical methods, visualization provides unique intuitive advantages in searching for rare signal events and reducing background noises. By applying t…

Performance Analysis of Matrix Multiplication for Deep Learning on the Edge
Cristian Ram\'irez, Adri\'an Castell\'o, H\'ector Mart\'inez, Enrique S. Quintana-Ort\'i
https://arxiv.org/abs/2403.07731

Performance Analysis of Matrix Multiplication for Deep Learning on the Edge
The devices designed for the Internet-of-Things encompass a large variety of distinct processor architectures, forming a highly heterogeneous zoo. In order to tackle this, we employ a simulator to estimate the performance of the matrix-matrix multiplication (GEMM) kernel on processors designed to operate at the edge. Our simulator adheres to the modern implementations of GEMM, advocated by GotoBLAS2, BLIS, OpenBLAS, etc., to carefully account for the amount of data transfers across the memory h…

scRDiT: Generating single-cell RNA-seq data by diffusion transformers and accelerating sampling
Shengze Dong, Zhuorui Cui, Ding Liu, Jinzhi Lei
https://arxiv.org/abs/2404.06153

scRDiT: Generating single-cell RNA-seq data by diffusion transformers and accelerating sampling
Motivation: Single-cell RNA sequencing (scRNA-seq) is a groundbreaking technology extensively utilized in biological research, facilitating the examination of gene expression at the individual cell level within a given tissue sample. While numerous tools have been developed for scRNA-seq data analysis, the challenge persists in capturing the distinct features of such data and replicating virtual datasets that share analogous statistical properties. Results: Our study introduces a generative app…

Topological Data Analysis of Monopoles in $U(1)$ Lattice Gauge Theory
Xavier Crean, Jeffrey Giansiracusa, Biagio Lucini
https://arxiv.org/abs/2403.07739 ht…

Topological Data Analysis of Monopoles in $U(1)$ Lattice Gauge Theory
In $4$-dimensional pure compact $U(1)$ lattice gauge theory, we analyse topological aspects of the dynamics of monopoles across the deconfinement phase transition. We do this using tools from Topological Data Analysis (TDA). We demonstrate that observables constructed from the zeroth and first homology groups of monopole current networks may be used to quantitatively and robustly locate the critical inverse coupling $β_{c}$ through finite-size scaling. Our method provides a mathematically robu…

This https://arxiv.org/abs/2112.06432 has been replaced.
link: https://scholar.google.com/scholar?q=a

Error analysis for parabolic optimal control problems with measure data in a nonconvex polygonal domain
This paper considers the finite element approximation to parabolic optimal control problems with measure data in a nonconvex polygonal domain. Such problems usually possess low regularity in the state variable due to the presence of measure data and the nonconvex nature of the domain. The low regularity of the solution allows the finite element approximations to converge at lower orders. We prove the existence, uniqueness and regularity results for the solution to the control problem satisfying…

Augmenting Interpolation-Based Model Checking with Auxiliary Invariants (Extended Version)
Dirk Beyer, Po-Chun Chien, Nian-Ze Lee
https://arxiv.org/abs/2403.07821

Augmenting Interpolation-Based Model Checking with Auxiliary Invariants (Extended Version)
Software model checking is a challenging problem, and generating relevant invariants is a key factor in proving the safety properties of a program. Program invariants can be obtained by various approaches, including lightweight procedures based on data-flow analysis and intensive techniques using Craig interpolation. Although data-flow analysis runs efficiently, it often produces invariants that are too weak to prove the properties. By contrast, interpolation-based approaches build strong invar…

Implementation of implicit filter for spatial spectra extraction
Kacper Nowak, Sergey Danilov, Vasco M\"uller, Caili Liu
https://arxiv.org/abs/2404.07398

Implementation of implicit filter for spatial spectra extraction
Scale analysis based on coarse-graining has been proposed recently as an alternative to Fourier analysis. It is now broadly used to analyze energy spectra and energy transfers in eddy-resolving ocean simulations. However, for data from unstructured-mesh models it requires interpolation to a regular grid. We present a high-performance Python implementation of an alternative coarse-graining method which relies on implicit filters using discrete Laplacians. This method can work on arbitrary (struc…

Highly Accurate Disease Diagnosis and Highly Reproducible Biomarker Identification with PathFormer
Zehao Dong, Qihang Zhao, Philip R. O. Payne, Michael A Province, Carlos Cruchaga, Muhan Zhang, Tianyu Zhao, Yixin Chen, Fuhai Li
https://arxiv.org/abs/2402.07268

Highly Accurate Disease Diagnosis and Highly Reproducible Biomarker Identification with PathFormer
Biomarker identification is critical for precise disease diagnosis and understanding disease pathogenesis in omics data analysis, like using fold change and regression analysis. Graph neural networks (GNNs) have been the dominant deep learning model for analyzing graph-structured data. However, we found two major limitations of existing GNNs in omics data analysis, i.e., limited-prediction (diagnosis) accuracy and limited-reproducible biomarker identification capacity across multiple datasets. …

