Tootfinder

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics
Yi Yang, Kei Ikemura, Qingwen Zhang, Xiaomeng Zhu, Ci Li, Nazre Batool, Sina Sharif Mansouri, John Folkesson
https://arxiv.org/abs/2508.13979

AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics
Recent multi-task learning studies suggest that linear scalarization, when using well-chosen fixed task weights, can achieve comparable to or even better performance than complex multi-task optimization (MTO) methods. It remains unclear why certain weights yield optimal performance and how to determine these weights without relying on exhaustive hyperparameter search. This paper establishes a direct connection between linear scalarization and MTO methods, revealing through extensive experiments…

Complete harmonic metrics and subharmonic functions on the unit disc
Natsuo Miyatake
https://arxiv.org/abs/2508.12848 https://arxiv.org/pdf/2508.12848

Complete harmonic metrics and subharmonic functions on the unit disc
Let $X$ be a Riemann surface, $K_X \rightarrow X$ the canonical bundle, and $T_X\rightarrow X$ the dual bundle of the canonical bundle. For each integer $r \geq 2$, each $q \in H^0(K_X^r)$, and each choice of the square root $K_X^{1/2}$ of the canonical bundle, we obtain a Higgs bundle $(\mathbb{K}_r,Φ(q))$, which is called a cyclic Higgs bundle. A diagonal harmonic metric $h = (h_1, \dots, h_r)$ on a cyclic Higgs bundle yields $r-1$-Hermitian metrics $H_1, \dots, H_{r-1}$ on $T_X$, while $h_1…

Data-driven quantification and visualization of resilience metrics of power distribution system
Dingwei Wang, Salish Maharjan, Junyuan Zheng, Liming Liu, Zhaoyu Wang
https://arxiv.org/abs/2508.12408

Data-driven quantification and visualization of resilience metrics of power distribution system
This paper presents a data-driven approach for quantifying the resilience of distribution power grids to extreme weather events using two key metrics: (a) the number of outages and (b) restoration time. The method leverages historical outage records maintained by power utilities and weather measurements collected by the National Oceanic and Atmospheric Administration (NOAA) to evaluate resilience across a utility's service territory. The proposed framework consists of three stages. First, outag…

Logarithmic resilience risk metrics that address the huge variations in blackout cost
Arslan Ahmad, Ian Dobson
https://arxiv.org/abs/2505.12016 https://

Logarithmic resilience risk metrics that address the huge variations in blackout cost
Resilience risk metrics must address the customer cost of the largest blackouts of greatest impact. However, there are huge variations in blackout cost in observed distribution utility data that make it impractical to properly estimate the mean large blackout cost and the corresponding risk. These problems are caused by the heavy tail observed in the distribution of customer costs. To solve these problems, we propose resilience metrics that describe large blackout risk using the mean of the log…

Is there a VSCode extension that can capture OpenTelemetry metrics and show them?

Segre forms of singular metrics on vector bundles and Lelong numbers
Mats Andersson, Richard L\"ark\"ang
https://arxiv.org/abs/2506.15473 https:/…

Segre forms of singular metrics on vector bundles and Lelong numbers
Let $E\to X$ be a holomorphic vector bundle with a singular Griffiths negative Hermitian metric $h$ with analytic singularities. One can define, given a smooth reference metric $h_0$, a current $s(E,h,h_0)$ that is called the associated Segre form, which defines the expected Bott-Chern class and coincides with the usual Segre form of $h$ where it is smooth. We prove that $s(E,h,h_0)$ is the limit of the Segre forms of a sequence of smooth metrics if the metric is smooth outside the unbounded lo…

Like all university rankings, the QS World University Rankings are of questionable value. “In partnership with Elsevier” says it all, really…
But of course, they can be a marketing tool, and the metrics based on perceptions also tell a story. This also goes for their Sustainability rankings. So, while they don’t necessarily tell us about the actual “sustainability" of a university, they certainly tell us about how well they communicate about their efforts.
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Like all university rankings, the QS World University Rankings are of questionable value. “In partnership with Elsevier” says it all, really…
But of course, they can be a marketing tool, and the metrics based on perceptions also tell a story. This also goes for their Sustainability rankings. So, while they don’t necessarily tell us about the actual “sustainability" of a university, they certainly tell us about how well they communicate about their efforts.
⇢

Like all university rankings, the QS World University Rankings are of questionable value. “In partnership with Elsevier” says it all, really…
But of course, they can be a marketing tool, and the metrics based on perceptions also tell a story. This also goes for their Sustainability rankings. So, while they don’t necessarily tell us about the actual “sustainability" of a university, they certainly tell us about how well they communicate about their efforts.
⇢

New York-based Talos, which develops digital asset trading infrastructure, says it acquired blockchain data provider Coin Metrics, a source says for over $100M (Leo Schwartz/Fortune)
https://fortune.com/crypto/2025/07/16/talos-crypto…

Exclusive: Crypto infrastructure giant Talos acquires Coin Metrics for more than $100 million
The deal is the latest in a flurry of M&A activity in the crypto sector.

Creative Intelligence is the only book that will give you the foundation, the ability for you to create your own floor. While others scramble to hit AI usage metrics and come up with bland ideas, you'll be building the skills and capabilities that actually matter and set you apart.
https://

I have thought from time to time and with increasing frequency, that it's time to take back Web Content Search and that it should be Open.
That it should be federated; there isn't one owner of the crawler or indexer, fingerprints of crawling indicate when a page was last visited; a Service provides the next randomly distributed domain/sub-domain to crawl; post indexing has validation and verification steps (which have their own Steering and Metrics);relevance should be content,…

Manhattan curves in complex dynamics and asymptotic correlation of multiplier spectra
Fabrizio Bianchi, Yan Mary He
https://arxiv.org/abs/2508.12490 https://

Manhattan curves in complex dynamics and asymptotic correlation of multiplier spectra
The Manhattan curve for a pair of hyperbolic structures (possibly with cusps) on a given surface is a geometric object that encodes the growth rate of lengths of closed geodesics with respect to the two different hyperbolic metrics. It has been extensively studied as a way to understand geodesics on surfaces, the thermodynamic formalism of the geodesic flows and comparison of hyperbolic metrics. Via Sullivan's dictionary, in this paper, we define and study the Manhattan curve for a pair of hy…

Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
Shagun Maheshwari, Janghoon Ock, Adeesh Kolluru, Amir Barati Farimani, John R. Kitchin
https://arxiv.org/abs/2506.14850

Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
Machine-learning force fields (MLFFs) have emerged as a promising solution for speeding up ab initio molecular dynamics (MD) simulations, where accurate force predictions are critical but often computationally expensive. In this work, we employ GemNet-T, a graph neural network model, as an MLFF and investigate two training strategies: (1) direct training on MD17 (10K samples) without pre-training, and (2) pre-training on the large-scale OC20 dataset followed by fine-tuning on MD17 (10K). While …

GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems
Jisoo Lee, Raeyoung Chang, Dongwook Kwon, Harmanpreet Singh, Nikhil Verma
https://arxiv.org/abs/2507.13190

GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems
Multi-agent systems built on language models have shown strong performance on collaborative reasoning tasks. However, existing evaluations focus only on the correctness of the final output, overlooking how inefficient communication and poor coordination contribute to redundant reasoning and higher computational costs. We introduce GEMMAS, a graph-based evaluation framework that analyzes the internal collaboration process by modeling agent interactions as a directed acyclic graph. To capture col…

Shannon entropy for harmonic metrics on cyclic Higgs bundles II
Natsuo Miyatake
https://arxiv.org/abs/2508.12844 https://arxiv.org/pdf/2508.12844

