Tootfinder

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
Simulation study to evaluate when Plasmode simulation is superior to parametric simulation in est...

Detecting Hardware Trojans in Microprocessors via Hardware Error Correction Code-based Modules
Alessandro Palumbo, Ruben Salvador
https://arxiv.org/abs/2506.15417

Detecting Hardware Trojans in Microprocessors via Hardware Error Correction Code-based Modules
Software-exploitable Hardware Trojans (HTs) enable attackers to execute unauthorized software or gain illicit access to privileged operations. This manuscript introduces a hardware-based methodology for detecting runtime HT activations using Error Correction Codes (ECCs) on a RISC-V microprocessor. Specifically, it focuses on HTs that inject malicious instructions, disrupting the normal execution flow by triggering unauthorized programs. To counter this threat, the manuscript introduces a Hardw…

Estimation of Treatment Effects in Extreme and Unobserved Data
Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis
https://arxiv.org/abs/2506.14051 https://

Estimation of Treatment Effects in Extreme and Unobserved Data
Causal effect estimation seeks to determine the impact of an intervention from observational data. However, the existing causal inference literature primarily addresses treatment effects on frequently occurring events. But what if we are interested in estimating the effects of a policy intervention whose benefits, while potentially important, can only be observed and measured in rare yet impactful events, such as extreme climate events? The standard causal inference methodology is not designed …

On the accretion luminosity of Class I protostars
L. Testi, A. Natta, S. Gozzi, C. F. Manara, J. P. Williams, R. Claes, U. Lebreuilly, P. Hennebelle, R. Klessen, S. Molinari
https://arxiv.org/abs/2506.14601

On the accretion luminosity of Class I protostars
The value of the accretion luminosity during the early phases of star formation is a crucial information which helps us understand how stars form, yet it is still very difficult to obtain. We develop a new methodology to measure accretion luminosity using mid-infrared hydrogen recombination lines, and apply it to a limited sample of Class~I protostars in the Taurus and Ophiuchus star forming regions. We adopt the commonly used assumption that the properties of disk-protostar accretion in Class …

A Determination of $\alpha_s(m_Z)$ at aN$^3$LO$_{\bf QCD}\otimes {\bf NLO}_{\bf QED}$ Accuracy from a Global PDF Analysis
The NNPDF Collaboration, Richard D. Ball, Andrea Barontini, Juan Cruz-Martinez, Stefano Forte, Felix Hekhorn, Emanuele R. Nocera, Juan Rojo, Roy Stegeman
https://arxiv.org/abs/2506.13871

A Determination of $α_s(m_Z)$ at aN$^3$LO$_{\bf QCD}\otimes {\bf NLO}_{\bf QED}$ Accuracy from a Global PDF Analysis
We present a determination of the strong coupling $α_s(m_Z)$ from a global dataset including both fixed-target and collider data from deep-inelastic scattering and a variety of hadronic processes, with a simultaneous determination of parton distribution functions (PDFs) based on the NNPDF4.0 methodology. This determination is performed at NNLO and approximate N$^3$LO (aN$^3$LO) perturbative QCD accuracy, including QED corrections and a photon PDF up to NLO accuracy. We extract $α_s$ using two…

Intrinsic and Extrinsic Organized Attention: Softmax Invariance and Network Sparsity
Oluwadamilola Fasina, Ruben V. C. Pohle, Pei-Chun Su, Ronald R. Coifman
https://arxiv.org/abs/2506.15541

Intrinsic and Extrinsic Organized Attention: Softmax Invariance and Network Sparsity
We examine the intrinsic (within the attention head) and extrinsic (amongst the attention heads) structure of the self-attention mechanism in transformers. Theoretical evidence for invariance of the self-attention mechanism to softmax activation is obtained by appealing to paradifferential calculus, (and is supported by computational examples), which relies on the intrinsic organization of the attention heads. Furthermore, we use an existing methodology for hierarchical organization of tensors …

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- Fast computation of exact confidence intervals for randomized experiments with binary outcomes
P. M. Aronow, Haoge Chang, Patrick Lopatto

Tidy Modeling with R: #rstats #machinelearning

Tidy Modeling with R
The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles. This book provides a thorough introduction to how to use tidymodels, and an outline of good methodology and statistical practice for phases of the modeling process.

Automatic Qiskit Code Refactoring Using Large Language Models
Jos\'e Manuel Su\'arez, Luis Mariano Bibb\'o, Joaquin Bogado, Alejandro Fernandez
https://arxiv.org/abs/2506.14535

Automatic Qiskit Code Refactoring Using Large Language Models
As quantum software frameworks evolve, developers face increasing challenges in maintaining compatibility with rapidly changing APIs. In this work, we present a novel methodology for refactoring Qiskit code using large language models (LLMs). We begin by extracting a taxonomy of migration scenarios from the different sources of official Qiskit documentation (such as release notes), capturing common patterns such as migration of functionality to different modules and deprecated usage. This taxon…

The Impact and Influence of Academic Genealogies
Bryan Briones, Ronald E. Mickens, Charmayne Patterson
https://arxiv.org/abs/2505.11503 https://

The Impact and Influence of Academic Genealogies
We introduce the concept of an academic genealogy, or AG, and illustrate how AG charts may be constructed and then demonstrate how this methodology can be used by applying it to create the partial or full AG charts to two scientists, Paul A. Samuelson and Ronald E. Mickens.

Side-Channel Extraction of Dataflow AI Accelerator Hardware Parameters
Guillaume Lomet, Ruben Salvador, Brice Colombier, Vincent Grosso, Olivier Sentieys, Cedric Killian
https://arxiv.org/abs/2506.15432

Side-Channel Extraction of Dataflow AI Accelerator Hardware Parameters
Dataflow neural network accelerators efficiently process AI tasks on FPGAs, with deployment simplified by ready-to-use frameworks and pre-trained models. However, this convenience makes them vulnerable to malicious actors seeking to reverse engineer valuable Intellectual Property (IP) through Side-Channel Attacks (SCA). This paper proposes a methodology to recover the hardware configuration of dataflow accelerators generated with the FINN framework. Through unsupervised dimensionality reduction…

Restarted contractive operators to learn at equilibrium
Leo Davy (Phys-ENS), Luis M. Briceno-Arias (UTFSM), N. Pustelnik (Phys-ENS)
https://arxiv.org/abs/2506.13239

Restarted contractive operators to learn at equilibrium
Bilevel optimization offers a methodology to learn hyperparameters in imaging inverse problems, yet its integration with automatic differentiation techniques remains challenging. On the one hand, inverse problems are typically solved by iterating arbitrarily many times some elementary scheme which maps any point to the minimizer of an energy functional, known as equilibrium point. On the other hand, introducing parameters to be learned in the energy functional yield architectures very reminisce…

interactome_stelzl: Stelzl human interactome (2005)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred via high-throughput Y2H experiments using bait and prey methodology.
This network has 1706 nodes and 6207 edges.
Tags: Biological, Protein interactions, Unweighted