Analysis: Asia Pacific data center deals, which have totaled $840.47M in 2024, or 50% of the global total so far, are set to surpass 2023's $3.45B record high (Reuters)
https://www.reuters.com/markets/deals/ai-boom-set-fuel-…

De Casteljau's Algorithm in Geometric Data Analysis: Theory and Application
Martin Hanik, Esfandiar Nava-Yazdani, Christoph von Tycowicz
https://arxiv.org/abs/2402.07550

De Casteljau's Algorithm in Geometric Data Analysis: Theory and Application
For decades, de Casteljau's algorithm has been used as a fundamental building block in curve and surface design and has found a wide range of applications in fields such as scientific computing, and discrete geometry to name but a few. With increasing interest in nonlinear data science, its constructive approach has been shown to provide a principled way to generalize parametric smooth curves to manifolds. These curves have found remarkable new applications in the analysis of parameter-dependen…

This https://arxiv.org/abs/2310.19181 has been replaced.
link: https://scholar.google.com/scholar?q=a

From Chatbots to PhishBots? -- Preventing Phishing scams created using ChatGPT, Google Bard and Claude
The advanced capabilities of Large Language Models (LLMs) have made them invaluable across various applications, from conversational agents and content creation to data analysis, research, and innovation. However, their effectiveness and accessibility also render them susceptible to abuse for generating malicious content, including phishing attacks. This study explores the potential of using four popular commercially available LLMs, i.e., ChatGPT (GPT 3.5 Turbo), GPT 4, Claude, and Bard, to gen…

This is the best source of stock splits, delistings, and other corporate actions that I have ever seen: https://stockanalysis.com/actions/
Makes me wonder where do they get the data for it. I have tried perusing the web a bit and just can't find one conclusive answer.

Corporate Actions List - Recent and Historical - Stock Analysis
A comprehensive list of recent and historical corporate actions and stock changes for companies listed on the US stock market.

This https://arxiv.org/abs/2403.16110 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

ByteCard: Enhancing ByteDance's Data Warehouse with Learned Cardinality Estimation
Cardinality estimation is a critical component and a longstanding challenge in modern data warehouses. ByteHouse, ByteDance's cloud-native engine for extensive data analysis in exabyte-scale environments, serves numerous internal decision-making business scenarios. With the increasing demand for ByteHouse, cardinality estimation becomes the bottleneck for efficiently processing queries. Specifically, the existing query optimizer of ByteHouse uses the traditional Selinger-like cardinality estima…

Validating the Galaxy and Quasar Catalog-Level Blinding Scheme for the DESI 2024 analysis
U. Andrade, J. Mena-Fern\'andez, H. Awan, A. J. Ross, S. Brieden, J. Pan, A. de Mattia, J. Aguilar, S. Ahlen, O. Alves, D. Brooks, E. Buckley-Geer, E. Chaussidon, T. Claybaugh, S. Cole, A. de la Macorra, Arjun Dey, P. Doel, K. Fanning, J. E. Forero-Romero, E. Gazta\~naga, H. Gil-Mar\'in, S. Gontcho A Gontcho, J. Guy, C. Hahn, M. M. S Hanif, K. Honscheid, C. Howlett, D. Huterer, S. Juneau, …

Validating the Galaxy and Quasar Catalog-Level Blinding Scheme for the DESI 2024 analysis
In the era of precision cosmology, ensuring the integrity of data analysis through blinding techniques is paramount -- a challenge particularly relevant for the Dark Energy Spectroscopic Instrument (DESI). DESI represents a monumental effort to map the cosmic web, with the goal to measure the redshifts of tens of millions of galaxies and quasars. Given the data volume and the impact of the findings, the potential for confirmation bias poses a significant challenge. To address this, we implement…

Investigating the Soft X-ray Spectra of Solar Flare Onsets
Anant Telikicherla, Thomas N. Woods, Bennet D. Schwab
https://arxiv.org/abs/2403.05992 https://<…

Investigating the Soft X-ray Spectra of Solar Flare Onsets
In this study we present the analysis of six solar flare events that occurred in 2022, using new data from the third-generation Miniature X-Ray Solar Spectrometer (MinXSS), also known as the Dual-zone Aperture X-ray Solar Spectrometer (DAXSS). The primary focus of this study is on the flare's "onset phase", which is characterized by elevated soft X-ray emissions even before the flare's impulsive phase. We analyze the temporal evolution of plasma temperature, emission measure, and elemental abun…

Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning
Qiao Sun, Liujia Yang, Minghao Ma, Nanyang Ye, Qinying Gu
https://arxiv.org/abs/2403.07342

Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning
Aspect Sentiment Triplet Extraction (ASTE) is a burgeoning subtask of fine-grained sentiment analysis, aiming to extract structured sentiment triplets from unstructured textual data. Existing approaches to ASTE often complicate the task with additional structures or external data. In this research, we propose a novel tagging scheme and employ a contrastive learning approach to mitigate these challenges. The proposed approach demonstrates comparable or superior performance in comparison to state…

