Tootfinder

{testthat} is great for automatic testing. Here are some tricks for the heavy user: #rstats

A Collusion-Resistance Privacy-Preserving Smart Metering Protocol for Operational Utility
Farid Zaredar, Morteza Amini
https://arxiv.org/abs/2508.14744 https://

A Collusion-Resistance Privacy-Preserving Smart Metering Protocol for Operational Utility
Modern grids have adopted advanced metering infrastructure (AMI) to facilitate bidirectional communication between smart meters and control centers. This enables smart meters to report consumption values at predefined intervals to utility providers for purposes including demand balancing, load forecasting, dynamic billing, and operational efficiency. Compared to traditional power grids, smart grids offer advantages such as enhanced reliability, improved energy efficiency, and increased security…

#Google setzt auf #Kernenergie der nächsten Generation und schließt mit dem US-Versorger #TVA einen Stromabnahmevertrag für den geplanten

Google announced the next step in its nuclear energy plans 
Google is supporting the development of a next-generation nuclear reactor in a bid to get enough electricity for its AI data centers.

ViT-FIQA: Assessing Face Image Quality using Vision Transformers
Andrea Atzori, Fadi Boutros, Naser Damer
https://arxiv.org/abs/2508.13957 https://arxiv.or…

ViT-FIQA: Assessing Face Image Quality using Vision Transformers
Face Image Quality Assessment (FIQA) aims to predict the utility of a face image for face recognition (FR) systems. State-of-the-art FIQA methods mainly rely on convolutional neural networks (CNNs), leaving the potential of Vision Transformer (ViT) architectures underexplored. This work proposes ViT-FIQA, a novel approach that extends standard ViT backbones, originally optimized for FR, through a learnable quality token designed to predict a scalar utility score for any given face image. The le…

RewardRank: Optimizing True Learning-to-Rank Utility
Gaurav Bhatt, Kiran Koshy Thekumparampil, Tanmay Gangwani, Tesi Xiao, Leonid Sigal
https://arxiv.org/abs/2508.14180 https://…

RewardRank: Optimizing True Learning-to-Rank Utility
Traditional ranking systems rely on proxy loss functions that assume simplistic user behavior, such as users preferring a rank list where items are sorted by hand-crafted relevance. However, real-world user interactions are influenced by complex behavioral biases, including position bias, brand affinity, decoy effects, and similarity aversion, which these objectives fail to capture. As a result, models trained on such losses often misalign with actual user utility, such as the probability of an…

Application Placement with Constraint Relaxation
Damiano Azzolini, Marco Duca, Stefano Forti, Francesco Gallo, Antonio Ielo
https://arxiv.org/abs/2507.13895

Application Placement with Constraint Relaxation
Novel utility computing paradigms rely upon the deployment of multi-service applications to pervasive and highly distributed cloud-edge infrastructure resources. Deciding onto which computational nodes to place services in cloud-edge networks, as per their functional and non-functional constraints, can be formulated as a combinatorial optimisation problem. Most existing solutions in this space are not able to deal with \emph{unsatisfiable} problem instances, nor preferences, i.e. requirements t…

Nonparametric Bounds for Evaluating the Clinical Utility of Treatment Rules
Johannes Hruza, Erin Gabriel, Arvid Sj\"olander, Samir Bhatt, Michael Sachs
https://arxiv.org/abs/2508.14653

Nonparametric Bounds for Evaluating the Clinical Utility of Treatment Rules
Evaluating the value of new clinical treatment rules based on patient characteristics is important but often complicated by hidden confounding factors in observational studies. Standard methods for estimating the average patient outcome if a new rule were universally adopted typically rely on strong, untestable assumptions about these hidden factors. This paper tackles this challenge by developing nonparametric bounds - a range of plausible values - for the expected outcome under a new rule, ev…

CRISP: Persistent Concept Unlearning via Sparse Autoencoders
Tomer Ashuach, Dana Arad, Aaron Mueller, Martin Tutek, Yonatan Belinkov
https://arxiv.org/abs/2508.13650 https://

CRISP: Persistent Concept Unlearning via Sparse Autoencoders
As large language models (LLMs) are increasingly deployed in real-world applications, the need to selectively remove unwanted knowledge while preserving model utility has become paramount. Recent work has explored sparse autoencoders (SAEs) to perform precise interventions on monosemantic features. However, most SAE-based methods operate at inference time, which does not create persistent changes in the model's parameters. Such interventions can be bypassed or reversed by malicious actors with …

Reliability, Embeddedness, and Agency: A Utility-Driven Mathematical Framework for Agent-Centric AI Adoption
Faruk Alpay, Taylan Alpay
https://arxiv.org/abs/2508.12896 https://

Reliability, Embeddedness, and Agency: A Utility-Driven Mathematical Framework for Agent-Centric AI Adoption
We formalize three design axioms for sustained adoption of agent-centric AI systems executing multi-step tasks: (A1) Reliability > Novelty; (A2) Embed > Destination; (A3) Agency > Chat. We model adoption as a sum of a decaying novelty term and a growing utility term and derive the phase conditions for troughs/overshoots with full proofs. We introduce: (i) an identifiability/confounding analysis for $(α,β,N_0,U_{\max})$ with delta-method gradients; (ii) a non-monotone comparator (logistic-with…

