Tootfinder

A German Gold-Standard Dataset for Sentiment Analysis in Software Engineering
Martin Obaidi, Marc Herrmann, Elisa Schmid, Raymond Ochsner, Kurt Schneider, Jil Kl\"under
https://arxiv.org/abs/2507.07325

A German Gold-Standard Dataset for Sentiment Analysis in Software Engineering
Sentiment analysis is an essential technique for investigating the emotional climate within developer teams, contributing to both team productivity and project success. Existing sentiment analysis tools in software engineering primarily rely on English or non-German gold-standard datasets. To address this gap, our work introduces a German dataset of 5,949 unique developer statements, extracted from the German developer forum Android-Hilfe.de. Each statement was annotated with one of six basic e…

Ground Reaction Force Estimation via Time-aware Knowledge Distillation
Eun Som Jeon, Sinjini Mitra, Jisoo Lee, Omik M. Save, Ankita Shukla, Hyunglae Lee, Pavan Turaga
https://arxiv.org/abs/2506.10265

Ground Reaction Force Estimation via Time-aware Knowledge Distillation
Human gait analysis with wearable sensors has been widely used in various applications, such as daily life healthcare, rehabilitation, physical therapy, and clinical diagnostics and monitoring. In particular, ground reaction force (GRF) provides critical information about how the body interacts with the ground during locomotion. Although instrumented treadmills have been widely used as the gold standard for measuring GRF during walking, their lack of portability and high cost make them impracti…

On the Performance of Cloud-based ARM SVE for Zero-Knowledge Proving Systems
Dumitrel Loghin, Shuang Liang, Shengwei Liu, Xiong Liu, Pingcheng Ruan, Zhigang Ye
https://arxiv.org/abs/2506.09505

On the Performance of Cloud-based ARM SVE for Zero-Knowledge Proving Systems
Zero-knowledge proofs (ZKP) are becoming a gold standard in scaling blockchains and bringing Web3 to life. At the same time, ZKP for transactions running on the Ethereum Virtual Machine require powerful servers with hundreds of CPU cores. The current zkProver implementation from Polygon is optimized for x86-64 CPUs by vectorizing key operations, such as Merkle tree building with Poseidon hashes over the Goldilocks field, with Advanced Vector Extensions (AVX and AVX512). With these optimizations…

Why Trump’s push for
‘gold-standard science’
has researchers alarmed
A new term keeps popping up in messages from Trump administration scientific agencies
— a pledge to restore “gold-standard science.”

Many scientists say the opposite is happening.

The administration’s
“MAHA Report,”
intended to diagnose the root cause of poor health in American children,
was written by Cabinet officials and political appointees,
most of whom lack…

Why Trump’s push for ‘gold-standard science’ has researchers alarmed
Many scientists fear the Trump administration’s new standard means putting political appointees in charge, which could undercut independent research.

Revolutionizing Clinical Trials: A Manifesto for AI-Driven Transformation
Mihaela van der Schaar, Richard Peck, Eoin McKinney, Jim Weatherall, Stuart Bailey, Justine Rochon, Chris Anagnostopoulos, Pierre Marquet, Anthony Wood, Nicky Best, Harry Amad, Julianna Piskorz, Krzysztof Kacprzyk, Rafik Salama, Christina Gunther, Francesca Frau, Antoine Pugeat, Ramon Hernandez

Revolutionizing Clinical Trials: A Manifesto for AI-Driven Transformation
This manifesto represents a collaborative vision forged by leaders in pharmaceuticals, consulting firms, clinical research, and AI. It outlines a roadmap for two AI technologies - causal inference and digital twins - to transform clinical trials, delivering faster, safer, and more personalized outcomes for patients. By focusing on actionable integration within existing regulatory frameworks, we propose a way forward to revolutionize clinical research and redefine the gold standard for clinical …

MAMBO: High-Resolution Generative Approach for Mammography Images
Milica \v{S}kipina, Nikola Jovi\v{s}i\'c, Nicola Dall'Asen, Vanja \v{S}venda, Anil Osman Tur, Slobodan Ili\'c, Elisa Ricci, Dubravko \'Culibrk
https://arxiv.org/abs/2506.08677