FNSPID: A Comprehensive Financial News Dataset in Time Series
Zihan Dong, Xinyu Fan, Zhiyuan Peng
https://arxiv.org/abs/2402.06698 https://

FNSPID: A Comprehensive Financial News Dataset in Time Series
Financial market predictions utilize historical data to anticipate future stock prices and market trends. Traditionally, these predictions have focused on the statistical analysis of quantitative factors, such as stock prices, trading volumes, inflation rates, and changes in industrial production. Recent advancements in large language models motivate the integrated financial analysis of both sentiment data, particularly market news, and numerical factors. Nonetheless, this methodology frequentl…

No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks
Gang Hu, Ke Qin, Chenhan Yuan, Min Peng, Alejandro Lopez-Lira, Benyou Wang, Sophia Ananiadou, Wanlong Yu, Jimin Huang, Qianqian Xie
https://arxiv.org/abs/2403.06249…

No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks
While the progression of Large Language Models (LLMs) has notably propelled financial analysis, their application has largely been confined to singular language realms, leaving untapped the potential of bilingual Chinese-English capacity. To bridge this chasm, we introduce ICE-PIXIU, seamlessly amalgamating the ICE-INTENT model and ICE-FLARE benchmark for bilingual financial analysis. ICE-PIXIU uniquely integrates a spectrum of Chinese tasks, alongside translated and original English datasets, …

This https://arxiv.org/abs/2404.06882 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

A Multi-wavelength Study on A Gamma-ray Bright AGN 1308+326 Using KVN at 22 and 43 GHz
In this paper, we conduct a multi-frequency analysis of the gamma-ray bright blazar 1308+326 from February 2013 to March 2020, using the Korean VLBI Network at 22 and 43 GHz and gamma-ray data from the Fermi Large Area Telescope (LAT). Our findings reveal spectral variations around the 2014 gamma-ray flare, aligning with the shock-in-jet model. A strong correlation is observed between gamma-ray and 43 GHz emissions, with a 27-day lag in the VLBI core light curve, indicating a 50-day delay from …

PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI
Peirong Liu, Oula Puonti, Annabel Sorby-Adams, William T. Kimberly, Juan E. Iglesias
https://arxiv.org/abs/2403.06227

PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI
Remarkable progress has been made by data-driven machine-learning methods in the analysis of MRI scans. However, most existing MRI analysis approaches are crafted for specific MR pulse sequences (MR contrasts) and usually require nearly isotropic acquisitions. This limits their applicability to diverse real-world clinical data, where scans commonly exhibit variations in appearances due to being obtained with varying sequence parameters, resolutions, and orientations -- especially in the presenc…

New slow-roll approximations for inflation in Einstein-Gauss-Bonnet gravity
E. O. Pozdeeva, M. A. Skugoreva, A. V. Toporensky, S. Yu. Vernov
https://arxiv.org/abs/2403.06147

New slow-roll approximations for inflation in Einstein-Gauss-Bonnet gravity
We propose new slow-roll approximations for inflationary models with the Gauss-Bonnet term. We find more accurate expressions of the standard slow-roll parameters as functions of the scalar field. To check the accuracy of approximations considered we construct inflationary models with quadratic and quartic monomial potentials and the Gauss-Bonnet term. Numerical analysis of these models indicates that the proposed inflationary scenarios do not contradict to the observation data. New slow-roll a…

Sensitivity analysis for publication bias in meta-analysis of sparse data based on exact likelihood
Taojun Hu, Yi Zhou, Sattoshi Hattori
https://arxiv.org/abs/2404.06837

Sensitivity analysis for publication bias in meta-analysis of sparse data based on exact likelihood
Meta-analysis is a powerful tool to synthesize findings from multiple studies. The normal-normal random-effects model is widely used accounting for between-study heterogeneity. However, meta-analysis of sparse data, which may arise when the event rate is low for binary or count outcomes, poses a challenge to the normal-normal random-effects model in the accuracy and stability in inference since the normal approximation in the within-study likelihood may not be good. To reduce bias arising from …

Correlation and Autocorrelation of Data on Complex Networks
Rudy Arthur
https://arxiv.org/abs/2405.05125 https://arxiv.org/pdf/2405.0…

Correlation and Autocorrelation of Data on Complex Networks
Networks where each node has one or more associated numerical values are common in applications. This work studies how summary statistics used for the analysis of spatial data can be applied to non-spatial networks for the purposes of exploratory data analysis. We focus primarily on Moran-type statistics and discuss measures of global autocorrelation, local autocorrelation and global correlation. We introduce null models based on fixing edges and permuting the data or fixing the data and permut…

This https://arxiv.org/abs/2307.11153 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Exploring the impact of high-precision top-quark pair production data on the structure of the proton at the LHC
The impact of recent LHC top-quark pair production single differential cross section measurements at 13 TeV collision energy on the structure of the proton is explored. In particular, the impact of these high-precision data on the gluon and other parton distribution functions (PDFs) of the proton at intermediate and large partonic momentum fraction $x$ is analyzed. This study extends the CT18 global analysis framework to include these new data. The interplay between top-quark pair and inclusive…