Incremental-Decremental Maximization
Yann Disser, Max Klimm, Annette Lutz, Lea Strubberg
https://arxiv.org/abs/2508.14516 https://arxiv.org/pdf/2508.14516

Incremental-Decremental Maximization
We introduce a framework for incremental-decremental maximization that captures the gradual transformation or renewal of infrastructures. In our model, an initial solution is transformed one element at a time and the utility of an intermediate solution is given by the sum of the utilities of the transformed and untransformed parts. We propose a simple randomized and a deterministic algorithm that both find an order in which to transform the elements while maintaining a large utility during all …

LLM-Based Community Surveys for Operational Decision Making in Interconnected Utility Infrastructures
Adaeze Okeukwu-Ogbonnaya, Rahul Amatapu, Jason Bergtold, George Amariucai
https://arxiv.org/abs/2507.13577

LLM-Based Community Surveys for Operational Decision Making in Interconnected Utility Infrastructures
We represent interdependent infrastructure systems and communities alike with a hetero-functional graph (HFG) that encodes the dependencies between functionalities. This graph naturally imposes a partial order of functionalities that can inform the sequence of repair decisions to be made during a disaster across affected communities. However, using such technical criteria alone provides limited guidance at the point where the functionalities directly impact the communities, since these can be r…

Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction
Niklas Bubeck, Suprosanna Shit, Chen Chen, Can Zhao, Pengfei Guo, Dong Yang, Georg Zitzlsberger, Daguang Xu, Bernhard Kainz, Daniel Rueckert, Jiazhen Pan
https://arxiv.org/abs/2508.13826

Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction
Cardiac Magnetic Resonance (CMR) imaging is a critical tool for diagnosing and managing cardiovascular disease, yet its utility is often limited by the sparse acquisition of 2D short-axis slices, resulting in incomplete volumetric information. Accurate 3D reconstruction from these sparse slices is essential for comprehensive cardiac assessment, but existing methods face challenges, including reliance on predefined interpolation schemes (e.g., linear or spherical), computational inefficiency, an…

Transitioning to off-grid living can save you money and reduce reliance on major utility companies. #climatechange #climatesolutions #climate

Off-grid homeowner shares before-and-after photos of family's new setup: 'That's awesome'
This homeowner is making the most out of her off-grid living, despite the hurdles trying to slow her down.

Learning in Repeated Multi-Objective Stackelberg Games with Payoff Manipulation
Phurinut Srisawad, Juergen Branke, Long Tran-Thanh
https://arxiv.org/abs/2508.14705 https://

Learning in Repeated Multi-Objective Stackelberg Games with Payoff Manipulation
We study payoff manipulation in repeated multi-objective Stackelberg games, where a leader may strategically influence a follower's deterministic best response, e.g., by offering a share of their own payoff. We assume that the follower's utility function, representing preferences over multiple objectives, is unknown but linear, and its weight parameter must be inferred through interaction. This introduces a sequential decision-making challenge for the leader, who must balance preference elicita…

A Distributed Learned Hash Table
Shengze Wang, Yi Liu, Xiaoxue Zhang, Liting Hu, Chen Qian
https://arxiv.org/abs/2508.14239 https://arxiv.org/pdf/2508.1423…

A Distributed Learned Hash Table
Distributed Hash Tables (DHTs) are pivotal in numerous high-impact key-value applications built on distributed networked systems, offering a decentralized architecture that avoids single points of failure and improves data availability. Despite their widespread utility, DHTs face substantial challenges in handling range queries, which are crucial for applications such as LLM serving, distributed storage, databases, content delivery networks, and blockchains. To address this limitation, we prese…

A Ghost Lemma for Commutative Ring Homomorphisms via Andr\'{e}-Quillen Homology
Daniel McCormick
https://arxiv.org/abs/2507.13988 https://

A Ghost Lemma for Commutative Ring Homomorphisms via André-Quillen Homology
We adapt the theory of ghost maps from derived categories to the setting of commutative rings using André-Quillen homology. The Frobenius endomorphism is a primary example of a ghost map in this setting. We prove an analogue of the ghost lemma for rings and demonstrate its utility by deducing a characteristic independent generalization of Kunz's theorem and an analogue for complete intersection rings.