Shannon entropy for harmonic metrics on cyclic Higgs bundles II
Let $X$ be a Riemann surface and $K_X \rightarrow X$ the canonical bundle. For each integer $r \geq 2$, each $q \in H^0(K_X^r)$, and each choice of the square root $K_X^{1/2}$ of the canonical bundle, we obtain a Higgs bundle, which is called a cyclic Higgs bundle. A diagonal harmonic metric $h = (h_1, \dots, h_r)$ on a cyclic Higgs bundle yields $r-1$-Hermitian metrics $H_1, \dots, H_{r-1}$ on $K_X^{-1} \rightarrow X$, while $h_1$, $h_r$, and $q$ yield a degenerate Hermitian metric $H_r$ on $K…

RadarQA: Multi-modal Quality Analysis of Weather Radar Forecasts
Xuming He, Zhiyuan You, Junchao Gong, Couhua Liu, Xiaoyu Yue, Peiqin Zhuang, Wenlong Zhang, Lei Bai
https://arxiv.org/abs/2508.12291

RadarQA: Multi-modal Quality Analysis of Weather Radar Forecasts
Quality analysis of weather forecasts is an essential topic in meteorology. Although traditional score-based evaluation metrics can quantify certain forecast errors, they are still far from meteorological experts in terms of descriptive capability, interpretability, and understanding of dynamic evolution. With the rapid development of Multi-modal Large Language Models (MLLMs), these models become potential tools to overcome the above challenges. In this work, we introduce an MLLM-based weather …

Skew-Induced Insertion Loss Deviation (SILD) and FOM_SILD: Metrics for Quantifying P/N Skew Effects in High-Speed Channels
David Nozadze, Zurab Kiguradze, Amendra Koul, Mike Sapozhnikov
https://arxiv.org/abs/2506.15105

Skew-Induced Insertion Loss Deviation (SILD) and FOM_SILD: Metrics for Quantifying P/N Skew Effects in High-Speed Channels
The rise of AI workloads and growing data center demands have driven the need for ultra-high-speed interconnects exceeding 200 Gb/s. As unit intervals (UI) shrink, even a few picoseconds of P/N skew can degrade serializer-deserializer (SerDes) performance. Traditional methods for quantifying skew fall short in capturing its impact. We introduce two new metrics: 1) Skew-Induced Insertion Loss Deviation (SILD) and 2) its complementary Figure of Merit (FOM_SILD), analytically developed to assess P…

Red Teaming Methodology for Design Obfuscation
Yuntao Liu, Abir Akib, Zelin Lu, Qian Xu, Ankur Srivastava, Gang Qu, David Kehlet, Nij Dorairaj
https://arxiv.org/abs/2508.13965 h…

Red Teaming Methodology for Design Obfuscation
The main goal of design obfuscation schemes is to protect sensitive design details from untrusted parties in the VLSI supply chain, including but not limited to off-shore foundries and untrusted end users. In this work, we provide a systematic red teaming approach to evaluate the security of design obfuscation approaches. Specifically, we propose security metrics and evaluation methodology for the scenarios where the adversary does not have access to a working chip. A case study on the RIPPER t…

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Predicting the Understandability of Computational Notebooks through Code Metrics Analysis
Ghahfarokhi, Asadi, Asgari, Mohammadi, Heydarnoori, Rizi

Vitamin N: Benefits of Different Forms of Public Greenery for Urban Health
Sanja \v{S}\'cepanovi\'c, Sagar Joglekar, Stephen Law, Daniele Quercia, Ke Zhou, Alice Battiston, Rossano Schifanella
https://arxiv.org/abs/2508.12998

Vitamin N: Benefits of Different Forms of Public Greenery for Urban Health
Urban greenery is often linked to better health, yet findings from past research have been inconsistent. One reason is that official greenery metrics measure the amount or nearness of greenery but ignore how often people actually may potentially see or use it in daily life. To address this gap, we introduced a new classification that separates on-road greenery, which people see while walking through streets, from off-road greenery, which requires planned visits. We did so by combining aerial im…

Mario Kart World: completed!
Hah. As if. And yet, I've seen the credits, unlocked the special cup, and have gold trophies displayed for all grands prix and knockout tours. This was done on the medium difficulty. By all my usual metrics, I've completed the game. But Mario Kart's not like that, and Mario Kart World is doubly not like that. The real game in Mario Kart appears at 150cc, the hardest difficulty level, when not only does your kart travel faster (to the extent…

When $B_2$ is Not Enough: Evaluating Simple Metrics for Predicting Phase Separation of Intrinsically Disordered Proteins
Wesley W. Oliver, William M. Jacob, Michael A. Webb
https://arxiv.org/abs/2507.12312

When $B_2$ is Not Enough: Evaluating Simple Metrics for Predicting Phase Separation of Intrinsically Disordered Proteins
Understanding and predicting the phase behavior of intrinsically disordered proteins (IDPs) is of significant interest due to their role in many biological processes. However, effectively characterizing phase behavior and its complex dependence on protein primary sequence remains challenging. In this study, we evaluate the efficacy of several simple computational metrics to quantify the propensity of single-component IDP solutions to phase separate; specific metrics considered include the singl…

Insights Informed Generative AI for Design: Incorporating Real-world Data for Text-to-Image Output
Richa Gupta, Alexander Htet Kyaw
https://arxiv.org/abs/2506.15008

Insights Informed Generative AI for Design: Incorporating Real-world Data for Text-to-Image Output
Generative AI, specifically text-to-image models, have revolutionized interior architectural design by enabling the rapid translation of conceptual ideas into visual representations from simple text prompts. While generative AI can produce visually appealing images they often lack actionable data for designers In this work, we propose a novel pipeline that integrates DALL-E 3 with a materials dataset to enrich AI-generated designs with sustainability metrics and material usage insights. After t…

Simulation-based validation of Bayes factor computation
Martin Modr\'ak, Sebastian Stroppel, Paul-Christian B\"urkner
https://arxiv.org/abs/2508.11814 https://

Simulation-based validation of Bayes factor computation
We propose and evaluate two methods that validate the computation of Bayes factors: one based on an improved variant of simulation-based calibration checking (SBC) and one based on calibration metrics for binary predictions. We show that in theory, binary prediction calibration is equivalent to a special case of SBC, but with finite resources, binary prediction calibration is typically more sensitive. With well-designed test quantities, SBC can however detect all possible problems in computatio…

Towards Automatic Evaluation and High-Quality Pseudo-Parallel Dataset Construction for Audio Editing: A Human-in-the-Loop Method
Yuhang Jia, Hui Wang, Xin Nie, Yujie Guo, Lianru Gao, Yong Qin
https://arxiv.org/abs/2508.11966

Towards Automatic Evaluation and High-Quality Pseudo-Parallel Dataset Construction for Audio Editing: A Human-in-the-Loop Method
Audio editing aims to manipulate audio content based on textual descriptions, supporting tasks such as adding, removing, or replacing audio events. Despite recent progress, the lack of high-quality benchmark datasets and comprehensive evaluation metrics remains a major challenge for both assessing audio editing quality and improving the task itself. In this work, we propose a novel approach for audio editing task by incorporating expert knowledge into both the evaluation and dataset constructio…

Periodic equatorial orbits in a black bounce scenario
Anderson Bragado, Gonzalo J. Olmo
https://arxiv.org/abs/2506.15600 https://arxi…

Periodic equatorial orbits in a black bounce scenario
We study equatorial closed orbits in a popular black bounce model to see if the internal structure of these objects could lead to peculiar observable features. Paralleling the analysis of the Schwarzschild and Kerr metrics, we show that in the black bounce case each orbit can also be associated with a triplet of integers which can then be used to construct a rational number characterizing each periodic orbit. When the black bounce solution represents a traversable wormhole, we show that the pre…