A template for data analysis projects structured as R packages (or not) #rstats #datascience

Study of the Impact of the Big Data Era on Accounting and Auditing
Yuxiang Sun, Jingyi Li, Mengdie Lu, Zongying Guo
https://arxiv.org/abs/2403.07180 https:…

Study of the Impact of the Big Data Era on Accounting and Auditing
Big data revolutionizes accounting and auditing, offering deep insights but also introducing challenges like data privacy and security. With data from IoT, social media, and transactions, traditional practices are evolving. Professionals must adapt to these changes, utilizing AI and machine learning for efficient data analysis and anomaly detection. Key to overcoming these challenges are enhanced analytics tools, continuous learning, and industry collaboration. By addressing these areas, the ac…

A Comparison of Different Representations of Ordinal Patterns and Their Usability in Data Analysis
Alexander Schnurr, Angelika Silbernagel
https://arxiv.org/abs/2402.07478

A Comparison of Different Representations of Ordinal Patterns and Their Usability in Data Analysis
We describe and analyze different approaches to represent ordinal patterns. All of these can be found in the literature. The most important representations (plus sub-classes) are compared in terms of their applicability from different angles. Namely we consider digital implementation, inverse patterns and ties between values. At the end we provide a guideline on which occasions which representation should be used.

IsoPredict: Dynamic Predictive Analysis for Detecting Unserializable Behaviors in Weakly Isolated Data Store Applications
Chujun Geng, Spyros Blanas, Michael D. Bond, Yang Wang
https://arxiv.org/abs/2404.04621

IsoPredict: Dynamic Predictive Analysis for Detecting Unserializable Behaviors in Weakly Isolated Data Store Applications
This paper presents the first dynamic predictive analysis for data store applications under weak isolation levels, called Isopredict. Given an observed serializable execution of a data store application, Isopredict generates and solves SMT constraints to find an unserializable execution that is a feasible execution of the application. Isopredict introduces novel techniques that handle divergent application behavior; solve mutually recursive sets of constraints; and balance coverage, precision, …

Performance Analysis of Matrix Multiplication for Deep Learning on the Edge
Cristian Ram\'irez, Adri\'an Castell\'o, H\'ector Mart\'inez, Enrique S. Quintana-Ort\'i
https://arxiv.org/abs/2403.07731

Performance Analysis of Matrix Multiplication for Deep Learning on the Edge
The devices designed for the Internet-of-Things encompass a large variety of distinct processor architectures, forming a highly heterogeneous zoo. In order to tackle this, we employ a simulator to estimate the performance of the matrix-matrix multiplication (GEMM) kernel on processors designed to operate at the edge. Our simulator adheres to the modern implementations of GEMM, advocated by GotoBLAS2, BLIS, OpenBLAS, etc., to carefully account for the amount of data transfers across the memory h…

Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning
Qiao Sun, Liujia Yang, Minghao Ma, Nanyang Ye, Qinying Gu
https://arxiv.org/abs/2403.07342

Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning
Aspect Sentiment Triplet Extraction (ASTE) is a burgeoning subtask of fine-grained sentiment analysis, aiming to extract structured sentiment triplets from unstructured textual data. Existing approaches to ASTE often complicate the task with additional structures or external data. In this research, we propose a novel tagging scheme and employ a contrastive learning approach to mitigate these challenges. The proposed approach demonstrates comparable or superior performance in comparison to state…

This https://arxiv.org/abs/2310.08560 has been replaced.
link: https://scholar.google.com/scholar?q=a

MemGPT: Towards LLMs as Operating Systems
Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversations and document analysis. To enable using context beyond limited context windows, we propose virtual context management, a technique drawing inspiration from hierarchical memory systems in traditional operating systems that provide the appearance of large memory resources through data movement between fast and slow memory. Using this tech…

PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI
Peirong Liu, Oula Puonti, Annabel Sorby-Adams, William T. Kimberly, Juan E. Iglesias
https://arxiv.org/abs/2403.06227

PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI
Remarkable progress has been made by data-driven machine-learning methods in the analysis of MRI scans. However, most existing MRI analysis approaches are crafted for specific MR pulse sequences (MR contrasts) and usually require nearly isotropic acquisitions. This limits their applicability to diverse real-world clinical data, where scans commonly exhibit variations in appearances due to being obtained with varying sequence parameters, resolutions, and orientations -- especially in the presenc…

Evaluation and thermodynamic optimization of phase diagram of lithium niobate tantalate solid solutions
Umar Bashir, Detlef Klimm, Michael Rusing, Matthias Bickermann, Steffen Ganschow
https://arxiv.org/abs/2403.07527

Evaluation and thermodynamic optimization of phase diagram of lithium niobate tantalate solid solutions
The phase diagram of the lithium niobate and lithium tantalate solid solutions was investigated using experimental data from differential thermal analysis (DTA) and crystal growth. We used XRF analysis to determine the elemental composition of crystals. Based on the Neumann-Kopp rule, essential data of end members lithium niobate (LN) and lithium tantalate (LT) was created. The heats of fusion of end members given by DTA measurements of LN (103 kJ/mol at 1531 K) and LT (289 kJ/mol at 1913 K) we…