Ever needed to duplicate a #keycloak realm.json, change a few values and re-import into the same keycloak ?
this is a Keycloak realm rekeying utility, safe UUID regeneration and alias normalization to avoid duplicate constraints during import https://

GitHub - rmdes/realm-duplicator-keycloak: Tools to rekey Keycloak realm JSON: regenerate IDs, normalize references, and eliminate collisions for seamless multi-realm imports.
Tools to rekey Keycloak realm JSON: regenerate IDs, normalize references, and eliminate collisions for seamless multi-realm imports. - rmdes/realm-duplicator-keycloak

ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning
Zihou Wu (School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China), Yuecheng Li (School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China), Tianchi Liao (School of Software Engineering, Sun Yat-sen University, Zhuhai, China), Jian Lou (School of Software Engineering, Sun Yat-sen University, Zhuhai, China), Chuan Chen (School of Computer Science and E…

ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning
Federated learning (FL) faces a critical dilemma: existing protection mechanisms like differential privacy (DP) and homomorphic encryption (HE) enforce a rigid trade-off, forcing a choice between model utility and computational efficiency. This lack of flexibility hinders the practical implementation. To address this, we introduce ParaAegis, a parallel protection framework designed to give practitioners flexible control over the privacy-utility-efficiency balance. Our core innovation is a strat…

Quantum Utility in Simulating the Real-time Dynamics of the Fermi-Hubbard Model using Superconducting Quantum Computers
Talal Ahmed Chowdhury, Vladimir Korepin, Vincent R. Pascuzzi, Kwangmin Yu
https://arxiv.org/abs/2509.14196

Quantum Utility in Simulating the Real-time Dynamics of the Fermi-Hubbard Model using Superconducting Quantum Computers
The Fermi-Hubbard model is a fundamental model in condensed matter physics that describes strongly correlated electrons. On the other hand, quantum computers are emerging as powerful tools for exploring the complex dynamics of these quantum many-body systems. In this work, we demonstrate the quantum simulation of the one-dimensional Fermi-Hubbard model using IBM's superconducting quantum computers, employing over 100 qubits. We introduce a first-order Trotterization scheme and extend it to an o…

A Lightweight Privacy-Preserving Smart Metering Billing Protocol with Dynamic Tariff Policy Adjustment
Farid Zaredar, Morteza Amini
https://arxiv.org/abs/2508.14815 https://

A Lightweight Privacy-Preserving Smart Metering Billing Protocol with Dynamic Tariff Policy Adjustment
The integration of information and communication technology (ICT) with traditional power grids has led to the emergence of smart grids. Advanced metering infrastructure (AMI) plays a crucial role in smart grids by facilitating two-way communication between smart meters and the utility provider. This bidirectional communication allows intelligent meters to report fine-grained consumption data at predefined intervals, enabling accurate billing, efficient grid monitoring and management, and rapid …

BreastSegNet: Multi-label Segmentation of Breast MRI
Qihang Li, Jichen Yang, Yaqian Chen, Yuwen Chen, Hanxue Gu, Lars J. Grimm, Maciej A. Mazurowski
https://arxiv.org/abs/2507.13604

BreastSegNet: Multi-label Segmentation of Breast MRI
Breast MRI provides high-resolution imaging critical for breast cancer screening and preoperative staging. However, existing segmentation methods for breast MRI remain limited in scope, often focusing on only a few anatomical structures, such as fibroglandular tissue or tumors, and do not cover the full range of tissues seen in scans. This narrows their utility for quantitative analysis. In this study, we present BreastSegNet, a multi-label segmentation algorithm for breast MRI that covers nine…

Replaced article(s) found for cs.IR. https://arxiv.org/list/cs.IR/new
[1/1]:
- Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy
Hengran Zhang, Keping Bi, Jiafeng Guo, Xueqi Cheng

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…

Choquet rank-dependent utility
Zachary Van Oosten, Ruodu Wang
https://arxiv.org/abs/2509.10788 https://arxiv.org/pdf/2509.10788

Choquet rank-dependent utility
We propose a new decision model under ambiguity, called the Choquet rank-dependent utility model. The model extends the Choquet expected utility model by allowing for the reduction to the rank-dependent utility model in the absence of ambiguity, rather than to the expected utility model. The model has three major components: a utility function $u$ and a probability distortion $g$, which together capture the risk component of the preferences, and generalized probabilistic beliefs $ν$, which cap…

#MacOS
where did #apple get the idea to get rid of the network utility app???
what is wrong with you guys!
#cook

Using utility graphs to search for Pareto-optimal outcomes in complex, interdependent issue negotiations
Valentin Robu, Mark Klein
https://arxiv.org/abs/2509.10885 https://

Using utility graphs to search for Pareto-optimal outcomes in complex, interdependent issue negotiations
This paper studies how utility graphs decomposition algorithms can be used to effectively search for Pareto-efficient outcomes in complex automated negotiation. We propose a number of algorithms that can efficiently handle high-dimensional utility graphs, and test them on a variety of utility graph topologies, generated based on state of the art methods for analysing complex graphs. We show that we can achieve exponential speed-up, for many structures, even for very large utility graphs. To our…

"Trump’s push to keep coal plants running could cost consumers billions"
#US #USA #America #FossilFuels

Trump’s push to keep coal plants running could cost consumers billions
The DOE’s emergency stay-open orders for aging fossil-fuel plants are forcing unexpected costs onto utility customers. Additional orders would deepen the…