5 #Grafana in #Docker examples to get started with metrics, logs, and traces
https://

Ges-QA: A Multidimensional Quality Assessment Dataset for Audio-to-3D Gesture Generation
Zhilin Gao, Yunhao Li, Sijing Wu, Yuqin Cao, Huiyu Duan, Guangtao Zhai
https://arxiv.org/abs/2508.12020

Ges-QA: A Multidimensional Quality Assessment Dataset for Audio-to-3D Gesture Generation
The Audio-to-3D-Gesture (A2G) task has enormous potential for various applications in virtual reality and computer graphics, etc. However, current evaluation metrics, such as Fréchet Gesture Distance or Beat Constancy, fail at reflecting the human preference of the generated 3D gestures. To cope with this problem, exploring human preference and an objective quality assessment metric for AI-generated 3D human gestures is becoming increasingly significant. In this paper, we introduce the Ges-QA …

Interpolative Metrics Are Not New: A Study of Generalized Contractions in b-Suprametric Spaces
Hassan Khandani
https://arxiv.org/abs/2506.11205 https://

Interpolative Metrics Are Not New: A Study of Generalized Contractions in b-Suprametric Spaces
{Researchers recently introduced interpolative metric spaces and established fixed-point theorems in this setting. We demonstrate that these metrics are a special case of b-metrics. On the other hand, suprametrics and b-suprametrics have also been introduced, and we show that b-suprametric spaces generalize b-metric spaces. We establish corresponding results previously presented in interpolative metric spaces in the framework of b-suprametric spaces with weaker conditions.

Hermitian-Einstein Metrics on Parabolic Bundles over compact complex surfaces
Xilun Li, Gang Tian
https://arxiv.org/abs/2506.15458 https://

Hermitian-Einstein Metrics on Parabolic Bundles over compact complex surfaces
We prove the Kobayashi-Hitchin correspondence for parabolic bundles over compact nonKähler surfaces with simple normal crossing divisor or compact nonKähler manifolds of any dimension with smooth divisor.

GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?
Yifang Tian, Yaming Liu, Zichun Chong, Zihang Huang, Hans-Arno Jacobsen
https://arxiv.org/abs/2508.12472

GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?
Root cause analysis (RCA) in microservice systems is challenging, requiring on-call engineers to rapidly diagnose failures across heterogeneous telemetry such as metrics, logs, and traces. Traditional RCA methods often focus on single modalities or merely rank suspect services, falling short of providing actionable diagnostic insights with remediation guidance. This paper introduces GALA, a novel multi-modal framework that combines statistical causal inference with LLM-driven iterative reasonin…

Approximating Language Model Training Data from Weights
John X. Morris, Junjie Oscar Yin, Woojeong Kim, Vitaly Shmatikov, Alexander M. Rush
https://arxiv.org/abs/2506.15553

Approximating Language Model Training Data from Weights
Modern language models often have open weights but closed training data. We formalize the problem of data approximation from model weights and propose several baselines and metrics. We develop a gradient-based approach that selects the highest-matching data from a large public text corpus and show its effectiveness at recovering useful data given only weights of the original and finetuned models. Even when none of the true training data is known, our method is able to locate a small subset of p…

Radar speed, tight-window throw conversion rate and more stats to know for fantasy football https://www.nytimes.com/athletic/6543008/2025/08/11/fantasy-football-stats-metrics-to-know/

Learning to Predict Mobile Robot Stability in Off-Road Environments
Nathaniel Rose, Arif Ahmed, Emanuel Gutierrez-Cornejo, Parikshit Maini
https://arxiv.org/abs/2507.12731

Learning to Predict Mobile Robot Stability in Off-Road Environments
Navigating in off-road environments for wheeled mobile robots is challenging due to dynamic and rugged terrain. Traditional physics-based stability metrics, such as Static Stability Margin (SSM) or Zero Moment Point (ZMP) require knowledge of contact forces, terrain geometry, and the robot's precise center-of-mass that are difficult to measure accurately in real-world field conditions. In this work, we propose a learning-based approach to estimate robot platform stability directly from proprioc…

Visual metrics on boundaries of hyperbolic spaces
Emily Stark
https://arxiv.org/abs/2506.10108 https://arxiv.org/pdf/2506.10108

Visual metrics on boundaries of hyperbolic spaces
This is an expository article on visual metrics on boundaries of hyperbolic metric spaces. We discuss the construction of visual metrics, quasisymmetries and their invariants, Hausdorff and conformal dimension, and constructions and applications of Gromov's round trees. There is a focus on providing examples throughout. These notes are based on the material of a minicourse given by the author at the 2024 Riverside Workshop in Geometric Group Theory.

An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences
Styliani-Christina Fragkouli, Somya Iqbal, Lisa Crossman, Barbara Gravel, Nagat Masued, Mark Onders, Devesh Haseja, Alex Stikkelman, Alfonso Valencia, Tom Lenaerts, Fotis Psomopoulos, Pilib \'O Broin, N\'uria Queralt-Rosinach, Davide Cirillo
htt…

An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences
Synthetic data has emerged as a powerful resource in life sciences, offering solutions for data scarcity, privacy protection and accessibility constraints. By creating artificial datasets that mirror the characteristics of real data, allows researchers to develop and validate computational methods in controlled environments. Despite its promise, the adoption of synthetic data in Life Sciences hinges on rigorous evaluation metrics designed to assess their fidelity and reliability. To explore the…

Towards Reliable Objective Evaluation Metrics for Generative Singing Voice Separation Models
Paul A. Bereuter, Benjamin Stahl, Mark D. Plumbley, Alois Sontacchi
https://arxiv.org/abs/2507.11427

Towards Reliable Objective Evaluation Metrics for Generative Singing Voice Separation Models
Traditional Blind Source Separation Evaluation (BSS-Eval) metrics were originally designed to evaluate linear audio source separation models based on methods such as time-frequency masking. However, recent generative models may introduce nonlinear relationships between the separated and reference signals, limiting the reliability of these metrics for objective evaluation. To address this issue, we conduct a Degradation Category Rating listening test and analyze correlations between the obtained…

Check out this piece I wrote for TechTarget on the ten metrics or KPIs that security leaders should track and show to the board.
Thanks to Dave Lindner of Contrast Security, Steve Cobb of SecurityScorecard and Bob Maley of Black Kite for their insight.
https://www.

10 key cybersecurity metrics and KPIs your board wants tracked | TechTarget
Learn about the cybersecurity metrics and KPIs that IT security managers should track to monitor security efforts and keep the board of directors informed.