Shadow and strong-field lensing effects of quantum-corrected RN blackhole
Nikko John Leo S. Lobos, Virginia C. Fernandez
https://arxiv.org/abs/2506.13091 h…

Shadow and strong-field lensing effects of quantum-corrected RN blackhole
We investigate the effects of a quantum-corrected RN black hole on the shadow using Perlick methodology and strong-field gravitational lensing using Tsukamoto's approach. Our analysis shows that even at extreme charge, the predicted shadow remains within the observational bounds set by the Event Horizon Telescope for both M87* and Sgr A*. The charge significantly influences the deflection angle near the photon sphere, while its effect diminishes at larger distances. In contrast, the quantum cor…

EPC Framework for BESS Projects
Zeenat Hameed, Chresten Tr{\ae}holt
https://arxiv.org/abs/2506.13281 https://arxiv.org/pdf/2506.13281…

EPC Framework for BESS Projects
Battery Energy Storage Systems (BESS) are critical for modern power networks, supporting grid services such as frequency regulation, peak shaving, and black start. Delivering a BESS under an Engineering, Procurement, and Construction (EPC) model requires a concise methodology that balances regulatory compliance, technical details, and schedule efficiency. This paper presents a streamlined, five step EPC framework covering feasibility assessment, permitting, procurement, construction, and commis…

General-relativistic magnetar magnetospheres in 3D with physics-informed neural networks
Petros Stefanou, Arthur G. Suvorov, Jose A. Pons
https://arxiv.org/abs/2506.13519

General-relativistic magnetar magnetospheres in 3D with physics-informed neural networks
Magnetar phenomena are likely intertwined with the location and structure of magnetospheric currents. While general-relativistic effects may be important in shaping the force-free equilibria describing static configurations, most studies have been restricted to axial symmetry. Using a novel methodology based on physics-informed neural networks, fully three-dimensional configurations of varying stellar compactness are constructed. Realistic profiles for surface currents, qualitatively capturing …

[2025-06-19 Thu (UTC), 8 new articles found for stat.ME Methodology]
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Validation of Hermes-3 turbulence simulations against the TCV-X21 diverted L-mode reference case
B D Dudson, M Kryjak, H Muhammed, J Omotani
https://arxiv.org/abs/2506.12180

Validation of Hermes-3 turbulence simulations against the TCV-X21 diverted L-mode reference case
Electrostatic flux-driven turbulence simulations with the Hermes-3 code are performed in TCV L-mode conditions in forward and reversed toroidal field configurations, and compared to the TCV-X21 reference dataset [D.S. Oliveira and T. Body et al. 2022] qualitatively and with a quantitative methodology. Using only the magnetic equilibrium, total power across the separatrix (120kW) and total particle flux to the targets (3e21/s) as inputs, the simulations produce time-averaged plasma profiles in g…

A sequential ensemble approach to epidemic modeling: Combining Hawkes and SEIR models using SMC$^2$
Dhorasso Temfack, Jason Wyse
https://arxiv.org/abs/2506.15511

A sequential ensemble approach to epidemic modeling: Combining Hawkes and SEIR models using SMC$^2$
This paper proposes a sequential ensemble methodology for epidemic modeling that integrates discrete-time Hawkes processes (DTHP) and Susceptible-Exposed-Infectious-Removed (SEIR) models. Motivated by the need for accurate and reliable epidemic forecasts to inform timely public health interventions, we develop a flexible model averaging (MA) framework using Sequential Monte Carlo Squared. While generating estimates from each model individually, our approach dynamically assigns them weights base…

The Anatomy of India's Industrial Interdependencies: 7-Digit Product-Level Supply-Use and Input-Output Tables from ASI Data (2016-2022) with a Case Study of the Mobile Phone Sector
Sourish Dutta
https://arxiv.org/abs/2506.13936

The Anatomy of India's Industrial Interdependencies: 7-Digit Product-Level Supply-Use and Input-Output Tables from ASI Data (2016-2022) with a Case Study of the Mobile Phone Sector
Outlines the construction of high-resolution, 7-digit product-level Supply-Use Tables (SUTs) and symmetric Input-Output Tables (IOTs) for the Indian economy, leveraging microdata from the Annual Survey of Industries (ASI) for the period 2016-2022. We delineate a robust methodology encompassing the generation of detailed input and output flows, with a particular focus on the reconciliation of data from registered and unregistered manufacturing sectors through a meticulously developed NPCMS-NIC c…

Defining the Game Producer: A Mapping of Key Characteristics and Differentiators of the Professional Behind Digital Game Production
Rafael C. Lopes, Danilo M. Ribeiro
https://arxiv.org/abs/2506.14409

Defining the Game Producer: A Mapping of Key Characteristics and Differentiators of the Professional Behind Digital Game Production
Introduction: As digital games grow in complexity, the role of the Game Producer becomes increasingly relevant for aligning creative, technical, and business dimensions. Objective: This study aimed to identify and map the main characteristics, skills, and competencies that define the Digital Game Producer profile. Methodology: A qualitative investigation was conducted with 11 semi-structured interviews, analyzed through Grounded Theory to build categories grounded in professional practice. Resu…

The Journey from Planck Distribution to Bose Statistics From Classical to Quantum Mechanics and Beyond
Shreetam Dash, Prasanta K. Panigrahi
https://arxiv.org/abs/2505.11519

The Journey from Planck Distribution to Bose Statistics From Classical to Quantum Mechanics and Beyond
In 1924, Satyendra Nath Bose's pioneering work laid the foundation for Bose-Einstein statistics, which describes particles with integral spins. His derivation of Planck's law for blackbody radiation bypassed classical assumptions, relying instead on the statistical mechanics of light quanta. Bose's methodology addressed limitations in existing theories, such as Einstein's dependence on classical concepts like Wien's displacement law and Bohr's correspondence principle. Further, his work undersc…

MIR: Methodology Inspiration Retrieval for Scientific Research Problems
Aniketh Garikaparthi, Manasi Patwardhan, Aditya Sanjiv Kanade, Aman Hassan, Lovekesh Vig, Arman Cohan
https://arxiv.org/abs/2506.00249

MIR: Methodology Inspiration Retrieval for Scientific Research Problems
There has been a surge of interest in harnessing the reasoning capabilities of Large Language Models (LLMs) to accelerate scientific discovery. While existing approaches rely on grounding the discovery process within the relevant literature, effectiveness varies significantly with the quality and nature of the retrieved literature. We address the challenge of retrieving prior work whose concepts can inspire solutions for a given research problem, a task we define as Methodology Inspiration Retr…

Building a Media Ecosystem Observatory from Scratch: Infrastructure, Methodology, and Insights
Zeynep Pehlivan, Saewon Park, Alexei Sisulu Abrahams, Mika Desblancs-Patel, Benjamin David Steel, Aengus Bridgman
https://arxiv.org/abs/2506.10942