Karmic Historiography Forum on The Immanent Frame
https://ift.tt/DetWo0Z
University of Alaska - Fairbanks - Alaska & Polar Regions Area Studies Bibliographer & Curator of…
via Input 4 RELCFP

Point upsampling networks for single-photon sensing
Jinyi Liu, Guoyang Zhao, Lijun Liu, Yiguang Hong, Weiping Zhang, Shuming Cheng
https://arxiv.org/abs/2508.12986 https://

Point upsampling networks for single-photon sensing
Single-photon sensing has generated great interest as a prominent technique of long-distance and ultra-sensitive imaging, however, it tends to yield sparse and spatially biased point clouds, thus limiting its practical utility. In this work, we propose using point upsampling networks to increase point density and reduce spatial distortion in single-photon point cloud. Particularly, our network is built on the state space model which integrates a multi-path scanning mechanism to enrich spatial c…

Ah yes, nearly 2am and have finally got audio working on the new T480. Was it too much to ask that when you plug a headphone cable in, the sound gets routed to them? Apparently so!
On the other hand, I've got a new example of the old "Don't give me that stony-faced glare, I'm a user too" cartoon.
I present:

The Economics of Information Pollution in the Age of AI: A General Equilibrium Approach to Welfare, Measurement, and Policy
Yukun Zhang, Tianyang Zhang
https://arxiv.org/abs/2509.13729

The Economics of Information Pollution in the Age of AI: A General Equilibrium Approach to Welfare, Measurement, and Policy
The advent of Large Language Models (LLMs) represents a fundamental shock to the economics of information production. By asymmetrically collapsing the marginal cost of generating low-quality, synthetic content while leaving high-quality production costly, AI systematically incentivizes information pollution. This paper develops a general equilibrium framework to analyze this challenge. We model the strategic interactions among a monopolistic platform, profit-maximizing producers, and utility-ma…

Distributed Nash Equilibrium Seeking in Non-Monotone Games over the Simplex
Tatiana Tatarenko, S. Rasoul Etesami
https://arxiv.org/abs/2507.13244 https://

Distributed Nash Equilibrium Seeking in Non-Monotone Games over the Simplex
In this work, we present a novel characterization of approximate Nash equilibria in a class of convex games over the simplex. To achieve this, we regularize the utility functions using the Shannon entropy term, connect the solutions to the regularized game with the set of Nash equilibria, and formulate a multi-objective optimization problem to solve the regularized game. Based on the obtained properties of the stationary points in this optimization problem, we formulate two distributed heuristi…

A utility for posting across multiple social networks at once.
https://github.com/humanwhocodes/crosspost

visionOS 26: Keine Public Beta, wenig Liquid Glass | TNBT-Podcast
Themen in dieser Folge: visionOS 26 Beta 4, PS-VR2-Controller, Apple Immersive Video Utility, Cyberpunk und mehr.
https://www.

Why Diffeology?
Patrick Iglesias-Zemmour
https://arxiv.org/abs/2508.11264 https://arxiv.org/pdf/2508.11264

Why Diffeology?
Diffeology extends differential geometry to spaces beyond smooth manifolds. This paper explores diffeology's key features and illustrates its utility with examples including singular and quotient spaces, and applications in symplectic geometry and prequantization. Diffeology provides a natural and effective framework for handling complexities from singularities and infinite-dimensional settings.

Assessing the Value of Visual Input: A Benchmark of Multimodal Large Language Models for Robotic Path Planning
Jacinto Colan, Ana Davila, Yasuhisa Hasegawa
https://arxiv.org/abs/2507.12391

Assessing the Value of Visual Input: A Benchmark of Multimodal Large Language Models for Robotic Path Planning
Large Language Models (LLMs) show potential for enhancing robotic path planning. This paper assesses visual input's utility for multimodal LLMs in such tasks via a comprehensive benchmark. We evaluated 15 multimodal LLMs on generating valid and optimal paths in 2D grid environments, simulating simplified robotic planning, comparing text-only versus text-plus-visual inputs across varying model sizes and grid complexities. Our results indicate moderate success rates on simpler small grids, where …

Replaced article(s) found for q-fin.PM. https://arxiv.org/list/q-fin.PM/new
[1/1]:
- Robust Utility Optimization via a GAN Approach
Florian Krach, Josef Teichmann, Hanna Wutte

Design and Reliability of a User Space Write-Ahead Log in Rust
Vitor K. F. Pellegatti, Gustavo M. D. Vieira
https://arxiv.org/abs/2507.13062 https://arxiv.…

Design and Reliability of a User Space Write-Ahead Log in Rust
Write-ahead logs (WALs) are a fundamental fault-tolerance technique found in many areas of computer science. WALs must be reliable while maintaining high performance, because all operations will be written to the WAL to ensure their stability. Without reliability a WAL is useless, because its utility is tied to its ability to recover data after a failure. In this paper we describe our experience creating a prototype user space WAL in Rust. We observed that Rust is easy to use, compact and has a…