MAMBO: High-Resolution Generative Approach for Mammography Images
Mammography is the gold standard for the detection and diagnosis of breast cancer. This procedure can be significantly enhanced with Artificial Intelligence (AI)-based software, which assists radiologists in identifying abnormalities. However, training AI systems requires large and diverse datasets, which are often difficult to obtain due to privacy and ethical constraints. To address this issue, the paper introduces MAMmography ensemBle mOdel (MAMBO), a novel patch-based diffusion approach des…

#GiftLink
The White House Gutted Science Funding. Now It Wants to ‘Correct’ Research. - The New York Times
https://www.nytimes.com/2025/06/03/climate/executive-order-gold-standard-science.html?unlocked_article_code=1.ME8.0Uio.9uHcPhd4AThq&smid=url-share

Properties of UTxO Ledgers and Programs Implemented on Them
Polina Vinogradova (Input Output Global), Alexey Sorokin (Input Output Global)
https://arxiv.org/abs/2506.05832

Properties of UTxO Ledgers and Programs Implemented on Them
Trace-based properties are the gold standard for program behaviour analysis. One of the domains of application of this type of analysis is cryptocurrency ledgers, both for the purpose of analyzing the behaviour of the ledger itself, and any user-defined programs called by it, known as smart contracts. The (extended) UTxO ledger model is a kind of ledger model where all smart contract code is stateless, and additional work must be done to model stateful programs. We formalize the application of …

System-Aware Unlearning Algorithms: Use Lesser, Forget Faster
Linda Lu, Ayush Sekhari, Karthik Sridharan
https://arxiv.org/abs/2506.06073 https://

System-Aware Unlearning Algorithms: Use Lesser, Forget Faster
Machine unlearning addresses the problem of updating a machine learning model/system trained on a dataset $S$ so that the influence of a set of deletion requests $U \subseteq S$ on the unlearned model is minimized. The gold standard definition of unlearning demands that the updated model, after deletion, be nearly identical to the model obtained by retraining. This definition is designed for a worst-case attacker (one who can recover not only the unlearned model but also the remaining data samp…

Nonparametric estimation of an optimal treatment rule with fused randomized trials and missing effect modifiers
Nicholas Williams, Kara Rudolph, Iv\'an D\'iaz
https://arxiv.org/abs/2506.10863

Nonparametric estimation of an optimal treatment rule with fused randomized trials and missing effect modifiers
A fundamental principle of clinical medicine is that a treatment should only be administered to those patients who would benefit from it. Treatment strategies that assign treatment to patients as a function of their individual characteristics are known as dynamic treatment rules. The dynamic treatment rule that optimizes the outcome in the population is called the optimal dynamic treatment rule. Randomized clinical trials are considered the gold standard for estimating the marginal causal effec…

This https://arxiv.org/abs/2309.02073 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Debiased regression adjustment in completely randomized experiments with moderately high-dimensional covariates
Completely randomized experiment is the gold standard for causal inference. When the covariate information for each experimental candidate is available, one typical way is to include them in covariate adjustments for more accurate treatment effect estimation. In this paper, we investigate this problem under the randomization-based framework, i.e., that the covariates and potential outcomes of all experimental candidates are assumed as deterministic quantities and the randomness comes solely fro…

Comparing effective temperatures in standard and Tsallis distributions from transverse momentum spectra in small collision systems
Peng-Cheng Zhang, Pei-Pin Yang, Ting-Ting Duan, Hailong Zhu, Fu-Hu Liu, Khusniddin K. Olimov
https://arxiv.org/abs/2506.00339

Comparing effective temperatures in standard and Tsallis distributions from transverse momentum spectra in small collision systems
The transverse momentum ($p_T$) spectra of identified light charged hadrons, specifically the bosons $π^{\pm}$ and $K^{\pm}$ as well as the fermions $p(\bar p)$, produced in small collision systems, namely deuteron-gold (d+Au) and proton-proton (p+p) collisions at the top energy of the Relativistic Heavy Ion Collider (RHIC) with a center-of-mass energy of $\sqrt{s_{NN}}=200$ GeV, are investigated in this paper. In present study, d+Au collisions are categorized into three centrality classes: ce…

A generation of scientific talent is at the brink of being lost to overseas competitors by the Trump administration’s dismantling of the National Science Foundation (NSF), with unprecedented political interference at the agency jeopardizing the future of US industries and economic growth, according to a Guardian investigation.
The gold standard peer-reviewed process used by the NSF to support cutting-edge, high-impact science is being undermined by the chaotic cuts to staff, programs a…

I think America was greater when the CDC was the global gold standard and not a pathetic political tool.