Pathology-Guided Virtual Staining Metric for Evaluation and Training
Qiankai Wang, James E. D. Tweel, Parsin Haji Reza, Anita Layton
https://arxiv.org/abs/2507.12624

Pathology-Guided Virtual Staining Metric for Evaluation and Training
Virtual staining has emerged as a powerful alternative to traditional histopathological staining techniques, enabling rapid, reagent-free image transformations. However, existing evaluation methods predominantly rely on full-reference image quality assessment (FR-IQA) metrics such as structural similarity, which are originally designed for natural images and often fail to capture pathology-relevant features. Expert pathology reviews have also been used, but they are inherently subjective and ti…

Developing a Responsible AI Framework for Healthcare in Low Resource Countries: A Case Study in Nepal and Ghana
Hari Krishna Neupane, Bhupesh Kumar Mishra
https://arxiv.org/abs/2508.12389

Developing a Responsible AI Framework for Healthcare in Low Resource Countries: A Case Study in Nepal and Ghana
The integration of Artificial Intelligence (AI) into healthcare systems in low-resource settings, such as Nepal and Ghana, presents transformative opportunities to improve personalized patient care, optimize resources, and address medical professional shortages. This paper presents a survey-based evaluation and insights from Nepal and Ghana, highlighting major obstacles such as data privacy, reliability, and trust issues. Quantitative and qualitative field studies reveal critical metrics, inclu…

Metrics on Permutation Families Defined by a Restriction Graph
Danylo Tymoshenko, Leonhard Nagel
https://arxiv.org/abs/2507.10569 https://

Metrics on Permutation Families Defined by a Restriction Graph
Understanding the metric structure of permutation families is fundamental to combinatorics and has applications in social choice theory, bioinformatics, and coding theory. We study permutation families defined by restriction graphs--oriented graphs that constrain the relative order of elements in valid permutations. For any restriction graph $G$, we determine the maximum distance achievable by two permutations under the $\ell_\infty$-metric and provide an explicit algorithm that constructs opti…

Closed $\mathrm{G}_2$ structures on compact manifolds satisfying the known topological obstructions to holonomy $\mathrm{G}_2$ metrics
Luc\'ia Mart\'in-Merch\'an
https://arxiv.org/abs/2508.12658

Closed $\mathrm{G}_2$ structures on compact manifolds satisfying the known topological obstructions to holonomy $\mathrm{G}_2$ metrics
We construct new compact manifolds endowed with closed $\mathrm{G}_2$ structures that satisfy the topological properties found by Joyce and Baraglia for the existence of a torsion-free $\mathrm{G}_2$ structure in the same cohomology class. Those manifolds arise as resolutions of orbifolds of the form $M/\mathbb{Z}_2$, where $M$ itself does not admit any torsion-free $\mathrm{G}_2$ structure. We develop an equivariant resolution procedure for closed $\mathrm{G}_2$ orbifolds with $\mathbb{Z}_2$ i…

A Review of Privacy Metrics for Privacy-Preserving Synthetic Data Generation
Frederik Marinus Trudslev, Matteo Lissandrini, Juan Manuel Rodriguez, Martin B{\o}gsted, Daniele Dell'Aglio
https://arxiv.org/abs/2507.11324

A Review of Privacy Metrics for Privacy-Preserving Synthetic Data Generation
Privacy Preserving Synthetic Data Generation (PP-SDG) has emerged to produce synthetic datasets from personal data while maintaining privacy and utility. Differential privacy (DP) is the property of a PP-SDG mechanism that establishes how protected individuals are when sharing their sensitive data. It is however difficult to interpret the privacy loss ($\varepsilon$) expressed by DP. To make the actual risk associated with the privacy loss more transparent, multiple privacy metrics (PMs) have b…

Metrics for Assessing Changes in Flow-based Networks
Micha{\l} Rzepka, Piotr Cho{\l}da
https://arxiv.org/abs/2508.09573 https://arxiv.org/pdf/2508.09573

Metrics for Assessing Changes in Flow-based Networks
This paper addresses the challenges of evaluating network performance in the presence of fluctuating traffic patterns, with a particular focus on the impact of peak data rates on network resources. We introduce a set of metrics to quantify network load and measure the impact of individual flows on the overall network state. By analyzing link and flow data through percentile values and sample distributions, and introducing the Utilization Score metric, the research provides insights into resourc…

LLM-Driven Data Generation and a Novel Soft Metric for Evaluating Text-to-SQL in Aviation MRO
Patrick Sutanto, Jonathan Kenrick, Max Lorenz, Joan Santoso
https://arxiv.org/abs/2506.13785

LLM-Driven Data Generation and a Novel Soft Metric for Evaluating Text-to-SQL in Aviation MRO
The application of Large Language Models (LLMs) to text-to-SQL tasks promises to democratize data access, particularly in critical industries like aviation Maintenance, Repair, and Operation (MRO). However, progress is hindered by two key challenges: the rigidity of conventional evaluation metrics such as execution accuracy, which offer coarse, binary feedback, and the scarcity of domain-specific evaluation datasets. This paper addresses these gaps. To enable more nuanced assessment, we introdu…

Mallows Model with Learned Distance Metrics: Sampling and Maximum Likelihood Estimation
Yeganeh Alimohammadi, Kiana Asgari
https://arxiv.org/abs/2507.08108

Mallows Model with Learned Distance Metrics: Sampling and Maximum Likelihood Estimation
\textit{Mallows model} is a widely-used probabilistic framework for learning from ranking data, with applications ranging from recommendation systems and voting to aligning language models with human preferences~\cite{chen2024mallows, kleinberg2021algorithmic, rafailov2024direct}. Under this model, observed rankings are noisy perturbations of a central ranking $σ$, with likelihood decaying exponentially in distance from $σ$, i.e, $P (π) \propto \exp\big(-β\cdot d(π, σ)\big),$ where $β> 0…

Sources: Anthropic's financial performance has prompted some investors to indicate interest in funding the company at a valuation of $100B if it pursues a deal (The Information)
https://www.theinformation.com/articles/investors-float-deal-valui…

Investors Float Deal Valuing Anthropic at More Than $100 Billion
As Anthropic’s sale of artificial intelligence has surged, the company has told investors some of its profit-related metrics are improving even as it burns a tremendous amount of cash. That financial performance has prompted some investors to indicate interest in funding Anthropic at a valuation ...

Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation
David Heineman, Valentin Hofmann, Ian Magnusson, Yuling Gu, Noah A. Smith, Hannaneh Hajishirzi, Kyle Lo, Jesse Dodge
https://arxiv.org/abs/2508.13144

Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation
Developing large language models is expensive and involves making decisions with small experiments, typically by evaluating on large, multi-task evaluation suites. In this work, we analyze specific properties which make a benchmark more reliable for such decisions, and interventions to design higher-quality evaluation benchmarks. We introduce two key metrics that show differences in current benchmarks: signal, a benchmark's ability to separate better models from worse models, and noise, a bench…

Replaced article(s) found for cs.DL. https://arxiv.org/list/cs.DL/new
[1/1]:
- A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challe...
Andrew Brown, Muhammad Roman, Barry Devereux

Error Analysis of Truncation Legendre Method for Solving Numerical Differentiation
Maksym Kyselov
https://arxiv.org/abs/2506.11529 https://

Error Analysis of Truncation Legendre Method for Solving Numerical Differentiation
We study the problem of numerical differentiation of functions from weighted Wiener classes. We construct and analyze a truncation Legendre method to recover arbitrary order derivatives. The main focus is on obtaining error estimates in integral and uniform metrics. Unlike previous studies, which predominantly focused on first-order derivatives and specific functional spaces, we conduct a comprehensive analysis across a wide spectrum of function regularity parameters and various metrics for mea…

Identification of Authoritative Nodes and Dismantling of Illicit Networks Using a Novel Metric for Measuring Strength of a Graph
Kartikeya Kansal, Arunabha Sen
https://arxiv.org/abs/2507.12711

Identification of Authoritative Nodes and Dismantling of Illicit Networks Using a Novel Metric for Measuring Strength of a Graph
Dismantling criminal networks or containing epidemics or misinformation through node removal is a well-studied problem. To evaluate the effectiveness of such efforts, one must measure the strength of the network before and after node removal. Process P1 is considered more effective than P2 if the strength of the residual network after removing k nodes via P1 is smaller than that from P2. This leads to the central question: How should network strength be measured? Existing metrics rely solely …

Breaking the Top-$K$ Barrier: Advancing Top-$K$ Ranking Metrics Optimization in Recommender Systems
Weiqin Yang, Jiawei Chen, Shengjia Zhang, Peng Wu, Yuegang Sun, Yan Feng, Chun Chen, Can Wang
https://arxiv.org/abs/2508.05673