Reliable high-accuracy error mitigation for utility-scale quantum circuits
Dorit Aharonov, Ori Alberton, Itai Arad, Yosi Atia, Eyal Bairey, Matan Ben Dov, Asaf Berkovitch, Zvika Brakerski, Itsik Cohen, Eran Fuchs, Omri Golan, Or Golan, Barak D. Gur, Ilya Gurwich, Avieli Haber, Rotem Haber, Dorri Halbertal, Yaron Itkin, Barak A. Katzir, Oded Kenneth, Shlomi Kotler, Roei Levi, Eyal Leviatan, Yotam Y. Lifshitz, Adi Ludmer, Shlomi Matityahu, Ron Aharon Melcer, Adiel Meyer, Omrie Ovdat, Avi…

Reliable high-accuracy error mitigation for utility-scale quantum circuits
Error mitigation is essential for unlocking the full potential of quantum algorithms and accelerating the timeline toward quantum advantage. As quantum hardware progresses to push the boundaries of classical simulation, efficient and robust error mitigation methods are becoming increasingly important for producing accurate and reliable outputs. We introduce QESEM, a reliable, high-accuracy, characterization-based software implementing efficient, unbiased quasi-probabilistic error mitigation. We…

Newly released court documents show power utility DTE Energy knowingly contributed $100,000 to a dark money non-profit that helped sabotage Michigangovernor Gretchen Whitmer’s Covid lockdown policies.
The documents contradict previous DTE statements that claimed the utility was not involved with the donation.

EPA plans to revise coal-fired power plant wastewater rules | Utility Dive
https://www.utilitydive.com/news/epa-coal-fired-power-plant-wastewater-effluent-elg/757529/

Distributed Algorithms for Potential Problems
Alkida Balliu, Thomas Boudier, Francesco d'Amore, Dennis Olivetti, Gustav Schmid, Jukka Suomela
https://arxiv.org/abs/2507.12038 …

Distributed Algorithms for Potential Problems
In this work we present a fast distributed algorithm for local potential problems: these are graph problems where the task is to find a locally optimal solution where no node can unilaterally improve the utility in its local neighborhood by changing its own label. A simple example of such a problem is the task of finding a locally optimal cut, i.e., a cut where for each node at least half of its incident edges are cut edges. The distributed round complexity of locally optimal cut has been wide …

@… Also the dialog utility makes it really easy to add a bit of curses UI powered by a simple shell script, which is exactly what you are seeing here. That whole thing was just written in bash.

Public Data Assisted Differentially Private In-Context Learning
Seongho Joo, Hyukhun Koh, Kyomin Jung
https://arxiv.org/abs/2509.10932 https://arxiv.org/pd…

Public Data Assisted Differentially Private In-Context Learning
In-context learning (ICL) in Large Language Models (LLMs) has shown remarkable performance across various tasks without requiring fine-tuning. However, recent studies have highlighted the risk of private data leakage through the prompt in ICL, especially when LLMs are exposed to malicious attacks. While differential privacy (DP) provides strong privacy guarantees, it often significantly reduces the utility of in-context learning (ICL). To address this challenge, we incorporate task-related publ…

The Interplay between Utility and Risk in Portfolio Selection
Leonardo Baggiani, Martin Herdegen, Nazem Khan
https://arxiv.org/abs/2509.10351 https://arxiv…

The Interplay between Utility and Risk in Portfolio Selection
We revisit the problem of portfolio selection, where an investor maximizes utility subject to a risk constraint. Our framework is very general and accommodates a wide range of utility and risk functionals, including non-concave utilities such as S-shaped utilities from prospect theory and non-convex risk measures such as Value at Risk. Our main contribution is a novel and complete characterization of well-posedness for utility-risk portfolio selection in one period that takes the interplay betw…

UTI-LLM: A Personalized Articulatory-Speech Therapy Assistance System Based on Multimodal Large Language Model
Yudong Yang, Xiaokang Liu, Shaofeng zhao, Rongfeng Su, Nan Yan, Lan Wang
https://arxiv.org/abs/2509.13145

UTI-LLM: A Personalized Articulatory-Speech Therapy Assistance System Based on Multimodal Large Language Model
Speech therapy plays a critical role in training speech disorders caused by neurological impairments such as stroke. However, traditional manual and computer-assisted systems are limited in real-time accessibility and articulatory motion feedback, constraining their practical utility. Recent advances in multimodal large language models (MLLMs) have demonstrated significant potential in healthcare, particularly through their ability to integrate multimodal data for adaptive assessment and therap…

Group Fair Matchings using Convex Cost Functions
Atasi Panda, Harsh Sharma, Anand Louis, Prajakta Nimbhorkar
https://arxiv.org/abs/2508.12549 https://arxiv…

Group Fair Matchings using Convex Cost Functions
We consider the problem of assigning items to platforms where each item has a utility associated with each of the platforms to which it can be assigned. Each platform has a soft constraint over the total number of items it serves, modeled via a convex cost function. Additionally, items are partitioned into groups, and each platform also incurs group-specific convex cost over the number of items from each group that can be assigned to the platform. These costs promote group fairness by penalizin…