Building a Media Ecosystem Observatory from Scratch: Infrastructure, Methodology, and Insights
Understanding the flow of information across today's fragmented digital media landscape requires scalable, cross-platform infrastructure. In this paper, we present the Canadian Media Ecosystem Observatory, a national-scale infrastructure designed to monitor political and media discourse across platforms in near real time. Media Ecosystem Observatory (MEO) data infrastructure features custom crawlers for major platforms, a unified indexing pipeline, and a normalization layer that harmonizes he…

[2025-06-18 Wed (UTC), 10 new articles found for stat.ME Methodology]
toXiv_bot_toot

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology
Gibaek Kim, Jungho Kim
https://arxiv.org/abs/2506.10418

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology
We propose a neural network(NN)-based surrogate modeling framework for photonic device optimization, especially in domains with imbalanced feature importance and high data generation costs. Our framework, which comprises physics-based transfer learning (PBTL)-enhanced surrogate modeling and scalarized multi-objective genetic algorithms (GAs), offers a generalizable solution for photonic design automation with minimal data resources.To validate the framework, we optimize mid-infrared quantum cas…

(SimPhon Speech Test): A Data-Driven Method for In Silico Design and Validation of a Phonetically Balanced Speech Test
Stefan Bleeck
https://arxiv.org/abs/2506.11620

(SimPhon Speech Test): A Data-Driven Method for In Silico Design and Validation of a Phonetically Balanced Speech Test
Traditional audiometry often provides an incomplete characterization of the functional impact of hearing loss on speech understanding, particularly for supra-threshold deficits common in presbycusis. This motivates the development of more diagnostically specific speech perception tests. We introduce the Simulated Phoneme Speech Test (SimPhon Speech Test) methodology, a novel, multi-stage computational pipeline for the in silico design and validation of a phonetically balanced minimal-pair speec…

Olfactory Inertial Odometry: Methodology for Effective Robot Navigation by Scent
Kordel K. France, Ovidiu Daescu
https://arxiv.org/abs/2506.02373 https://

Olfactory Inertial Odometry: Methodology for Effective Robot Navigation by Scent
Olfactory navigation is one of the most primitive mechanisms of exploration used by organisms. Navigation by machine olfaction (artificial smell) is a very difficult task to both simulate and solve. With this work, we define olfactory inertial odometry (OIO), a framework for using inertial kinematics, and fast-sampling olfaction sensors to enable navigation by scent analogous to visual inertial odometry (VIO). We establish how principles from SLAM and VIO can be extrapolated to olfaction to ena…

Canonical Partition Function on a Quantum Computer through Trotter Interpolation
Taozhi Guo, Gumaro Rendon, Rutuja Kshirsagar
https://arxiv.org/abs/2506.09318

Canonical Partition Function on a Quantum Computer through Trotter Interpolation
In this work, we present a Gibbs state observable estimation algorithm based on Trotter interpolation, which reaches a state-of-the-art quantum computational cost of $ \tilde{O}(β\log{1/ε})$. Our approach saves $\log(Γ)$ ancilla qubits compared with the qubitization-based methods for Hamiltonian with $Γ$ stages. To provide a robust assessment of our approach, we benchmark our results against state-of-the-art methodology using the SYK model as a testbed. Our method provides an efficient alte…

Unpacking Graduate Students' Learning Experience with Generative AI Teaching Assistant in A Quantitative Methodology Course
Zhanxin Hao, Haifeng Luo, Yongyi Chen, Yu Zhang
https://arxiv.org/abs/2506.02966

Unpacking Graduate Students' Learning Experience with Generative AI Teaching Assistant in A Quantitative Methodology Course
The study was conducted in an Advanced Quantitative Research Methods course involving 20 graduate students. During the course, student inquiries made to the AI were recorded and coded using Bloom's taxonomy and the CLEAR framework. A series of independent sample t-tests and poisson regression analyses were employed to analyse the characteristics of different questions asked by students with different backgrounds. Post course interviews were conducted with 10 students to gain deeper insights int…

Deployment of Containerized Simulations in an API-Driven Distributed Infrastructure
Tim Kraus, Axel Sauer, Ingo Feldner
https://arxiv.org/abs/2506.10642 ht…

Deployment of Containerized Simulations in an API-Driven Distributed Infrastructure
The increasingly dynamic market for embedded systems makes virtual prototypes an indispensable tool for hardware/software codesign. The broad acceptance of the methodology has led to a diverse range of solutions: from open-source, pure console-based simulators to highly capable commercial simulation tools. In this work we present SUNRISE, an infrastructure to provide users a unified approach to utilizing virtual prototyping solutions, facilitate access to various simulation technologies and boo…

This https://arxiv.org/abs/2505.15030 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Harnessing On-Device Large Language Model: Empirical Results and Implications for AI PC
The increasing deployment of Large Language Models (LLMs) on edge devices, driven by model advancements and hardware improvements, offers significant privacy benefits. However, these on-device LLMs inherently face performance limitations due to reduced model capacity and necessary compression techniques. To address this, we introduce a systematic methodology -- encompassing model capability, development efficiency, and system resources -- for evaluating on-device LLMs. Our comprehensive evaluat…

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
Conformal Risk Control
https://arxiv.org/abs…

Verification of Quantum Circuits through Barrier Certificates using a Scenario Approach
Siwei Hu, Victor Lopata, Sadegh Soudjani, Paolo Zuliani
https://arxiv.org/abs/2506.07635

Verification of Quantum Circuits through Barrier Certificates using a Scenario Approach
In recent years, various techniques have been explored for the verification of quantum circuits, including the use of barrier certificates, mathematical tools capable of demonstrating the correctness of such systems. These certificates ensure that, starting from initial states and applying the system's dynamics, the system will never reach undesired states. In this paper, we propose a methodology for synthesizing such certificates for quantum circuits using a scenario-based approach, for both f…

State constrained stochastic optimal control of a PV system with battery storage via Fokker-Planck and Hamilton-Jacobi-Bellman equations
Alfredo Berm\'udez, Iago Pad\'in
https://arxiv.org/abs/2506.11765

State constrained stochastic optimal control of a PV system with battery storage via Fokker-Planck and Hamilton-Jacobi-Bellman equations
With the growing global emphasis on sustainability and the implementation of contemporary environmental policies, the penetration of renewable energy sources has significantly increased --primarily due to their inexhaustible nature. In this context, integrating photovoltaic (PV) modules with energy storage systems offers a means to mitigate the inherent variability of solar energy. This paper proposes an optimal methodology for power management and market bidding strategies tailored to PV power…

SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research
Ahmed Adel Attia, Jing Liu, Carl Espy-Wilson
https://arxiv.org/abs/2506.09206

SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research
The scarcity of large-scale classroom speech data has hindered the development of AI-driven speech models for education. Public classroom datasets remain limited, and the lack of a dedicated classroom noise corpus prevents the use of standard data augmentation techniques. In this paper, we introduce a scalable methodology for synthesizing classroom noise using game engines, a framework that extends to other domains. Using this methodology, we present SimClass, a dataset that includes both a s…