Breaking the Top-$K$ Barrier: Advancing Top-$K$ Ranking Metrics Optimization in Recommender Systems
In the realm of recommender systems (RS), Top-$K$ ranking metrics such as NDCG@$K$ are the gold standard for evaluating recommendation performance. However, during the training of recommendation models, optimizing NDCG@$K$ poses significant challenges due to its inherent discontinuous nature and the intricate Top-$K$ truncation. Recent efforts to optimize NDCG@$K$ have either overlooked the Top-$K$ truncation or suffered from high computational costs and training instability. To overcome these …

Dataset-Adaptive Dimensionality Reduction
Hyeon Jeon, Jeongin Park, Soohyun Lee, Dae Hyun Kim, Sungbok Shin, Jinwook Seo
https://arxiv.org/abs/2507.11984 h…

Dataset-Adaptive Dimensionality Reduction
Selecting the appropriate dimensionality reduction (DR) technique and determining its optimal hyperparameter settings that maximize the accuracy of the output projections typically involves extensive trial and error, often resulting in unnecessary computational overhead. To address this challenge, we propose a dataset-adaptive approach to DR optimization guided by structural complexity metrics. These metrics quantify the intrinsic complexity of a dataset, predicting whether higher-dimensional s…

Interesting numbers about US top websites.
https://pressgazette.co.uk/media-audience-and-business-data/media_metrics/most-popular-websites-news-us-monthly-3/

Top 50 news websites in the US: AP shrugs off Trump row with huge traffic boost
Press Gazette lists the top 50 biggest websites for news in the US. Monthly updated top 50 listing based on data from Similarweb.

Deep learning inference with the Event Horizon Telescope I. Calibration improvements and a comprehensive synthetic data library
M. Janssen, C. -k. Chan, J. Davelaar, I. Natarajan, H. Olivares, B. Ripperda, J. R\"oder, M. Rynge, M. Wielgus
https://arxiv.org/abs/2506.13873

Deep learning inference with the Event Horizon Telescope I. Calibration improvements and a comprehensive synthetic data library
(abridged) In a series of publications, we describe a comprehensive comparison of Event Horizon Telescope (EHT) data with theoretical models of Sgr A* and M87*. Here, we report on improvements made to our observational data reduction pipeline and present the generation of observables derived from the EHT models. We make use of ray-traced GRMHD simulations that are based on different black hole spacetime metrics and accretion physics parameters. These broad classes of models provide a good repre…

Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery
Hanxue Gu, Yaqian Chen, isoo Lee, Diego Schaps, Regina Woody, Roy Colglazier, Maciej A. Mazurowski, Christopher Mantyh
https://arxiv.org/abs/2506.11996

Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery
Objective: To evaluate whether preoperative body composition metrics automatically extracted from CT scans can predict postoperative outcomes after colectomy, either alone or combined with clinical variables or existing risk predictors. Main outcomes and measures: The primary outcome was the predictive performance for 1-year all-cause mortality following colectomy. A Cox proportional hazards model with 1-year follow-up was used, and performance was evaluated using the concordance index (C-index…

Analysis: Mail Online is among major news brands most-impacted by Google's AI Overviews, with 68.8% of the top 100 keyword searches resulting in no site visits (Charlotte Tobitt/Press Gazette)
https://press…

How Google AI Overviews is fuelling zero-click searches for top publishers
New Similarweb data shows likes of Mail Online and People see high proportion of AI Overviews for their key search terms.

Balancing Preservation and Modification: A Region and Semantic Aware Metric for Instruction-Based Image Editing
Zhuoying Li, Zhu Xu, Yuxin Peng, Yang Liu
https://arxiv.org/abs/2506.13827

Balancing Preservation and Modification: A Region and Semantic Aware Metric for Instruction-Based Image Editing
Instruction-based image editing, which aims to modify the image faithfully according to the instruction while preserving irrelevant content unchanged, has made significant progress. However, there still lacks a comprehensive metric for assessing the editing quality. Existing metrics either require high human evaluation costs, which hinder large-scale evaluation, or are adapted from other tasks and lose task-specific concerns, failing to comprehensively evaluate both instruction-based modificati…

Energy Aware Development of Neuromorphic Implantables: From Metrics to Action
Enrique Barba Roque, Luis Cruz
https://arxiv.org/abs/2506.09599 https://

Energy Aware Development of Neuromorphic Implantables: From Metrics to Action
Spiking Neural Networks (SNNs) and neuromorphic computing present a promising alternative to traditional Artificial Neural Networks (ANNs) by significantly improving energy efficiency, particularly in edge and implantable devices. However, assessing the energy performance of SNN models remains a challenge due to the lack of standardized and actionable metrics and the difficulty of measuring energy consumption in experimental neuromorphic hardware. In this paper, we conduct a preliminary explora…

Environmentally-Conscious Cloud Orchestration Considering Geo-Distributed Data Centers
Giulio Attenni, Novella Bartolini
https://arxiv.org/abs/2507.11563 h…

Environmentally-Conscious Cloud Orchestration Considering Geo-Distributed Data Centers
This paper presents a theoretical discussion for environmentally-conscious job deployment and migration in cloud environments, aiming to minimize the environmental impact of resource provisioning while incorporating sustainability requirements. As the demand for sustainable cloud services grows, it is crucial for cloud customers to select data center operators based on sustainability metrics and to accurately report the ecological footprint of their services. To this end, we analyze sustainabil…

Meta-Metrics and Best Practices for System-Level Inference Performance Benchmarking
Shweta Salaria, Zhuoran Liu, Nelson Mimura Gonzalez
https://arxiv.org/abs/2508.10251 https://…

Meta-Metrics and Best Practices for System-Level Inference Performance Benchmarking
Benchmarking inference performance (speed) of Foundation Models such as Large Language Models (LLM) involves navigating a vast experimental landscape to understand the complex interactions between hardware and software components. However, evaluating every possible test configuration is impractical, unfeasible and unnecessary. To address this challenge, we introduce FMwork, a comprehensive and methodical approach to creating a controlled testing environment that accurately reflects and characte…

Optimal and Suboptimal Decoders under Finite-Alphabet Interference: A Mismatched Decoding Perspective
Sibo Zhang, Bruno Clerckx
https://arxiv.org/abs/2506.12646

Optimal and Suboptimal Decoders under Finite-Alphabet Interference: A Mismatched Decoding Perspective
Interference widely exists in communication systems and is often not optimally treated at the receivers due to limited knowledge and/or computational burden. Evolutions of receivers have been proposed to balance complexity and spectral efficiency, for example, for 6G, while commonly used performance metrics, such as capacity and mutual information, fail to capture the suboptimal treatment of interference, leading to potentially inaccurate performance evaluations. Mismatched decoding is an infor…

Regularity estimates on harmonic eigenmaps with arbitrary number of coordinates
Romain Petrides
https://arxiv.org/abs/2508.10448 https://arxiv.org/pdf/2508…

Regularity estimates on harmonic eigenmaps with arbitrary number of coordinates
We revisit the well-established regularity estimates on harmonic maps on surfaces to question their independence with respect to the dimension of the target manifold. We are mainly interested in harmonic maps into target ellipsoids, that we call Laplace harmonic eigenmaps. These maps are related to critical metrics in the context of eigenvalue optimization. The tools that we gather here are useful to handle convergence of almost critical metrics via Palais-Smale sequences of (almost harmonic) e…

Beyond N-Grams: Rethinking Evaluation Metrics and Strategies for Multilingual Abstractive Summarization
Itai Mondshine, Tzuf Paz-Argaman, Reut Tsarfaty
https://arxiv.org/abs/2507.08342