SafeConstellations: Steering LLM Safety to Reduce Over-Refusals Through Task-Specific Trajectory
Utsav Maskey, Sumit Yadav, Mark Dras, Usman Naseem
https://arxiv.org/abs/2508.11290

SafeConstellations: Steering LLM Safety to Reduce Over-Refusals Through Task-Specific Trajectory
LLMs increasingly exhibit over-refusal behavior, where safety mechanisms cause models to reject benign instructions that superficially resemble harmful content. This phenomena diminishes utility in production applications that repeatedly rely on common prompt templates or applications that frequently rely on LLMs for specific tasks (e.g. sentiment analysis, language translation). Through comprehensive evaluation, we demonstrate that LLMs still tend to refuse responses to harmful instructions wh…

Efficacy of Galaxy Catalogues for following up gravitational wave events
Tamojeet Roychowdhury, Harsh Choudhary, Varun Bhalerao, David O. Cook, Viraj Karambelkar, Mansi M. Kasliwal, Harsh Kumar, Surhud More, Gaurav Waratkar
https://arxiv.org/abs/2507.11635

Efficacy of Galaxy Catalogues for following up gravitational wave events
The detection of gravitational waves (GW) by the LIGO-Virgo-KAGRA (LVK) network has opened up a new era in astrophysics. The identification of the electromagnetic counterparts of GW sources is crucial for multi-messenger astronomy, one way of which is to use galaxy catalogues to guide optical follow-up observations. In this paper, we test the utility of galaxy-targeted approach with mass prioritised galaxy ranking for the ongoing LIGO O4 run. We have used the simulated results for the expected …

Dual is Different: EFTs, Axions and Nonpropagating Form Fields in Cosmology
C. P. Burgess, F. Quevedo
https://arxiv.org/abs/2509.11340 https://arxiv.org/pd…

Dual is Different: EFTs, Axions and Nonpropagating Form Fields in Cosmology
Scalar fields in 4D are known to have equivalent dual descriptions in terms of form-field gauge potentials, but this is often regarded as an arcane fact. Why use more complicated formulations when simpler scalar descriptions exist and are equivalent? We describe three ways in which scalars that arise as duals can differ from their garden-variety counterparts. Two of these -- the interchange of weak and strong couplings and utility in bringing topological information to the low-energy theory -- …

FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Health
Nobin Sarwar, Shubhashis Roy Dipta
https://arxiv.org/abs/2509.14275 https://

FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Health
Privacy-preserving adaptation of Large Language Models (LLMs) in sensitive domains (e.g., mental health) requires balancing strict confidentiality with model utility and safety. We propose FedMentor, a federated fine-tuning framework that integrates Low-Rank Adaptation (LoRA) and domain-aware Differential Privacy (DP) to meet per-domain privacy budgets while maintaining performance. Each client (domain) applies a custom DP noise scale proportional to its data sensitivity, and the server adaptiv…

DEF CON volunteers step up to help water sector after China, Iran attack utilities https://therecord.media/def-con-franklin-water-utility-cybersecurity-volunteers

#Toronto apparently has a Sake Crisis. LCBO tells me the nearest bottle is 50km away, the rest marked back order. We suspect the true bottler is US based 😔
That's Utility Sake; for cooking I'm not keen on paying premium for the nice stuff, though it may come to that. Time to research local brands.

On The Road - To Xi’An/Before & After 🌬️
在路上 - 去西安/之前之后🌬️
📷 Pentax MX
🎞️Lucky SHD 400
#filmphotography #Photography #blackandwhite

It was raining the whole night, so I dreamt of checking if the utility building didn't get flooded. I didn't want to get soaked in the rain, so I considering jumping there by pure thought. I succeeded. And indeed, water was already seeping in the corner, so I've started wondering where the drainage pump were. "Probably in the garage", I thought. The garage is locked. The are inside the house. The house is also locked, from inside. I didn't prepare well for this teleportation, and I didn't feel like I can jump back the same way…
#dream

Perception of Brain-Computer Interface Implantation Surgery for Motor, Sensory, and Autonomic Restoration in Spinal Cord Injury and Stroke
Derrick Lin, Tracie Tran, Shravan Thaploo, Jose Gabrielle E. Matias, Joy E. Pixley, Zoran Nenadic, An H. Do
https://arxiv.org/abs/2507.11572

Perception of Brain-Computer Interface Implantation Surgery for Motor, Sensory, and Autonomic Restoration in Spinal Cord Injury and Stroke
(Abridged) Stroke and SCI are conditions that can significantly impact the QoL of survivors in both the physical and psychosocial domains. Both diseases often result in significant motor and sensory impairments that are not fully reversible despite current available therapies. Invasive BCIs have emerged as a promising means to bypass the site of injury and potentially restore motor and sensory function. However, to maximize the utility and participant satisfaction with such technology, particip…

Learning Mechanistic Subtypes of Neurodegeneration with a Physics-Informed Variational Autoencoder Mixture Model
Sanduni Pinnawala, Annabelle Hartanto, Ivor J. A. Simpson, Peter A. Wijeratne
https://arxiv.org/abs/2509.15124