I wonder where Ireland’s regulator got this idea?
https://mastodon.neilzone.co.uk/@neil/114751272606146547

This https://arxiv.org/abs/2303.16750 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

A Gold Standard Dataset for the Reviewer Assignment Problem
Many peer-review venues are using algorithms to assign submissions to reviewers. The crux of such automated approaches is the notion of the "similarity score" -- a numerical estimate of the expertise of a reviewer in reviewing a paper -- and many algorithms have been proposed to compute these scores. However, these algorithms have not been subjected to a principled comparison, making it difficult for stakeholders to choose the algorithm in an evidence-based manner. The key challenge in comparin…

This https://arxiv.org/abs/2403.17285 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments
A/B testing has become the gold standard for policy evaluation in modern technological industries. Motivated by the widespread use of switchback experiments in A/B testing, this paper conducts a comprehensive comparative analysis of various switchback designs in Markovian environments. Unlike many existing works which derive the optimal design based on specific and relatively simple estimators, our analysis covers a range of state-of-the-art estimators developed in the reinforcement learning (R…

Quantum Assisted Ghost Gutzwiller Ansatz
P. V. Sriluckshmy, Fran\c{c}ois Jamet, Fedor \v{S}imkovic IV
https://arxiv.org/abs/2506.21431 https://

Quantum Assisted Ghost Gutzwiller Ansatz
The ghost Gutzwiller ansatz (gGut) embedding technique was shown to achieve comparable accuracy to the gold standard dynamical mean-field theory method in simulating real material properties, yet at a much lower computational cost. Despite that, gGut is limited by the algorithmic bottleneck of computing the density matrix of the underlying effective embedding model, a quantity which must be converged within a self-consistent embedding loop. We develop a hybrid quantum-classical gGut technique w…

This https://arxiv.org/abs/2411.11190 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

DeepSPV: A Deep Learning Pipeline for 3D Spleen Volume Estimation from 2D Ultrasound Images
Splenomegaly, the enlargement of the spleen, is an important clinical indicator for various associated medical conditions, such as sickle cell disease (SCD). Spleen length measured from 2D ultrasound is the most widely used metric for characterising spleen size. However, it is still considered a surrogate measure, and spleen volume remains the gold standard for assessing spleen size. Accurate spleen volume measurement typically requires 3D imaging modalities, such as computed tomography or magn…

Self-Supervised Speech Quality Assessment (S3QA): Leveraging Speech Foundation Models for a Scalable Speech Quality Metric
Mattson Ogg, Caitlyn Bishop, Han Yi, Sarah Robinson
https://arxiv.org/abs/2506.01655

Self-Supervised Speech Quality Assessment (S3QA): Leveraging Speech Foundation Models for a Scalable Speech Quality Metric
Methods for automatically assessing speech quality are critical for many human language technologies. Behavioral ratings provided by human raters (e.g., mean opinion scores; MOS) are considered the gold standard, but they are susceptible to variability between individual raters, cannot easily be generalized across corpora, and are labor-intensive to collect, thus limiting the acoustic challenges they can quantify. Here, we present a new, scalable method for automatically assessing speech qualit…

Transporting results from a trial to an external target population when trial participation impacts adherence
Rachael K. Ross, Ivan Diaz, Amy J. Pitts, Elizabeth A. Stuart, Kara E. Rudolph
https://arxiv.org/abs/2506.00157

Transporting results from a trial to an external target population when trial participation impacts adherence
Randomized clinical trials are considered the gold standard for informing treatment guidelines, but results may not generalize to real-world populations. Generalizability is hindered by distributional differences in baseline covariates and treatment-outcome mediators. Approaches to address differences in covariates are well established, but approaches to address differences in mediators are more limited. Here we consider the setting where trial activities that differ from usual care settings (e…

Photon Absorption Remote Sensing (PARS): Comprehensive Absorption Imaging Enabling Label-Free Biomolecule Characterization and Mapping
Benjamin R. Ecclestone, James A. Tummon Simmons, James E. D. Tweel, Deepak Dinakaran, Parsin Haji Reza
https://arxiv.org/abs/2506.20069