Novel methodology to obtain transonic solutions for dissipative flows around compact objects
Shilpa Sarkar
https://arxiv.org/abs/2505.24839 https://…

Novel methodology to obtain transonic solutions for dissipative flows around compact objects
A novel methodology to obtain global transonic solutions around compact objects is reported here. A unified methodology to obtain accretion as well as wind solutions around these objects has been presented. Flows around compact objects are dissipative, and the conservation equations are therefore stiff. In such conditions, obtaining of sonic point(s) and hence, the transonic solution is not trivial. The conserved equations of motion fail to integrate in the presence of realistic viscosity, ther…

This https://arxiv.org/abs/2402.16887 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

A Comprehensive Survey on Artificial Intelligence for Complex Network: Potential, Methodology and Application
Complex networks pervade various real-world systems, from the natural environment to human societies. The essence of these networks is in their ability to transition and evolve from microscopic disorder-where network topology and node dynamics intertwine-to a macroscopic order characterized by certain collective behaviors. Over the past two decades, complex network science has significantly enhanced our understanding of the statistical mechanics, structures, and dynamics underlying real-world n…

interactome_stelzl: Stelzl human interactome (2005)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred via high-throughput Y2H experiments using bait and prey methodology.
This network has 1706 nodes and 6207 edges.
Tags: Biological, Protein interactions, Unweighted

This https://arxiv.org/abs/2403.18566 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Computer-Assisted Proofs of Existence of Invariant Tori in Quasi-periodic Systems via Fourier Methods
The goal of this paper is to provide a methodology to prove existence of (fiberwise hyperbolic) real-analytic invariant tori in real-analytic quasi-periodic skew-product dynamical systems that present nearly-invariant tori of the same characteristics. The methodology is based on the application of a Newton-Kantorovich theorem whose hypotheses are tested using Fourier analysis methods for a numerical approximation of the parameterization of an invariant torus.

🚣‍♂️ Scientists develop pneumatic propellers that could replace diesel engines in ferry boats
https://techxplore.com/news/2025-04-scientists-pneumatic-propellers-diesel-ferry.html

Scientists develop pneumatic propellers that could replace diesel engines in ferry boats
Scientists say they have created a methodology that could replace two diesel engines powering a ferry boat with pneumatic propellers.

This https://arxiv.org/abs/2412.02544 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Testing inflation on all scales: a case study with $α$-attractors
A plethora of inflationary models can produce interesting small-scale phenomenology, such as enhanced scalar fluctuations leading to primordial black hole (PBH) production and large scalar-induced GW. Nevertheless, good models must simultaneously explain current observations on all scales. In this work, we showcase our methodology to establish the small-scale phenomenology of inflationary models on firm grounds. We consider the case of hybrid $α$-attractors, and focus on a reduced parameter sp…

This https://arxiv.org/abs/2406.12451 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Small maximal clusters are very unlikely in critical random graphs
We describe a probabilistic methodology, based on random walk estimates, to obtain exponential upper bounds for the probability of observing unusually small maximal components in two classical (near-)critical random graph models. More specifically, we analyse the near-critical Erdős-Rényi model $\mathbb{G}(n,p)$ and the random graph $\mathbb{G}(n,d,p)$ obtained by performing near-critical $p$-bond percolation on a simple random $d$-regular graph and show that, for each one of these models, th…

[2025-06-17 Tue (UTC), 27 new articles found for stat.ME Methodology]
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Minimal Pair-Based Evaluation of Code-Switching
Igor Sterner, Simone Teufel
https://arxiv.org/abs/2506.01840 https://arxiv.org/pdf/25…

Minimal Pair-Based Evaluation of Code-Switching
There is a lack of an evaluation methodology that estimates the extent to which large language models (LLMs) use code-switching (CS) in the same way as bilinguals. Existing methods do not have wide language coverage, fail to account for the diverse range of CS phenomena, or do not scale. We propose an intervention based on minimal pairs of CS. Each minimal pair contains one naturally occurring CS sentence and one minimally manipulated variant. We collect up to 1,000 such pairs each for 11 langu…

Discovery of Odd Radio Circles and Other Peculiars in the First Year of the EMU Survey using Object Detection
Nikhel Gupta, Ray P. Norris, Zeeshan Hayder, Minh Huynh, Heinz Andernach, Andrew M. Hopkins, Stanislav Shabala, Lawrence Rudnick, Miroslav D. Filipovi\'c, B\"arbel S. Koribalski, Lars Petersson, X. Rosalind Wang
https://

Discovery of Odd Radio Circles and Other Peculiars in the First Year of the EMU Survey using Object Detection
We present a systematic search for Odd Radio Circles (ORCs) and other unusual radio morphologies using data from the first year of the EMU (Evolutionary Map of the Universe) survey. ORCs are rare, enigmatic objects characterized by edge-brightened rings of radio emission, often found in association with distant galaxies. To identify these objects, we employ a hybrid methodology combining supervised object detection techniques and visual inspection of radio source candidates. This approach leads…

Evasion Attacks Against Bayesian Predictive Models
Pablo G. Arce, Roi Naveiro, David R\'ios Insua
https://arxiv.org/abs/2506.09640 https://

Evasion Attacks Against Bayesian Predictive Models
There is an increasing interest in analyzing the behavior of machine learning systems against adversarial attacks. However, most of the research in adversarial machine learning has focused on studying weaknesses against evasion or poisoning attacks to predictive models in classical setups, with the susceptibility of Bayesian predictive models to attacks remaining underexplored. This paper introduces a general methodology for designing optimal evasion attacks against such models. We investigate …

This https://arxiv.org/abs/2410.05460 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPL_…

It's Not Easy Being Green: On the Energy Efficiency of Programming Languages
Does the choice of programming language affect energy consumption? Previous highly visible studies have established associations between certain programming languages and energy consumption. A causal misinterpretation of this work has led academics and industry leaders to use or support certain languages based on their claimed impact on energy consumption. This paper tackles this causal question directly. It first corrects and improves the measurement methodology used by prior work. It then dev…

Dynamic structure factor of a driven-dissipative Bose-Hubbard model
Subhanka Mal, Anushree Dey, Kingshuk Adikary, Bimalendu Deb
https://arxiv.org/abs/2506.09615

Dynamic structure factor of a driven-dissipative Bose-Hubbard model
Dynamic structure factor (DSF) is important for understanding excitations in many-body physics; it reveals information about the spectral and spatial correlations of fluctuations in quantum systems. Collective phenomena like quantum phase transitions of ultracold atoms are addressed by harnessing density fluctuations. Here, we calculate the DSF of a nonequilibrium spinless Bose-Hubbard model (BHM) from the perspective of dissipative phase transition (DPT) in a steady state. Our methodology uses…