Learning Mechanistic Subtypes of Neurodegeneration with a Physics-Informed Variational Autoencoder Mixture Model
Modelling the underlying mechanisms of neurodegenerative diseases demands methods that capture heterogeneous and spatially varying dynamics from sparse, high-dimensional neuroimaging data. Integrating partial differential equation (PDE) based physics knowledge with machine learning provides enhanced interpretability and utility over classic numerical methods. However, current physics-integrated machine learning methods are limited to considering a single PDE, severely limiting their application…

When is it (im)possible to respect all individuals' preferences under uncertainty?
Kensei Nakamura
https://arxiv.org/abs/2508.12542 https://arxiv.org/p…

When is it (im)possible to respect all individuals' preferences under uncertainty?
When aggregating Subjective Expected Utility preferences, the Pareto principle leads to an impossibility result unless the individuals have a common belief. This paper examines the source of this impossibility in more detail by considering the aggregation of a general class of incomplete preferences that can represent gradual ambiguity perceptions. Our result shows that the planner cannot avoid ignoring some individuals unless there is a probability distribution that all individuals agree is mo…

The Hidden Cost of Correlation: Rethinking Privacy Leakage in Local Differential Privacy
Sandaru Jayawardana, Sennur Ulukus, Ming Ding, Kanchana Thilakarathna
https://arxiv.org/abs/2508.12539

The Hidden Cost of Correlation: Rethinking Privacy Leakage in Local Differential Privacy
Local differential privacy (LDP) has emerged as a promising paradigm for privacy-preserving data collection in distributed systems, where users contribute multi-dimensional records with potentially correlated attributes. Recent work has highlighted that correlation-induced privacy leakage (CPL) plays a critical role in shaping the privacy-utility trade-off under LDP, especially when correlations exist among attributes. Nevertheless, it remains unclear to what extent the prevailing assumptions a…

Reconciling Risk-Aversion Paradoxes in the Distribution-Free Newsvendor Problem: Scarf's Rule Meets Dual Utility
Jonathan Yu-Meng Li, Tiantian Mao, Reza Valimoradi
https://arxiv.org/abs/2507.10735

Reconciling Risk-Aversion Paradoxes in the Distribution-Free Newsvendor Problem: Scarf's Rule Meets Dual Utility
How should a risk-averse newsvendor order optimally under distributional ambiguity? Attempts to extend Scarf's celebrated distribution-free ordering rule using risk measures have led to conflicting prescriptions: CVaR-based models invariably recommend ordering less as risk aversion increases, while mean-standard deviation models -- paradoxically -- suggest ordering more, particularly when ordering costs are high. We resolve this behavioral paradox through a coherent generalization of Scarf's di…

DOE taps 10 advanced reactor companies for expedited nuclear pilot | Utility Dive
https://www.utilitydive.com/news/doe-names-11-advanced-reactor-projects-for-rapid-deployment/757535/

"Renewables reach record 76% of demand on Australia’s main grid"
#Australia #Renewables #Energy

Renewables reach record 76% of demand on Australia’s main grid
The share of renewables in Australia’s main electricity grid has reached a new high with rooftop and utility-scale solar, wind and hydro combining to deliver 76.4% of electricity demand on Monday.

End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRI
Yihong Chen, Jiancheng Yang, Deniz Sayin Mercadier, Hieu Le, Juerg Schwitter, Pascal Fua
https://arxiv.org/abs/2509.12090

End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRI
Reconstructing cardiac motion from cine CMR sequences is critical for diagnosis, prediction, and intervention. Existing methods rely on complete CMR stacks to infer full heart motion, limiting their utility in intra-procedural scenarios where only sparse observations are available. We present TetHeart, the first end-to-end framework that unifies full 4D multi-structure heart mesh recovery from both offline full-stack acquisitions and intra-procedural sparse-slice observations. Our method levera…

Energy companies in Vermont offer major rebates on money-saving appliances. Some families can get appliances at little to no cost, while others benefit from discounts on battery-storage systems and electric-vehicle charging.
https://www.thecooldown.com/green-home/ver

Energy companies offer homeowners major rebates on money-saving appliances: 'It saved potentially up to $3 million in one hour'
Local utility companies in Vermont could help you reduce your electricity bill while also keeping your home prepared for outages.