Beyond N-Grams: Rethinking Evaluation Metrics and Strategies for Multilingual Abstractive Summarization
Automatic n-gram based metrics such as ROUGE are widely used for evaluating generative tasks such as summarization. While these metrics are considered indicative (even if imperfect) of human evaluation for English, their suitability for other languages remains unclear. To address this, we systematically assess evaluation metrics for generation both n-gram-based and neural based to evaluate their effectiveness across languages and tasks. Specifically, we design a large-scale evaluation suite acr…

A class of charged and charged-Taub-NUT metrics in the presence of a massless scalar field and some of their astrophysical aspects
Fatemeh Sadeghi, Behrouz Mirza, Marzieh Moradzadeh
https://arxiv.org/abs/2506.09922

A class of charged and charged-Taub-NUT metrics in the presence of a massless scalar field and some of their astrophysical aspects
We consider a class of exact solutions to the Einstein equations in the presence of a scalar field, recently introduced in [arXiv:2307.09328, arXiv:2307.13588], and derive their generalized form with dyonic charges using Harrison transformations. For specific parameter values, this class of metrics includes the charged Fisher Janis Newman Winicour (FJNW) and gamma metrics. We then investigate the motion of neutral particles in the background of these metrics and derive the corresponding effecti…

Evaluating Robot Program Performance with Power Consumption Driven Metrics in Lightweight Industrial Robots
Juan Heredia, Emil Stubbe Kolvig-Raun, Sune Lundo Sorensen, Mikkel Baun Kjaergaard
https://arxiv.org/abs/2508.06295

Evaluating Robot Program Performance with Power Consumption Driven Metrics in Lightweight Industrial Robots
The code performance of industrial robots is typically analyzed through CPU metrics, which overlook the physical impact of code on robot behavior. This study introduces a novel framework for assessing robot program performance from an embodiment perspective by analyzing the robot's electrical power profile. Our approach diverges from conventional CPU based evaluations and instead leverages a suite of normalized metrics, namely, the energy utilization coefficient, the energy conversion metric, a…

A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)
https://arxiv.org/abs/2506.08153

A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
How can the complexity of ML-enabled systems be managed effectively? The goal of this research is to investigate how complexity affects ML-Enabled Systems (MLES). To address this question, this research aims to introduce a metrics-based architectural model to characterize the complexity of MLES. The goal is to support architectural decisions, providing a guideline for the inception and growth of these systems. This paper showcases the first step for creating the metrics-based architectural mode…

Examples of complete Calabi--Yau metrics on affine smoothings of irregular toric Calabi--Yau cones
Ronan J. Conlon, Tran-Trung Nghiem
https://arxiv.org/abs/2506.14118

Examples of complete Calabi--Yau metrics on affine smoothings of irregular toric Calabi--Yau cones
We present new examples of affine Calabi--Yau manifolds of Euclidean volume growth and quadratic curvature decay, whose tangent cones at infinity are irregular and have smooth links. In the process, we demonstrate (and provide the relevant computer code) how to explicitly compute the Reeb field and all Minkowski decompositions of a given toric Calabi--Yau cone with smooth link from the data of its toric polytope. Minkowski decompositions of this polytope into lattice segments and/or triangles g…

Multimodal Quantitative Measures for Multiparty Behaviour Evaluation
Ojas Shirekar, Wim Pouw, Chenxu Hao, Vrushank Phadnis, Thabo Beeler, Chirag Raman
https://arxiv.org/abs/2508.10916

Multimodal Quantitative Measures for Multiparty Behaviour Evaluation
Digital humans are emerging as autonomous agents in multiparty interactions, yet existing evaluation metrics largely ignore contextual coordination dynamics. We introduce a unified, intervention-driven framework for objective assessment of multiparty social behaviour in skeletal motion data, spanning three complementary dimensions: (1) synchrony via Cross-Recurrence Quantification Analysis, (2) temporal alignment via Multiscale Empirical Mode Decompositionbased Beat Consistency, and (3) structu…

Prompt-Response Semantic Divergence Metrics for Faithfulness Hallucination and Misalignment Detection in Large Language Models
Igor Halperin
https://arxiv.org/abs/2508.10192 htt…

Prompt-Response Semantic Divergence Metrics for Faithfulness Hallucination and Misalignment Detection in Large Language Models
The proliferation of Large Language Models (LLMs) is challenged by hallucinations, critical failure modes where models generate non-factual, nonsensical or unfaithful text. This paper introduces Semantic Divergence Metrics (SDM), a novel lightweight framework for detecting Faithfulness Hallucinations -- events of severe deviations of LLMs responses from input contexts. We focus on a specific implementation of these LLM errors, {confabulations, defined as responses that are arbitrary and semanti…

Covering instability for the existence of positive scalar curvature metrics
Chao Li, Boyu Zhang
https://arxiv.org/abs/2506.13885 https://

Covering instability for the existence of positive scalar curvature metrics
We show that a closed non-orientable $3$-manifold admits a positive scalar curvature metric if and only if its orientation double cover does; however, for each $4\le n\le 7$, there exist infinitely many non-homeomorphic smooth non-orientable $n$--manifolds such that each of them does not admit any Riemannian metric with positive scalar curvature, but its orientation double cover does. These examples were first introduced by Alpert--Balitskiy--Guth in the study of Urysohn widths. To prove the …

An Analysis of Datasets, Metrics and Models in Keyphrase Generation
Florian Boudin, Akiko Aizawa
https://arxiv.org/abs/2506.10346 https://

An Analysis of Datasets, Metrics and Models in Keyphrase Generation
Keyphrase generation refers to the task of producing a set of words or phrases that summarises the content of a document. Continuous efforts have been dedicated to this task over the past few years, spreading across multiple lines of research, such as model architectures, data resources, and use-case scenarios. Yet, the current state of keyphrase generation remains unknown as there has been no attempt to review and analyse previous work. In this paper, we bridge this gap by presenting an analys…

Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification
Kazuki Shimada, Archontis Politis, Iran R. Roman, Parthasaarathy Sudarsanam, David Diaz-Guerra, Ruchi Pandey, Kengo Uchida, Yuichiro Koyama, Naoya Takahashi, Takashi Shibuya, Shusuke Takahashi, Tuomas Virtanen, Yuki Mitsufuji
https://arxiv.org/a…

Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification
This paper presents the objective, dataset, baseline, and metrics of Task 3 of the DCASE2025 Challenge on sound event localization and detection (SELD). In previous editions, the challenge used four-channel audio formats of first-order Ambisonics (FOA) and microphone array. In contrast, this year's challenge investigates SELD with stereo audio data (termed stereo SELD). This change shifts the focus from more specialized 360° audio and audiovisual scene analysis to more commonplace audio and me…

Breaking New Ground in Software Defect Prediction: Introducing Practical and Actionable Metrics with Superior Predictive Power for Enhanced Decision-Making
Carlos Andr\'es Ram\'irez Cata\~no, Makoto Itoh
https://arxiv.org/abs/2508.04408

Breaking New Ground in Software Defect Prediction: Introducing Practical and Actionable Metrics with Superior Predictive Power for Enhanced Decision-Making
Software defect prediction using code metrics has been extensively researched over the past five decades. However, prediction harnessing non-software metrics is under-researched. Considering that the root cause of software defects is often attributed to human error, human factors theory might offer key forecasting metrics for actionable insights. This paper explores automated software defect prediction at the method level based on the developers' coding habits. First, we propose a framework for…

When a Paper Has 1000 Authors: Rethinking Citation Metrics in the Era of LLMs
Weihang Guo, Zhao Song, Jiahao Zhang
https://arxiv.org/abs/2508.06004 https://

When a Paper Has 1000 Authors: Rethinking Citation Metrics in the Era of LLMs
Author-level citation metrics provide a practical, interpretable, and scalable signal of scholarly influence in a complex research ecosystem. It has been widely used as a proxy in hiring decisions. However, the past five years have seen the rapid emergence of large-scale publications in the field of large language models and foundation models, with papers featuring hundreds to thousands of co-authors and receiving tens of thousands of citations within months. For example, Gemini has 1361 author…

Survey: 47% of UK adults and 81% of UK "decision makers" say they use AI "as part of consuming news and staying up to date with current events" (Dominic Ponsford/Press Gazette)
https://pressgazette.co.uk/media-audie

Where do UK 'decision makers' get their news? Publishers, social media and AI
Decision makers and news: Where do UK business and political leaders get informed about current affairs. Data on publishers, social media and AI.