Towards Data-center Level Carbon Modeling and Optimization for Deep Learning Inference
Shixin Ji, Zhuoping Yang, Xingzhen Chen, Jingtong Hu, Yiyu Shi, Alex K. Jones, Peipei Zhou
https://arxiv.org/abs/2403.04976

Towards Data-center Level Carbon Modeling and Optimization for Deep Learning Inference
Recently, the increasing need for computing resources has led to the prosperity of data centers, which poses challenges to the environmental impacts and calls for improvements in data center provisioning strategies. In this work, we show a comprehensive analysis based on profiling a variety of deep-learning inference applications on different generations of GPU servers. Our analysis reveals several critical factors which can largely affect the design space of provisioning strategies including t…

Investigating the Soft X-ray Spectra of Solar Flare Onsets
Anant Telikicherla, Thomas N. Woods, Bennet D. Schwab
https://arxiv.org/abs/2403.05992 https://<…

Investigating the Soft X-ray Spectra of Solar Flare Onsets
In this study we present the analysis of six solar flare events that occurred in 2022, using new data from the third-generation Miniature X-Ray Solar Spectrometer (MinXSS), also known as the Dual-zone Aperture X-ray Solar Spectrometer (DAXSS). The primary focus of this study is on the flare's "onset phase", which is characterized by elevated soft X-ray emissions even before the flare's impulsive phase. We analyze the temporal evolution of plasma temperature, emission measure, and elemental abun…

Integrating LSTM and BERT for Long-Sequence Data Analysis in Intelligent Tutoring Systems
Zhaoxing Li, Jujie Yang, Jindi Wang, Lei Shi, Sebastian Stein
https://arxiv.org/abs/2405.05136

Integrating LSTM and BERT for Long-Sequence Data Analysis in Intelligent Tutoring Systems
The field of Knowledge Tracing aims to understand how students learn and master knowledge over time by analyzing their historical behaviour data. To achieve this goal, many researchers have proposed Knowledge Tracing models that use data from Intelligent Tutoring Systems to predict students' subsequent actions. However, with the development of Intelligent Tutoring Systems, large-scale datasets containing long-sequence data began to emerge. Recent deep learning based Knowledge Tracing models fac…

This https://arxiv.org/abs/2305.19618 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Verifying the Smoothness of Graph Signals: A Graph Signal Processing Approach
Graph signal processing (GSP) deals with the representation, analysis, and processing of structured data, i.e. graph signals that are defined on the vertex set of a generic graph. A crucial prerequisite for applying various GSP and graph neural network (GNN) approaches is that the examined signals are smooth graph signals with respect to the underlying graph, or, equivalently, have low graph total variation (TV). In this paper, we develop GSP-based approaches to verify the validity of the smoot…

Evaluating Navigation and Comparison Performance of Computational Notebooks on Desktop and in Virtual Reality
Sungwon In, Erick Krokos, Kirsten Whitley, Chris North, Yalong Yang
https://arxiv.org/abs/2404.07161

Evaluating Navigation and Comparison Performance of Computational Notebooks on Desktop and in Virtual Reality
The computational notebook serves as a versatile tool for data analysis. However, its conventional user interface falls short of keeping pace with the ever-growing data-related tasks, signaling the need for novel approaches. With the rapid development of interaction techniques and computing environments, there is a growing interest in integrating emerging technologies in data-driven workflows. Virtual reality, in particular, has demonstrated its potential in interactive data visualizations. In …

Loglinear modeling with mixed numerical and categorical predictor variables through an Extended Stereotype Model
Mark de Rooij
https://arxiv.org/abs/2402.07634

Loglinear modeling with mixed numerical and categorical predictor variables through an Extended Stereotype Model
Loglinear analysis is most useful when we have two or more categorical response variables. Loglinear analysis, however, requires categorical predictor variables, such that the data can be represented in a contingency table. Researchers often have a mix of categorical and numerical predictors. We present a new statistical methodology for the analysis of multiple categorical response variables with a mix of numeric and categorical predictor variables. Therefore, the stereotype model, a reduced ra…

Hydrogen Column Density Variability in a Sample of Local Compton-Thin AGN II
A. Pizzetti, N. Torres-Alba, S. Marchesi, J. Buchner, I. Cox, X. Zhao, S. Neal, D. Sengupta, R. Silver, M. Ajello
https://arxiv.org/abs/2403.06919

Hydrogen Column Density Variability in a Sample of Local Compton-Thin AGN II
We present the multi-epoch analysis of 13 variable, nearby (z<0.1), Compton-thin (22

This https://arxiv.org/abs/2303.12018 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