This https://arxiv.org/abs/2505.07614 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Trial and Trust: Addressing Byzantine Attacks with Comprehensive Defense Strategy
Recent advancements in machine learning have improved performance while also increasing computational demands. While federated and distributed setups address these issues, their structure is vulnerable to malicious influences. In this paper, we address a specific threat, Byzantine attacks, where compromised clients inject adversarial updates to derail global convergence. We combine the trust scores concept with trial function methodology to dynamically filter outliers. Our methods address the c…

A data-based notion of quantiles on Hadamard spaces
Ha-Young Shin, Hee-Seok Oh
https://arxiv.org/abs/2506.12534 https://arxiv.org/pdf…

A data-based notion of quantiles on Hadamard spaces
This paper defines an alternative notion, described as data-based, of geometric quantiles on Hadamard spaces, in contrast to the existing methodology, described as parameter-based. In addition to having the same desirable properties as parameter-based quantiles, these data-based quantiles are shown to have several theoretical advantages related to large-sample properties like strong consistency and asymptotic normality, breakdown points, extreme quantiles and the gradient of the loss function. …

Augmented Reality User Interfaces for First Responders: A Scoping Literature Review
Erin Argo, Tanim Ahmed, Sarah Gable, Callie Hampton, Jeronimo Grandi, Regis Kopper
https://arxiv.org/abs/2506.09236

Augmented Reality User Interfaces for First Responders: A Scoping Literature Review
During the past decade, there has been a significant increase in research focused on integrating AR User Interfaces into public safety applications, particularly for first responders in the domains of Emergency Medical Services, Firefighting, and Law Enforcement. This paper presents the results of a scoping review involving the application of AR user interfaces in the public safety domain and applies an established systematic review methodology to provide a comprehensive analysis of the current…

This https://arxiv.org/abs/2406.09096 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Casimir energy for $N$ constant conductivity $δ$-plates with a neural network perception
The Casimir energy for $N$ $δ$-function plates depends on the multiple scattering parameter $Δ$. This $N$-body interaction was distributed into interactions with nearest neighbour scattering and next-to-nearest neighbour scattering based on partitions of $N-1$ and its permutations. Implementing this methodology, we investigate the Casimir interaction for multiple plates with constant conductivity relatable to Graphene. We also study the Casimir energy between a perfect magnetic conductor and …

This https://arxiv.org/abs/2210.11138 has been replaced.
link: https://scholar.google.com/scholar?q=a

New financial ratios based on the compositional data methodology
Due to their type of mathematical construction, the use of standard financial ratios in studies analysing the financial health of a group of firms leads to a series of statistical problems that can invalidate the results obtained. These problems are originated by the asymmetry of financial ratios. The present article justifies the use of a new methodology using compositional data (CoDa) to analyse the financial statements of a sector, improving analyses using conventional ratios since the new m…

EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical Model
Alyssa Pinnock, Shakya Jayakody, Kawsher A Roxy, Md Rubel Ahmed
https://arxiv.org/abs/2506.09061

EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical Model
This paper introduces EdgeProfiler, a fast profiling framework designed for evaluating lightweight Large Language Models (LLMs) on edge systems. While LLMs offer remarkable capabilities in natural language understanding and generation, their high computational, memory, and power requirements often confine them to cloud environments. EdgeProfiler addresses these challenges by providing a systematic methodology for assessing LLM performance in resource-constrained edge settings. The framework pro…

This https://arxiv.org/abs/2503.18617 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Scaling Laws for Emulation of Stellar Spectra
Neural network-based emulators for the inference of stellar parameters and elemental abundances represent an increasingly popular methodology in modern spectroscopic surveys. However, these approaches are often constrained by their emulation precision and domain transfer capabilities. Greater generalizability has previously been achieved only with significantly larger model architectures, as demonstrated by Transformer-based models in natural language processing. This observation aligns with ne…

Using Large Language Models to Simulate Human Behavioural Experiments: Port of Mars
Oliver Slumbers, Joel Z. Leibo, Marco A. Janssen
https://arxiv.org/abs/2506.05555

Using Large Language Models to Simulate Human Behavioural Experiments: Port of Mars
Collective risk social dilemmas (CRSD) highlight a trade-off between individual preferences and the need for all to contribute toward achieving a group objective. Problems such as climate change are in this category, and so it is critical to understand their social underpinnings. However, rigorous CRSD methodology often demands large-scale human experiments but it is difficult to guarantee sufficient power and heterogeneity over socio-demographic factors. Generative AI offers a potential comple…

Wide Reflective Equilibrium in LLM Alignment: Bridging Moral Epistemology and AI Safety
Matthew Brophy
https://arxiv.org/abs/2506.00415 https://

Wide Reflective Equilibrium in LLM Alignment: Bridging Moral Epistemology and AI Safety
As large language models (LLMs) become more powerful and pervasive across society, ensuring these systems are beneficial, safe, and aligned with human values is crucial. Current alignment techniques, like Constitutional AI (CAI), involve complex iterative processes. This paper argues that the Method of Wide Reflective Equilibrium (MWRE) -- a well-established coherentist moral methodology -- offers a uniquely apt framework for understanding current LLM alignment efforts. Moreover, this methodolo…

A Dynamic Framework for Semantic Grouping of Common Data Elements (CDE) Using Embeddings and Clustering
Madan Krishnamurthy, Daniel Korn, Melissa A Haendel, Christopher J Mungall, Anne E Thessen
https://arxiv.org/abs/2506.02160

A Dynamic Framework for Semantic Grouping of Common Data Elements (CDE) Using Embeddings and Clustering
This research aims to develop a dynamic and scalable framework to facilitate harmonization of Common Data Elements (CDEs) across heterogeneous biomedical datasets by addressing challenges such as semantic heterogeneity, structural variability, and context dependence to streamline integration, enhance interoperability, and accelerate scientific discovery. Our methodology leverages Large Language Models (LLMs) for context-aware text embeddings that convert CDEs into dense vectors capturing semant…

Latent Variable Autoregression with Exogenous Inputs
Daniil Bargman
https://arxiv.org/abs/2506.04488 https://arxiv.org/pdf/2506.04488…

Latent Variable Autoregression with Exogenous Inputs
This paper introduces a new least squares regression methodology called (C)LARX: a (constrained) latent variable autoregressive model with exogenous inputs. Two additional contributions are made as a side effect: First, a new matrix operator is introduced for matrices and vectors with blocks along one dimension; Second, a new latent variable regression (LVR) framework is proposed for economics and finance. The empirical section examines how well the stock market predicts real economic activity …

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
Berry-Esseen bounds for design-based causal inference with possibly diverging treatment levels an...