Tracking of Intermittent and Moving Speakers : Dataset and Metrics
Taous Iatariene (MULTISPEECH), Alexandre Gu\'erin (MULTISPEECH), Romain Serizel (MULTISPEECH)
https://arxiv.org/abs/2506.11145

Tracking of Intermittent and Moving Speakers : Dataset and Metrics
This paper presents the problem of tracking intermittent and moving sources, i.e, sources that may change position when they are inactive. This issue is seldom explored, and most current tracking methods rely on spatial observations for track identity management. They are either based on a previous localization step, or designed to perform joint localization and tracking by predicting ordered position estimates. This raises concerns about whether such methods can maintain reliable track identit…

The fundamental physical importance of generic off-diagonal solutions and Grigori Perelman entropy in the Einstein gravity theory
Sergiu I. Vacaru, El\c{s}en V. Veliev
https://arxiv.org/abs/2508.10939 …

The fundamental physical importance of generic off-diagonal solutions and Grigori Perelman entropy in the Einstein gravity theory
The gravitational field equations in general relativity (GR) consist of a sophisticated system of nonlinear partial differential equations. Solving such equations in some generic off-diagonal forms is usually a hard analytic or numeric task. Physically important solutions in GR were constructed using a diagonal ansatz for metrics with a maximum of 4 independent coefficients. The Einstein equations can be solved in exact or parametric forms determined by some integration constants for correspond…

Hunting in the Dark: Metrics for Early Stage Traffic Discovery
Max Gao, Michael Collins, Ricky Mok, kc Claffy
https://arxiv.org/abs/2507.05213 https://

Hunting in the Dark: Metrics for Early Stage Traffic Discovery
Threat hunting is an operational security process where an expert analyzes traffic, applying knowledge and lightweight tools on unlabeled data in order to identify and classify previously unknown phenomena. In this paper, we examine threat hunting metrics and practice by studying the detection of Crackonosh, a cryptojacking malware package, has on various metrics for identifying its behavior. Using a metric for discoverability, we model the ability of defenders to measure Crackonosh traffic as …

A Riemannian Characterization of Compact Affine Manifolds with Parallel Volume
Mihail Cocos
https://arxiv.org/abs/2506.14778 https://…

A Riemannian Characterization of Compact Affine Manifolds with Parallel Volume
We establish that any affine manifold $(M,\nabla)$ endowed with a parallel volume form $ω,$ admits, in any conformal class of Riemannian metrics, a representative $H$ for which $\nabla$ is the Levi-Civita connection. This provides a constructive proof that such manifolds are necessarily complete, generalizing the "if" direction of Markus' conjecture \cite{markus1962}. Moreover, our result demonstrates that these structures are intrinsically Riemannian-flat, a stronger conclusion than the affin…

On balanced Hermitian threefolds with parallel Bismut torsion
Quanting Zhao, Fangyang Zheng
https://arxiv.org/abs/2506.15141 https://…

On balanced Hermitian threefolds with parallel Bismut torsion
We continue our study on Hermitian manifolds that are {\em Bismut torsion parallel,} or {\em BTP} for brevity, which means that the Bismut connection has parallel torsion tensor. For $n\geq 3$, BTP metrics can be balanced (and non-Kähler). In this paper, we give a classification of all compact, balanced BTP threefolds.

Language models align with brain regions that represent concepts across modalities
Maria Ryskina, Greta Tuckute, Alexander Fung, Ashley Malkin, Evelina Fedorenko
https://arxiv.org/abs/2508.11536

Language models align with brain regions that represent concepts across modalities
Cognitive science and neuroscience have long faced the challenge of disentangling representations of language from representations of conceptual meaning. As the same problem arises in today's language models (LMs), we investigate the relationship between LM--brain alignment and two neural metrics: (1) the level of brain activation during processing of sentences, targeting linguistic processing, and (2) a novel measure of meaning consistency across input modalities, which quantifies how consiste…

Sustainability Evaluation Metrics for Recommender Systems
Alexander Felfernig, Damian Garber, Viet-Man Le, Sebastian Lubos, Thi Ngoc Trang Tran
https://arxiv.org/abs/2507.22520 …

Sustainability Evaluation Metrics for Recommender Systems
Sustainability-oriented evaluation metrics can help to assess the quality of recommender systems beyond wide-spread metrics such as accuracy, precision, recall, and satisfaction. Following the United Nations`s sustainable development goals (SDGs), such metrics can help to analyse the impact of recommender systems on environmental, social, and economic aspects. We discuss different basic sustainability evaluation metrics for recommender systems and analyze their applications.

Replaced article(s) found for math.DG. https://arxiv.org/list/math.DG/new
[1/1]:
- Calabi-Yau metrics on rank two symmetric spaces with horospherical tangent cone at infinity
Tran-Trung Nghiem

Information-theoretic Estimation of the Risk of Privacy Leaks
Kenneth Odoh
https://arxiv.org/abs/2506.12328 https://arxiv.org/pdf/250…

Information-theoretic Estimation of the Risk of Privacy Leaks
Recent work~\cite{Liu2016} has shown that dependencies between items in a dataset can lead to privacy leaks. We extend this concept to privacy-preserving transformations, considering a broader set of dependencies captured by correlation metrics. Specifically, we measure the correlation between the original data and their noisy responses from a randomizer as an indicator of potential privacy breaches. This paper aims to leverage information-theoretic measures, such as the Maximal Information Coe…

Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metrics
Chunhua Liu, Hong Yi Lin, Patanamon Thongtanunam
https://arxiv.org/abs/2508.08661

Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metrics
Language models have shown strong capabilities across a wide range of tasks in software engineering, such as code generation, yet they suffer from hallucinations. While hallucinations have been studied independently in natural language and code generation, their occurrence in tasks involving code changes which have a structurally complex and context-dependent format of code remains largely unexplored. This paper presents the first comprehensive analysis of hallucinations in two critical tasks i…

Real-World Summarization: When Evaluation Reaches Its Limits
Patr\'icia Schmidtov\'a, Ond\v{r}ej Du\v{s}ek, Saad Mahamood
https://arxiv.org/abs/2507.11508

Real-World Summarization: When Evaluation Reaches Its Limits
We examine evaluation of faithfulness to input data in the context of hotel highlights: brief LLM-generated summaries that capture unique features of accommodations. Through human evaluation campaigns involving categorical error assessment and span-level annotation, we compare traditional metrics, trainable methods, and LLM-as-a-judge approaches. Our findings reveal that simpler metrics like word overlap correlate surprisingly well with human judgments (Spearman correlation rank of 0.63), often…

Special vector fields on Riemannian manifolds of constant negative sectional curvature and conservation laws
Keti Tenenblat, Alice Barbora Tumpach
https://arxiv.org/abs/2506.14960