The CMS di-photon excess at 95 GeV in view of the LHC Run 2 results
The CMS collaboration has recently reported the results of a low-mass Higgs-boson search in the di-photon final state based on the full Run 2 data set with refined analysis techniques. The new results show an excess of events at a mass of about 95 GeV with a local significance of $2.9\,σ$, confirming a previously reported excess at about the same mass and similar significance based on the first-year Run 2 plus Run 1 data. The observed excess is compatible with the limits obtained in the corres…

Evaluation and thermodynamic optimization of phase diagram of lithium niobate tantalate solid solutions
Umar Bashir, Detlef Klimm, Michael Rusing, Matthias Bickermann, Steffen Ganschow
https://arxiv.org/abs/2403.07527

Evaluation and thermodynamic optimization of phase diagram of lithium niobate tantalate solid solutions
The phase diagram of the lithium niobate and lithium tantalate solid solutions was investigated using experimental data from differential thermal analysis (DTA) and crystal growth. We used XRF analysis to determine the elemental composition of crystals. Based on the Neumann-Kopp rule, essential data of end members lithium niobate (LN) and lithium tantalate (LT) was created. The heats of fusion of end members given by DTA measurements of LN (103 kJ/mol at 1531 K) and LT (289 kJ/mol at 1913 K) we…

SIDE-real: Truncated marginal neural ratio estimation for Supernova Ia Dust Extinction with real data
Konstantin Karchev, Matthew Grayling, Benjamin M. Boyd, Roberto Trotta, Kaisey S. Mandel, Christoph Weniger
https://arxiv.org/abs/2403.07871

SIDE-real: Truncated marginal neural ratio estimation for Supernova Ia Dust Extinction with real data
We present the first fully simulation-based hierarchical analysis of the light curves of a population of low-redshift type Ia supernovae (SNae Ia). Our hardware-accelerated forward model, released in the Python package slicsim, includes stochastic variations of each SN's spectral flux distribution (based on the pre-trained BayeSN model), extinction from dust in the host and in the Milky Way, redshift, and realistic instrumental noise. By utilising truncated marginal neural ratio estimation (TMN…

A Repository for Formal Contexts
Tom Hanika, Robert J\"aschke
https://arxiv.org/abs/2404.04344 https://arxiv.org/pdf/2404.04344<…

A Repository for Formal Contexts
Data is always at the center of the theoretical development and investigation of the applicability of formal concept analysis. It is therefore not surprising that a large number of data sets are repeatedly used in scholarly articles and software tools, acting as de facto standard data sets. However, the distribution of the data sets poses a problem for the sustainable development of the research field. There is a lack of a central location that provides and describes FCA data sets and links the…

Investigating Interaction Modes and User Agency in Human-LLM Collaboration for Domain-Specific Data Analysis
Jiajing Guo, Vikram Mohanty, Jorge Piazentin Ono, Hongtao Hao, Liang Gou, Liu Ren
https://arxiv.org/abs/2405.05548

Investigating Interaction Modes and User Agency in Human-LLM Collaboration for Domain-Specific Data Analysis
Despite demonstrating robust capabilities in performing tasks related to general-domain data-operation tasks, Large Language Models (LLMs) may exhibit shortcomings when applied to domain-specific tasks. We consider the design of domain-specific AI-powered data analysis tools from two dimensions: interaction and user agency. We implemented two design probes that fall on the two ends of the two dimensions: an open-ended high agency (OHA) prototype and a structured low agency (SLA) prototype. We c…

A step towards the integration of machine learning and small area estimation
Tomasz \.Z\k{a}d{\l}o, Adam Chwila
https://arxiv.org/abs/2402.07521 https://…

A step towards the integration of machine learning and small area estimation
The use of machine-learning techniques has grown in numerous research areas. Currently, it is also widely used in statistics, including the official statistics for data collection (e.g. satellite imagery, web scraping and text mining, data cleaning, integration and imputation) but also for data analysis. However, the usage of these methods in survey sampling including small area estimation is still very limited. Therefore, we propose a predictor supported by these algorithms which can be used t…

Analysis of Distributed Optimization Algorithms on a Real Processing-In-Memory System
Steve Rhyner, Haocong Luo, Juan G\'omez-Luna, Mohammad Sadrosadati, Jiawei Jiang, Ataberk Olgun, Harshita Gupta, Ce Zhang, Onur Mutlu
https://arxiv.org/abs/2404.07164

Analysis of Distributed Optimization Algorithms on a Real Processing-In-Memory System
Machine Learning (ML) training on large-scale datasets is a very expensive and time-consuming workload. Processor-centric architectures (e.g., CPU, GPU) commonly used for modern ML training workloads are limited by the data movement bottleneck, i.e., due to repeatedly accessing the training dataset. As a result, processor-centric systems suffer from performance degradation and high energy consumption. Processing-In-Memory (PIM) is a promising solution to alleviate the data movement bottleneck b…

GlossLM: Multilingual Pretraining for Low-Resource Interlinear Glossing
Michael GinnUniversity of Colorado, Lindia TjuatjaCarnegie Mellon University, Taiqi HeCarnegie Mellon University, Enora RiceUniversity of Colorado, Graham NeubigCarnegie Mellon University, Alexis PalmerUniversity of Colorado, Lori LevinCarnegie Mellon University
https://