A Preference-Driven Methodology for High-Quality Solidity Code Generation
Zhiyuan Peng, Xin Yin, Chenhao Ying, Chao Ni, Yuan Luo
https://arxiv.org/abs/2506.03006

A Preference-Driven Methodology for High-Quality Solidity Code Generation
While Large Language Models (LLMs) have demonstrated remarkable progress in generating functionally correct Solidity code, they continue to face critical challenges in producing gas-efficient and secure code, which are critical requirements for real-world smart contract deployment. Although recent advances leverage Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) for code preference alignment, existing approaches treat functional correctness, gas optimization, and security …

Midplane based 3D single pass unbiased segment-to-segment contact interaction using penalty method
Indrajeet Sahu, Nik Petrinic
https://arxiv.org/abs/2506.04841

Midplane based 3D single pass unbiased segment-to-segment contact interaction using penalty method
This work introduces a contact interaction methodology for an unbiased treatment of contacting surfaces without assigning surfaces as master and slave. The contact tractions between interacting discrete segments are evaluated with respect to a midplane in a single pass, inherently maintaining the equilibrium of tractions. These tractions are based on the penalisation of true interpenetration between opposite surfaces, and the procedure of their integral for discrete contacting segments is descr…

IntLevPy: A Python library to classify and model intermittent and L\'evy processes
Shailendra Bhandari, Pedro Lencastre, Sergiy Denysov, Yurii Bystryk, Pedro G. Lind
https://arxiv.org/abs/2506.03729

IntLevPy: A Python library to classify and model intermittent and Lévy processes
IntLevPy provides a comprehensive description of the IntLevPy Package, a Python library designed for simulating and analyzing intermittent and Lévy processes. The package includes functionalities for process simulation, including full parameter estimation and fitting optimization for both families of processes, moment calculation, and classification methods. The classification methodology utilizes adjusted-$R^2$ and a noble performance measure Γ, enabling the distinction between intermittent …

Computational adversarial risk analysis for general security games
Jose Manuel Camacho, Roi Naveiro, David Rios Insua
https://arxiv.org/abs/2506.02603 http…

Computational adversarial risk analysis for general security games
This paper provides an efficient computational scheme to handle general security games from an adversarial risk analysis perspective. Two cases in relation to single-stage and multi-stage simultaneous defend-attack games motivate our approach to general setups which uses bi-agent influence diagrams as underlying problem structure and augmented probability simulation as core computational methodology. Theoretical convergence and numerical, modeling, and implementation issues are thoroughly discu…

Automated Symmetric Constructions in Discrete Geometry
Bernardo Subercaseaux, Ethan Mackey, Long Qian, Marijn J. H. Heule
https://arxiv.org/abs/2506.00224 …

Automated Symmetric Constructions in Discrete Geometry
We present a computational methodology for obtaining rotationally symmetric sets of points satisfying discrete geometric constraints, and demonstrate its applicability by discovering new solutions to some well-known problems in combinatorial geometry. Our approach takes the usage of SAT solvers in discrete geometry further by directly embedding rotational symmetry into the combinatorial encoding of geometric configurations. Then, to realize concrete point sets corresponding to abstract designs …

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks
Xiaobei Yan, Han Qiu, Tianwei Zhang
https://arxiv.org/abs/2506.10744

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks
Bit-flip attacks (BFAs) represent a serious threat to Deep Neural Networks (DNNs), where flipping a small number of bits in the model parameters or binary code can significantly degrade the model accuracy or mislead the model prediction in a desired way. Existing defenses exclusively focus on protecting models for specific attacks and platforms, while lacking effectiveness for other scenarios. We propose ObfusBFA, an efficient and holistic methodology to mitigate BFAs targeting both the high-le…

This https://arxiv.org/abs/2506.00168 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

A novel sensitivity analysis method for agent-based models stratifies \emph{in-silico} tumor spheroid simulations
Agent-based models (ABMs) are widely used in biology to understand how individual actions scale into emergent population behavior. Modelers employ sensitivity analysis (SA) algorithms to quantify input parameters' impact on model outputs, however, it is hard to perform SA for ABMs due to their computational and complex nature. In this work, we develop the \textbf{S}imulate, \textbf{S}ummarize, \textbf{R}educe, \textbf{C}luster, and \textbf{A}nalyze (SSRCA) methodology, a machine-learning based …

Veritas: Deterministic Verilog Code Synthesis from LLM-Generated Conjunctive Normal Form
Prithwish Basu Roy, Akashdeep Saha, Manaar Alam, Johann Knechtel, Michail Maniatakos, Ozgur Sinanoglu, Ramesh Karri
https://arxiv.org/abs/2506.00005

Veritas: Deterministic Verilog Code Synthesis from LLM-Generated Conjunctive Normal Form
Automated Verilog code synthesis poses significant challenges and typically demands expert oversight. Traditional high-level synthesis (HLS) methods often fail to scale for real-world designs. While large language models (LLMs) have enhanced scalability, they often introduce syntactical and logical errors requiring extensive post-generation verification. Here, we introduce a novel conjunctive normal form (CNF)-guided synthesis methodology. The idea is to have an LLM generate CNF clauses, a form…

Classification of Extremal Dependence in Financial Markets via Bootstrap Inference
Qian Hui, Sidney I. Resnick, Tiandong Wang
https://arxiv.org/abs/2506.04656

Classification of Extremal Dependence in Financial Markets via Bootstrap Inference
Accurately identifying the extremal dependence structure in multivariate heavy-tailed data is a fundamental yet challenging task, particularly in financial applications. Following a recently proposed bootstrap-based testing procedure, we apply the methodology to absolute log returns of U.S. S&P 500 and Chinese A-share stocks over a time period well before the U.S. election in 2024. The procedure reveals more isolated clustering of dependent assets in the U.S. economy compared with China which e…

This https://arxiv.org/abs/2502.00698 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models
IQ testing has served as a foundational methodology for evaluating human cognitive capabilities, deliberately decoupling assessment from linguistic background, language proficiency, or domain-specific knowledge to isolate core competencies in abstraction and reasoning. Yet, artificial intelligence research currently lacks systematic benchmarks to quantify these critical cognitive capabilities in multimodal systems. To address this crucial gap, we propose MM-IQ, a comprehensive evaluation framew…

Predictive reinforcement learning based adaptive PID controller
Chaoqun Ma, Zhiyong Zhang
https://arxiv.org/abs/2506.08509 https://ar…

Predictive reinforcement learning based adaptive PID controller
Purpose: This study aims to address the challenges of controlling unstable and nonlinear systems by proposing an adaptive PID controller based on predictive reinforcement learning (PRL-PID), where the PRL-PID combines the advantages of both data-driven and model-driven approaches. Design/methodology/approach: A predictive reinforcement learning framework is introduced, incorporating action smooth strategy to suppress overshoot and oscillations, and a hierarchical reward function to support trai…

Empirical and computer-aided robustness analysis of long-step and accelerated methods in smooth convex optimization
Pierre Vernimmen, Fran\c{c}ois Glineur
https://arxiv.org/abs/2506.09730