Special vector fields on Riemannian manifolds of constant negative sectional curvature and conservation laws
We show that any $n$-dimensional Riemannian manifold with constant negative sectional curvature admits local orthonormal vector fields such that one of them $v_1$ is tangent to geodesics and the other $n-1$ vector fields are tangent to horocycles. We prove that the $1$-form dual to $v_1$ is a closed form. We show how the closed form can be used to obtain conservation laws for PDEs whose generic solutions define metrics on open subsets with constant negative sectional curvature. These results ex…

Vacuum Homogeneous and Nonhomogeneous Metrics with Conventional and Quantized Metric Tensor: Singular or Nonsingular Solution
A. Tawfik (Egyptian Ctr. Theor. Phys., Cairo,Future U. in Egypt), S. G. Elgendi (Islamic U. in Madinah), M. Hanafy (Benha U.)
https://arxiv.org/abs/2506.08254

Vacuum Homogeneous and Nonhomogeneous Metrics with Conventional and Quantized Metric Tensor: Singular or Nonsingular Solution
To investigate whether the Universe underwent a singularity or maintained a nonsingular state, we carry out analytical and numerical analyses of the evolution of the Raychaudhuri equations in vacuum, alongside homogeneous and nonhomogeneous cosmic backgrounds. The results obtained from the Schwarzschild, Friedmann--Lemaitre--Robertson--Walker (FLRW), and Einstein--Gilbert--Straus (EGS) metrics are systematically compared. Analyzing the results from both, conventional and quantized metric tensor…

Geometry of left-invariant vector fields on Lie groups
M. L. Foka, R. P. Nimpa, M. B. N. Djiadeu
https://arxiv.org/abs/2508.11440 https://arxiv.org/pdf/250…

Geometry of left-invariant vector fields on Lie groups
This paper examines the geometry of left-invariant vector fields on five-dimensional, simply connected, nilpotent Lie groups equipped with left-invariant Riemannian metrics. Using the canonical identification between the Lie algebra and the space of left-invariant vector fields, we derive algebraic characterizations for Killing, one-harmonic Killing, conformal, and concurrent vector fields. Employing a classification of these nilpotent Lie algebras into ten canonical types, we perform a case-by…

DCR: Quantifying Data Contamination in LLMs Evaluation
Cheng Xu, Nan Yan, Shuhao Guan, Changhong Jin, Yuke Mei, Yibing Guo, M-Tahar Kechadi
https://arxiv.org/abs/2507.11405

DCR: Quantifying Data Contamination in LLMs Evaluation
The rapid advancement of large language models (LLMs) has heightened concerns about benchmark data contamination (BDC), where models inadvertently memorize evaluation data, inflating performance metrics and undermining genuine generalization assessment. This paper introduces the Data Contamination Risk (DCR) framework, a lightweight, interpretable pipeline designed to detect and quantify BDC across four granular levels: semantic, informational, data, and label. By synthesizing contamination sco…

Addressing Data Imbalance in Transformer-Based Multi-Label Emotion Detection with Weighted Loss
Xia Cui
https://arxiv.org/abs/2507.11384 https://

Addressing Data Imbalance in Transformer-Based Multi-Label Emotion Detection with Weighted Loss
This paper explores the application of a simple weighted loss function to Transformer-based models for multi-label emotion detection in SemEval-2025 Shared Task 11. Our approach addresses data imbalance by dynamically adjusting class weights, thereby enhancing performance on minority emotion classes without the computational burden of traditional resampling methods. We evaluate BERT, RoBERTa, and BART on the BRIGHTER dataset, using evaluation metrics such as Micro F1, Macro F1, ROC-AUC, Accurac…

On families of Finsler metrics
Ismail Saglam (IRMA), Ken'Ichi Ohshika (IRMA), Athanase Papadopoulos (IRMA)
https://arxiv.org/abs/2506.03749 https://

On families of Finsler metrics
In this paper, we answer some natural questions on symmetrisation and more general combinations of Finsler metrics, with a view towards applications to Funk and Hilbert geometries and to metrics on Teichm{ü}ller spaces. For a general non-symmetric Finsler metric on a smooth manifold, we introduce two different families of metrics, containing as special cases the arithmetic and the max symmetrisations respectively of the distance functions associated with these Finsler metrics. We are intereste…

This https://arxiv.org/abs/2505.23327 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Twisted and coupled constant scalar curvature Kähler metrics on minimal ruled surfaces
In this paper, we study the existence of twisted constant scalar curvature Kähler (cscK) metrics and non-existence of coupled cscK metrics on minimal ruled surfaces over a Riemann surface of genus $2$. Moreover, we give a bound for the Chen-Cheng invariant related to Chen's continuity path for cscK problem on these ruled surfaces.

Contact magnetic geodesic and sub-Riemannian flows on $V_{n,2}$ and integrable cases of a heavy rigid body with a gyrostat
Bozidar Jovanovic
https://arxiv.org/abs/2506.13101

Contact magnetic geodesic and sub-Riemannian flows on $V_{n,2}$ and integrable cases of a heavy rigid body with a gyrostat
We prove the integrability of magnetic geodesic flows of $SO(n)$--invariant Riemannian metrics on the rank two Stefel variety $V_{n,2}$ with respect to the magnetic field $η\, dα$, where $α$ is the standard contact form on $V_{n,2}$ and $η$ is a real parameter. Also, we prove the integrability of magnetic sub-Riemannian geodesic flows for $SO(n)$-invariant sub-Riemannian structures on $V_{n,2}$. All statements in the limit $η=0$ imply the integrability of the problems without the influence…

Local classification of K\"ahler metrics with constant holomorphic sectional curvature
Martin de Borbon
https://arxiv.org/abs/2507.06855 https://

Local classification of Kähler metrics with constant holomorphic sectional curvature
We prove the local classification of Kähler metrics with constant holomorphic sectional curvature by exploiting the geometry of the bundle of 1-jets of holomorphic functions.

Linear stability of Perelman's $\nu$-entropy of standard Einstein manifolds
Emilio A. Lauret, Alejandro Tolcachier
https://arxiv.org/abs/2506.12435 htt…

Linear stability of Perelman's $ν$-entropy of standard Einstein manifolds
Paul Schwahn recently exhibited 112 non-symmetric, connected, simply connected, compact Einstein manifolds that are stable with respect to the total scalar curvature functional restricted to the space of Riemannian metrics with constant scalar curvature and fixed volume. This stability follows from the inequality $λ_L > 2E$, where $λ_L$ denotes the smallest eigenvalue of the Lichnerowicz Laplacian on TT-tensors and $E$ is the corresponding Einstein factor. In this paper, we estimate the sma…

A Lorentzian splitting theorem for continuously differentiable metrics and weights
Mathias Braun, Nicola Gigli, Robert J. McCann, Argam Ohanyan, Clemens S\"amann
https://arxiv.org/abs/2507.06836

A Lorentzian splitting theorem for continuously differentiable metrics and weights
We prove a splitting theorem for globally hyperbolic, weighted spacetimes with metrics and weights of regularity $C^1$ by combining elliptic techniques for the negative homogeneity $p$-d'Alembert operator from our recent work in the smooth setting with the concept of line-adapted curves introduced here. Our results extend the Lorentzian splitting theorem proved for smooth globally hyperbolic spacetimes by Galloway -- and variants of its weighted counterparts by Case and Woolgar--Wylie -- to thi…

Replaced article(s) found for math.DG. https://arxiv.org/list/math.DG/new
[1/1]:
- Ricci curvature bounds and rigidity for non-smooth Riemannian and semi-Riemannian metrics
Michael Kunzinger, Argam Ohanyan, Alessio Vardabasso