GlossLM: Multilingual Pretraining for Low-Resource Interlinear Glossing
A key aspect of language documentation is the creation of annotated text in a format such as interlinear glossed text (IGT), which captures fine-grained morphosyntactic analyses in a morpheme-by-morpheme format. Prior work has explored methods to automatically generate IGT in order to reduce the time cost of language analysis. However, many languages (particularly those requiring preservation) lack sufficient IGT data to train effective models, and crosslingual transfer has been proposed as a m…

Towards $21$-cm intensity mapping at $z=2.28$ with uGMRT using the tapered gridded estimator -- IV. Wideband analysis
Khandakar Md Asif ElahiIndian Institute of Technology Kharagpur, Kharagpur, India, Somnath BharadwajIndian Institute of Technology Kharagpur, Kharagpur, India, Srijita PalIndian Institute of Science, Bangalore, India, Abhik GhoshBanwarilal Bhalotia College, Asansol, India, Sk. Saiyad AliJadavpur University, Kolkata, India, Samir ChoudhuriIndian Institute of Technology M…

Towards $21$-cm intensity mapping at $z=2.28$ with uGMRT using the tapered gridded estimator -- IV. Wideband analysis
We present a Wideband Tapered Gridded Estimator (TGE), which incorporates baseline migration and variation of the primary beam pattern for neutral hydrogen (${\rm H\hspace{0.5mm}}{\scriptsize {\rm I}}$) 21-cm intensity mapping (IM) with large frequency bandwidth radio-interferometric observations. Here we have analysed $394-494 \, {\rm MHz}$ $(z = 1.9 - 2.6)$ uGMRT data to estimate the Multi-frequency Angular Power Spectrum (MAPS) $C_\ell(Δν)$ from which we have removed the foregrounds using …

Investigating Interaction Modes and User Agency in Human-LLM Collaboration for Domain-Specific Data Analysis
Jiajing Guo, Vikram Mohanty, Jorge Piazentin Ono, Hongtao Hao, Liang Gou, Liu Ren
https://arxiv.org/abs/2405.05548

Investigating Interaction Modes and User Agency in Human-LLM Collaboration for Domain-Specific Data Analysis
Despite demonstrating robust capabilities in performing tasks related to general-domain data-operation tasks, Large Language Models (LLMs) may exhibit shortcomings when applied to domain-specific tasks. We consider the design of domain-specific AI-powered data analysis tools from two dimensions: interaction and user agency. We implemented two design probes that fall on the two ends of the two dimensions: an open-ended high agency (OHA) prototype and a structured low agency (SLA) prototype. We c…

This https://arxiv.org/abs/2311.02206 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Modern Datalog on the GPU
Modern deductive database engines (e.g., LogicBlox and Soufflé) enable their users to write declarative queries which compute recursive deductions over extensional data, leaving their high-performance operationalization (query planning, semi-naïve evaluation, and parallelization) to the engine. Such engines form the backbone of modern high-throughput applications in static analysis, security auditing, social-media mining, and business analytics. State-of-the-art engines are built upon nested …

This https://arxiv.org/abs/2403.15721 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Design and Implementation of an Analysis Pipeline for Heterogeneous Data
Managing and preparing complex data for deep learning, a prevalent approach in large-scale data science can be challenging. Data transfer for model training also presents difficulties, impacting scientific fields like genomics, climate modeling, and astronomy. A large-scale solution like Google Pathways with a distributed execution environment for deep learning models exists but is proprietary. Integrating existing open-source, scalable runtime tools and data frameworks on high-performance comp…

GuideGen: A Text-guided Framework for Joint CT Volume and Anatomical structure Generation
Linrui Dai, Rongzhao Zhang, Zhongzhen Huang, Xiaofan Zhang
https://arxiv.org/abs/2403.07247

GuideGen: A Text-guided Framework for Joint CT Volume and Anatomical structure Generation
The annotation burden and extensive labor for gathering a large medical dataset with images and corresponding labels are rarely cost-effective and highly intimidating. This results in a lack of abundant training data that undermines downstream tasks and partially contributes to the challenge image analysis faces in the medical field. As a workaround, given the recent success of generative neural models, it is now possible to synthesize image datasets at a high fidelity guided by external constr…

Preliminary Guidelines For Combining Data Integration and Visual Data Analysis
Adam Coscia, Ashley Suh, Remco Chang, Alex Endert
https://arxiv.org/abs/2403.04757

Preliminary Guidelines For Combining Data Integration and Visual Data Analysis
Data integration is often performed to consolidate information from multiple disparate data sources during visual data analysis. However, integration operations are usually separate from visual analytics operations such as encode and filter in both interface design and empirical research. We conducted a preliminary user study to investigate whether and how data integration should be incorporated directly into the visual analytics process. We used two interface alternatives featuring contrasting…