Empirical and computer-aided robustness analysis of long-step and accelerated methods in smooth convex optimization
This work assesses both empirically and theoretically, using the performance estimation methodology, how robust different first-order optimization methods are when subject to relative inexactness in their gradient computations. Relative inexactness occurs, for example, when compressing the gradient using fewer bits of information, which happens when dealing with large-scale problems on GPUs. Three major families of methods are analyzed: constant step gradient descent, long-step methods, and acc…

Methodological Advances and Challenges in Indirect Treatment Comparisons: A Review of International Guidelines and HAS TC Case Studies
Matthias Monnereau, Ana Jarne, Axel Benoist, Cl\'emence Fradet, Maurice Perol, Thomas Filleron, Louise Baschet
https://arxiv.org/abs/2506.11587

Methodological Advances and Challenges in Indirect Treatment Comparisons: A Review of International Guidelines and HAS TC Case Studies
To evaluate methodological challenges and regulatory considerations of indirect treatment comparisons (ITCs) with the analysis of international health technology assessment guidelines and French Transparency Committee (TC) decisions. We conducted a pragmatic review of ITC guidelines from major health technology assessment (HTA) bodies and multistakeholder organizations. Then, we analyzed TC opinions published between 2021-2023. We extracted data on ITC methodology, therapeutic areas, acceptabil…

Biased rate estimates in bump-hunt searches
William Murray, "Matt O'Neill", "Finn O'Gara"
https://arxiv.org/abs/2506.01505 http…

Biased rate estimates in bump-hunt searches
The cleanest way to discover a new particle is generally the "bump-hunt" methodology: looking for a localised excess in a mass (or related) distribution. However, if the mass of the particle being discovered is not known the procedure of quoting the most significant excess seen is "greedy", and random fluctuations from the background can be merged with the signal. This means that an observed 3σ evidence probably has the rate over-estimated by of order 10% and the mass uncertainty underestimate…

This https://arxiv.org/abs/2503.04672 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Finding White Dwarfs' Hidden Companions using an Unsupervised Machine Learning Technique
White dwarfs (WD) with main-sequence (MS) companions are crucial probes of stellar evolution. However, due to the significant difference in their luminosities, the WD is often outshined by the MS star. The aim of this work is to find hidden companions in Gaia's sample of WD candidates. Our methodology involves applying an unsupervised machine learning algorithm for dimensionality reduction and clustering, known as Self-Organizing Map (SOM), to Gaia BP/RP (XP) spectra. This strategy allows us to…

A Study on the MCP x A2A Framework for Enhancing Interoperability of LLM-based Autonomous Agents
Cheonsu Jeong
https://arxiv.org/abs/2506.01804 https://

A Study on the MCP x A2A Framework for Enhancing Interoperability of LLM-based Autonomous Agents
This paper provides an in-depth technical analysis and implementation methodology of the open-source Agent-to-Agent (A2A) protocol developed by Google and the Model Context Protocol (MCP) introduced by Anthropic. While the evolution of LLM-based autonomous agents is rapidly accelerating, efficient interactions among these agents and their integration with external systems remain significant challenges. In modern AI systems, collaboration between autonomous agents and integration with external t…

This https://arxiv.org/abs/2409.16548 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Optimised neural network predictions of galaxy formation histories using semi-stochastic corrections
We present a novel methodology to improve predictions of galaxy formation histories by incorporating semi-stochastic corrections to account for short-timescale variability. Our paper addresses limitations in existing models that capture broad trends in galaxy evolution, but fail to reproduce the bursty nature of star formation and chemical enrichment, resulting in inaccurate predictions of key observables such as stellar masses, optical spectra, and colour distributions. We introduce a simple t…

Path Signatures for Feature Extraction. An Introduction to the Mathematics Underpinning an Efficient Machine Learning Technique
Stephan Sturm
https://arxiv.org/abs/2506.01815

Path Signatures for Feature Extraction. An Introduction to the Mathematics Underpinning an Efficient Machine Learning Technique
We provide an introduction to the topic of path signatures as means of feature extraction for machine learning from data streams. The article stresses the mathematical theory underlying the signature methodology, highlighting the conceptual character without plunging into the technical details of rigorous proofs. These notes are based on an introductory presentation given to students of the Research Experience for Undergraduates in Industrial Mathematics and Statistics at Worcester Polytechnic …

The Geometry of Extended Kalman Filters on Manifolds with Affine Connection
Yixiao Ge, Pieter van Goor, Robert Mahony
https://arxiv.org/abs/2506.05728 http…

The Geometry of Extended Kalman Filters on Manifolds with Affine Connection
The extended Kalman filter (EKF) has been the industry standard for state estimation problems over the past sixty years. The classical formulation of the EKF is posed for nonlinear systems defined on global Euclidean spaces. The design methodology is regularly applied to systems on smooth manifolds by choosing local coordinates, however, it is well known that this approach is not intrinsic to the manifold and performance depends heavily on choosing 'good' coordinates. In this paper, we propose …

FIST: A Structured Threat Modeling Framework for Fraud Incidents
Yu-Chen Dai, Lu-An Chen, Sy-Jye Her, Yu-Xian Jiang
https://arxiv.org/abs/2506.05740 https:…

FIST: A Structured Threat Modeling Framework for Fraud Incidents
Fraudulent activities are rapidly evolving, employing increasingly diverse and sophisticated methods that pose serious threats to individuals, organizations, and society. This paper proposes the FIST Framework (Fraud Incident Structured Threat Framework), an innovative structured threat modeling methodology specifically designed for fraud scenarios. Inspired by MITRE ATT\&CK and DISARM, FIST systematically incorporates social engineering tactics, stage-based behavioral decomposition, and detail…

Minimal Neuron Circuits -- Part I: Resonators
Amr Nabil, T. Nandha Kumar, Haider Abbas F. Almurib
https://arxiv.org/abs/2506.02341 https://

Minimal Neuron Circuits -- Part I: Resonators
Spiking Neural Networks have earned increased recognition in recent years owing to their biological plausibility and event-driven computation. Spiking neurons are the fundamental building components of Spiking Neural Networks. Those neurons act as computational units that determine the decision to fire an action potential. This work presents a methodology to implement biologically plausible yet scalable spiking neurons in hardware. We show that it is more efficient to design neurons that mimic …

Exploring the Non-uniqueness of Node Co-occurrence Matrices of Hypergraphs
Timothy LaRock, Renaud Lambiotte
https://arxiv.org/abs/2506.01479 https://

Exploring the Non-uniqueness of Node Co-occurrence Matrices of Hypergraphs
Hypergraphs extend traditional networks by capturing multi-way or group interactions. Given the complexity of hypergraph data and the wide range of methodology available for pairwise network analysis, hypergraph data is often projected onto a weighted and undirected network. The simplest of these projections, often referred to as a node co-occurrence matrix, is known to be non-unique, as distinct non-isomorphic hypergraphs can produce the same weighted adjacency matrix. This non-uniqueness rais…

Analysis of Multiple Long Run Relations in Panel Data Models with Applications to Financial Ratios
Alexander Chudik, M. Hashem Pesaran, Ron P. Smith
https://arxiv.org/abs/2506.02135