Photon Absorption Remote Sensing (PARS): Comprehensive Absorption Imaging Enabling Label-Free Biomolecule Characterization and Mapping
Label-free optical absorption microscopy techniques continue to evolve as promising tools for label-free histopathological imaging of cells and tissues. However, critical challenges relating to specificity and contrast, as compared to current gold-standard methods continue to hamper adoption. This work introduces Photon Absorption Remote Sensing (PARS), a new absorption microscope modality, which simultaneously captures the dominant de-excitation processes following an absorption event. In PARS…

Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG
Alexander Selivanov, Philip M\"uller, \"Ozg\"un Turgut, Nil Stolt-Ans\'o, Daniel R\"uckert
https://arxiv.org/abs/2506.20683

Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG
An electrocardiogram (ECG) is a widely used, cost-effective tool for detecting electrical abnormalities in the heart. However, it cannot directly measure functional parameters, such as ventricular volumes and ejection fraction, which are crucial for assessing cardiac function. Cardiac magnetic resonance (CMR) is the gold standard for these measurements, providing detailed structural and functional insights, but is expensive and less accessible. To bridge this gap, we propose PTACL (Patient and …

Staining normalization in histopathology: Method benchmarking using multicenter dataset
Umair Khan, Jouni H\"ark\"onen, Marjukka Friman, Leena Latonen, Teijo Kuopio, Pekka Ruusuvuori
https://arxiv.org/abs/2506.19106

Staining normalization in histopathology: Method benchmarking using multicenter dataset
Hematoxylin and Eosin (H&E) has been the gold standard in tissue analysis for decades, however, tissue specimens stained in different laboratories vary, often significantly, in appearance. This variation poses a challenge for both pathologists' and AI-based downstream analysis. Minimizing stain variation computationally is an active area of research. To further investigate this problem, we collected a unique multi-center tissue image dataset, wherein tissue samples from colon, kidney, and skin …

Replaced article(s) found for q-bio.OT. https://arxiv.org/list/q-bio.OT/new
[1/1]:
English dictionaries, gold and silver standard corpora for biomedical natural language processing...

Robust and efficient multiple-unit switchback experimentation
Paul Missault, Lorenzo Masoero, Christian Delb\'e, Thomas Richardson, Guido Imbens
https://arxiv.org/abs/2506.12654

Robust and efficient multiple-unit switchback experimentation
User-randomized A/B testing has emerged as the gold standard for online experimentation. However, when this kind of approach is not feasible due to legal, ethical or practical considerations, experimenters have to consider alternatives like item-randomization. Item-randomization is often met with skepticism due to its poor empirical performance. To fill this gap, in this paper we introduce a novel and rich class of experimental designs, "Regular Balanced Switchback Designs" (RBSDs). At their co…

Angio-Diff: Learning a Self-Supervised Adversarial Diffusion Model for Angiographic Geometry Generation
Zhifeng Wang, Renjiao Yi, Xin Wen, Chenyang Zhu, Kai Xu, Kunlun He
https://arxiv.org/abs/2506.19455

Angio-Diff: Learning a Self-Supervised Adversarial Diffusion Model for Angiographic Geometry Generation
Vascular diseases pose a significant threat to human health, with X-ray angiography established as the gold standard for diagnosis, allowing for detailed observation of blood vessels. However, angiographic X-rays expose personnel and patients to higher radiation levels than non-angiographic X-rays, which are unwanted. Thus, modality translation from non-angiographic to angiographic X-rays is desirable. Data-driven deep approaches are hindered by the lack of paired large-scale X-ray angiography …

Adaptive Supergeo Design: A Scalable Framework for Geographic Marketing Experiments
Charles Shaw
https://arxiv.org/abs/2506.20499 https://

Adaptive Supergeo Design: A Scalable Framework for Geographic Marketing Experiments
Geographic experiments are a gold-standard for measuring incremental return on ad spend (iROAS) at scale, yet their design is challenging: the unit count is small, heterogeneity is large, and the optimal Supergeo partitioning problem is NP-hard. We introduce Adaptive Supergeo Design (ASD), a two-stage framework that renders Supergeo designs practical for thousands of markets. A bespoke graph-neural network first learns geo-embeddings and proposes a concise candidate set of 'supergeos'; a CP-SAT…