A General Identification Algorithm For Data Fusion Problems Under Systematic Selection
Jaron J. R. Lee, AmirEmad Ghassami, Ilya Shpitser
https://arxiv.org/abs/2404.06602

A General Identification Algorithm For Data Fusion Problems Under Systematic Selection
Causal inference is made challenging by confounding, selection bias, and other complications. A common approach to addressing these difficulties is the inclusion of auxiliary data on the superpopulation of interest. Such data may measure a different set of variables, or be obtained under different experimental conditions than the primary dataset. Analysis based on multiple datasets must carefully account for similarities between datasets, while appropriately accounting for differences. In add…

Seeds of Stereotypes: A Large-Scale Textual Analysis of Race and Gender Associations with Diseases in Online Sources
Lasse Hyldig Hansen, Nikolaj Andersen, Jack Gallifant, Liam G. McCoy, James K Stone, Nura Izath, Marcela Aguirre-Jerez, Danielle S Bitterman, Judy Gichoya, Leo Anthony Celi
https://arxiv.org/abs/2405.05049<…

Seeds of Stereotypes: A Large-Scale Textual Analysis of Race and Gender Associations with Diseases in Online Sources
Background Advancements in Large Language Models (LLMs) hold transformative potential in healthcare, however, recent work has raised concern about the tendency of these models to produce outputs that display racial or gender biases. Although training data is a likely source of such biases, exploration of disease and demographic associations in text data at scale has been limited. Methods We conducted a large-scale textual analysis using a dataset comprising diverse web sources, including Arxi…

BOLD v4: A Centralized Bioinformatics Platform for DNA-based Biodiversity Data
Sujeevan Ratnasingham, Catherine Wei, Dean Chan, Jireh Agda, Josh Agda, Liliana Ballesteros-Mejia, Hamza Ait Boutou, Zak Mohammad El Bastami, Eddie Ma, Ramya Manjunath, Dana Rea, Chris Ho, Angela Telfer, Jaclyn McKeowan, Miduna Rahulan, Claudia Steinke, Justin Dorsheimer, Megan Milton, Paul D. N. Hebert

BOLD v4: A Centralized Bioinformatics Platform for DNA-based Biodiversity Data
BOLD, the Barcode of Life Data System, supports the acquisition, storage, validation, analysis, and publication of DNA barcodes, activities requiring the integration of molecular, morphological, and distributional data. Its pivotal role in curating the reference library of DNA barcodes, coupled with its data management and analysis capabilities, make it a central resource for biodiversity science. It enables rapid, accurate identification of specimens and also reveals patterns of genetic divers…

Analysis of Distributed Algorithms for Big-data
Rajendra Purohit, K R Chowdhary, S D Purohit
https://arxiv.org/abs/2404.06461 https://

Analysis of Distributed Algorithms for Big-data
The parallel and distributed processing are becoming de facto industry standard, and a large part of the current research is targeted on how to make computing scalable and distributed, dynamically, without allocating the resources on permanent basis. The present article focuses on the study and performance of distributed and parallel algorithms their file systems, to achieve scalability at local level (OpenMP platform), and at global level where computing and file systems are distributed. Vario…

Seeds of Stereotypes: A Large-Scale Textual Analysis of Race and Gender Associations with Diseases in Online Sources
Lasse Hyldig Hansen, Nikolaj Andersen, Jack Gallifant, Liam G. McCoy, James K Stone, Nura Izath, Marcela Aguirre-Jerez, Danielle S Bitterman, Judy Gichoya, Leo Anthony Celi
https://arxiv.org/abs/2405.05049<…

Seeds of Stereotypes: A Large-Scale Textual Analysis of Race and Gender Associations with Diseases in Online Sources
Background Advancements in Large Language Models (LLMs) hold transformative potential in healthcare, however, recent work has raised concern about the tendency of these models to produce outputs that display racial or gender biases. Although training data is a likely source of such biases, exploration of disease and demographic associations in text data at scale has been limited. Methods We conducted a large-scale textual analysis using a dataset comprising diverse web sources, including Arxi…

BOLD v4: A Centralized Bioinformatics Platform for DNA-based Biodiversity Data
Sujeevan Ratnasingham, Catherine Wei, Dean Chan, Jireh Agda, Josh Agda, Liliana Ballesteros-Mejia, Hamza Ait Boutou, Zak Mohammad El Bastami, Eddie Ma, Ramya Manjunath, Dana Rea, Chris Ho, Angela Telfer, Jaclyn McKeowan, Miduna Rahulan, Claudia Steinke, Justin Dorsheimer, Megan Milton, Paul D. N. Hebert

BOLD v4: A Centralized Bioinformatics Platform for DNA-based Biodiversity Data
BOLD, the Barcode of Life Data System, supports the acquisition, storage, validation, analysis, and publication of DNA barcodes, activities requiring the integration of molecular, morphological, and distributional data. Its pivotal role in curating the reference library of DNA barcodes, coupled with its data management and analysis capabilities, make it a central resource for biodiversity science. It enables rapid, accurate identification of specimens and also reveals patterns of genetic divers…