Analysis of Multiple Long Run Relations in Panel Data Models with Applications to Financial Ratios
This paper provides a new methodology for the analysis of multiple long run relations in panel data models where the cross section dimension, $n$, is large relative to the time series dimension, $T$. For panel data models with large $n$ researchers have focused on panels with a single long run relationship. The main difficulty has been to eliminate short run dynamics without generating significant uncertainty for identification of the long run. We overcome this problem by using non-overlapping …

Deep Retrieval at CheckThat! 2025: Identifying Scientific Papers from Implicit Social Media Mentions via Hybrid Retrieval and Re-Ranking
Pascal J. Sager, Ashwini Kamaraj, Benjamin F. Grewe, Thilo Stadelmann
https://arxiv.org/abs/2505.23250

Deep Retrieval at CheckThat! 2025: Identifying Scientific Papers from Implicit Social Media Mentions via Hybrid Retrieval and Re-Ranking
We present the methodology and results of the Deep Retrieval team for subtask 4b of the CLEF CheckThat! 2025 competition, which focuses on retrieving relevant scientific literature for given social media posts. To address this task, we propose a hybrid retrieval pipeline that combines lexical precision, semantic generalization, and deep contextual re-ranking, enabling robust retrieval that bridges the informal-to-formal language gap. Specifically, we combine BM25-based keyword matching with a F…

Detailed Time Resolved Spectral and Temporal Investigations of SGR J1550-5418 Bursts Detected with Fermi/Gamma-ray Burst Monitor
Mustafa Demirer, Ersin G\"o\u{g}\"u\c{s}, Yuki Kaneko, \"Ozge Keskin, Sinem \c{S}a\c{s}maz, Shotaro Yamasaki
https://arxiv.org/abs/2506.04414

Detailed Time Resolved Spectral and Temporal Investigations of SGR J1550-5418 Bursts Detected with Fermi/Gamma-ray Burst Monitor
We have conducted a time-resolved spectral analysis of magnetar bursts originating from SGR J1550-5418. Our analysis utilizes a two-step methodology for temporal segmentation of the data. We first generated and fitted overlapping time segments. Subsequently, we obtained non-overlapping time segments with varying lengths based on their spectral evolution patterns, employing a machine learning algorithm called k-means clustering. For the fitting process, we employed three distinct models, namely …

Boosting Open-Source LLMs for Program Repair via Reasoning Transfer and LLM-Guided Reinforcement Learning
Xunzhu Tang, Jacques Klein, Tegawend\'e F. Bissyand\'e
https://arxiv.org/abs/2506.03921

Boosting Open-Source LLMs for Program Repair via Reasoning Transfer and LLM-Guided Reinforcement Learning
Several closed-source LLMs have consistently outperformed open-source alternatives in program repair tasks, primarily due to their superior reasoning capabilities and extensive pre-training. This paper introduces Repairity, a novel three-stage methodology that significantly narrows this performance gap through reasoning extraction and reinforcement learning. Our approach: (1) systematically filters high-quality reasoning traces from closed-source models using correctness verification, (2) trans…

Unwinding the rare $\Omega$ sector: Fragmentation of fully charmed baryons from HL-LHC to FCC
Francesco Giovanni Celiberto
https://arxiv.org/abs/2506.00776

Unwinding the rare $Ω$ sector: Fragmentation of fully charmed baryons from HL-LHC to FCC
By adopting a hadron-structure-oriented approach, we present and discuss the release of the novel OMG3Q1.0 set of collinear fragmentation functions for fully charmed, rare $Ω$ baryons. Our methodology combines diquark-like proxy model inputs for both charm-quark and gluon channels, calculated at the initial energy scales, with a DGLAP evolution that ensures a consistent treatment of heavy-quark thresholds, following directly from the HF-NRevo scheme. We complement our work with a phenomenologi…

This https://arxiv.org/abs/2409.07138 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Contiguous Zero-Copy for Encrypted Transport Protocols
We propose in this paper to revisit the design of existing encrypted transport protocols to improve their efficiency. We call the methodology "Reverso" from reversing the order of field elements within a protocol specification. We detail how such a benign-looking change within the specifications may unlock implementation optimizations for encrypted protocols during data transport. To demonstrate our findings, we release quiceh, a QUIC implementation of QUIC VReverso, an extension of the QUIC V1…

[2025-06-13 Fri (UTC), 6 new articles found for stat.ME Methodology]
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A Vertical Approach to Designing and Managing Sustainable Heterogeneous Edge Data Centers
Aikaterini Maria Panteleaki, Varatheepan Paramanayakam, Vasileios Pentsos, Andreas Karatzas, Spyros Tragoudas, Iraklis Anagnostopoulos
https://arxiv.org/abs/2506.01712

A Vertical Approach to Designing and Managing Sustainable Heterogeneous Edge Data Centers
The increasing demand for Artificial Intelligence (AI) computing poses significant environmental challenges, with both operational and embodied carbon emissions becoming major contributors. This paper presents a carbon-aware holistic methodology for designing and managing sustainable Edge Data Centers (EDCs), based on three design principles that challenge the state-of-the-art optimization paradigms. Our approach employs vertical integration across the architecture, system, and runtime layers, …

[2025-06-12 Thu (UTC), 11 new articles found for stat.ME Methodology]
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[2025-06-11 Wed (UTC), 6 new articles found for stat.ME Methodology]
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[2025-06-10 Tue (UTC), 18 new articles found for stat.ME Methodology]
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[2025-06-09 Mon (UTC), 9 new articles found for stat.ME Methodology]
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[2025-06-06 Fri (UTC), 11 new articles found for stat.ME Methodology]
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[2025-06-04 Wed (UTC), 15 new articles found for stat.ME Methodology]
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[2025-06-03 Tue (UTC), 24 new articles found for stat.ME Methodology]
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Uncovering Bias Mechanisms in Observational Studies
Ilker Demirel, Zeshan Hussain, Piersilvio De Bartolomeis, David Sontag
https://arxiv.org/abs/2506.01191

Uncovering Bias Mechanisms in Observational Studies
Observational studies are a key resource for causal inference but are often affected by systematic biases. Prior work has focused mainly on detecting these biases, via sensitivity analyses and comparisons with randomized controlled trials, or mitigating them through debiasing techniques. However, there remains a lack of methodology for uncovering the underlying mechanisms driving these biases, e.g., whether due to hidden confounding or selection of participants. In this work, we show that the r…

This https://arxiv.org/abs/2401.07018 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Least squares for cardinal paired comparisons data
Least square estimators for graphical models for cardinal paired comparison data with and without covariates are rigorously analyzed. Novel, graph--based, necessary and sufficient conditions that guarantee strong consistency, asymptotic normality and the exponential convergence of the estimated ranks are emphasized. A complete theory for models with covariates is laid out. In particular, conditions under which covariates can be safely omitted from the model are provided. The methodology is empl…