@… The only downside with relying on the dialog utility is that it very often is not installed by default. So I spent 66 lines of shell early on doing distro detection and telling the user how to install it. Here is that snippet so you get the idea. 😂

Towards a Utility-Scale Quantum Edge Detection for Real-World Medical Image Data
Emmanuel Billias, Nikos Chrisochoides
https://arxiv.org/abs/2507.10939 htt…

Towards a Utility-Scale Quantum Edge Detection for Real-World Medical Image Data
We present a two-level decomposition strategy to enhance the quality and performance of Quantum Hadamard Edge Detection (QHED) for practical image analysis on Noisy Intermediate-Scale Quantum (NISQ) devices. A Data-Level Decomposition partitions an input image into P augmented sub-images, each encoded into a separate quantum circuit. Each of these circuits is then further cut via Circuit-Level Decomposition into Q smaller sub-circuits suitable for execution on near-term quantum devices. The two…

All Models Are Wrong, But Can They Be Useful? Lessons from COVID-19 Agent-Based Models: A Systematic Review
Emma Von Hoene, Sara Von Hoene, Szandra Peter, Ethan Hopson, Emily Csizmadia, Faith Fenyk, Kai Barner, Timothy Leslie, Hamdi Kavak, Andreas Zufle, Amira Roess, Taylor Anderson
https://arxiv.org/abs/2509.13346

All Models Are Wrong, But Can They Be Useful? Lessons from COVID-19 Agent-Based Models: A Systematic Review
The COVID-19 pandemic prompted a surge in computational models to simulate disease dynamics and guide interventions. Agent-based models (ABMs) are well-suited to capture population and environmental heterogeneity, but their rapid deployment raised questions about utility for health policy. We systematically reviewed 536 COVID-19 ABM studies published from January 2020 to December 2023, retrieved from Web of Science, PubMed, and Wiley on January 30, 2024. Studies were included if they used ABMs …

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection
Zhanhong Jiang, Md Zahid Hasan, Nastaran Saadati, Aditya Balu, Chao Liu, Soumik Sarkar
https://arxiv.org/abs/2509.09485

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection
Stochastic optimization is a pivotal enabler in modern machine learning, producing effective models for various tasks. However, several existing works have shown that model parameters and gradient information are susceptible to privacy leakage. Although Differentially Private SGD (DPSGD) addresses privacy concerns, its static noise mechanism impacts the error bounds for model performance. Additionally, with the exponential increase in model parameters, efficient learning of these models using s…

Simulating Clinical AI Assistance using Multimodal LLMs: A Case Study in Diabetic Retinopathy
Nadim Barakat, William Lotter
https://arxiv.org/abs/2509.13234 https://

Simulating Clinical AI Assistance using Multimodal LLMs: A Case Study in Diabetic Retinopathy
Diabetic retinopathy (DR) is a leading cause of blindness worldwide, and AI systems can expand access to fundus photography screening. Current FDA-cleared systems primarily provide binary referral outputs, where this minimal output may limit clinical trust and utility. Yet, determining the most effective output format to enhance clinician-AI performance is an empirical challenge that is difficult to assess at scale. We evaluated multimodal large language models (MLLMs) for DR detection and thei…

Case-Based Decision-Theoretic Decoding with Quality Memories
Hiroyuki Deguchi, Masaaki Nagata
https://arxiv.org/abs/2509.12677 https://arxiv.org/pdf/2509.1…

Case-Based Decision-Theoretic Decoding with Quality Memories
Minimum Bayes risk (MBR) decoding is a decision rule of text generation, which selects the hypothesis that maximizes the expected utility and robustly generates higher-quality texts than maximum a posteriori (MAP) decoding. However, it depends on sample texts drawn from the text generation model; thus, it is difficult to find a hypothesis that correctly captures the knowledge or information of out-of-domain. To tackle this issue, we propose case-based decision-theoretic (CBDT) decoding, another…

Crosslisted article(s) found for cs.CY. https://arxiv.org/list/cs.CY/new
[1/2]:
- Bilevel subsidy-enabled mobility hub network design with perturbed utility coalitional choice-bas...
Hai Yang, Joseph Y. J. Chow

On QoE-Aware Traffic Management for Real-time, Interactive Video with Time-variant Spatial Complexity
Szilveszter N\'adas, Lars Ernstr\"om, David Lindero, Jonathan Lynam
https://arxiv.org/abs/2507.11798

On QoE-Aware Traffic Management for Real-time, Interactive Video with Time-variant Spatial Complexity
We analyzed spatial complexity, defined as the relationship between the required bitrate and a corresponding picture Quality of Experience (QoE) metric, for realistic, long, real-time, interactive video clips. Apart from variation across different content types, e.g., game genres, we discovered time-variability within a clip from second to second, and explored the ramifications for traffic management. We introduced utility as an elegant way to manage resource sharing preferences. Our analysis o…

Karmic Historiography Forum on The Immanent Frame
https://ift.tt/Hxtq25V
University of Alaska - Fairbanks - Alaska & Polar Regions Area Studies Bibliographer & Curator of…
via Input 4 RELCFP

Requirements for Early Quantum Advantage and Quantum Utility in the Capacitated Vehicle Routing Problem
Chinonso Onah, Kristel Michielsen
https://arxiv.org/abs/2509.11469 https:…

Requirements for Early Quantum Advantage and Quantum Utility in the Capacitated Vehicle Routing Problem
We introduce a transparent, encoding-agnostic framework for determining when the Capacitated Vehicle Routing Problem (CVRP) can achieve early quantum advantage. Our analysis shows this is unlikely on noisy intermediate scale quantum (NISQ) hardware even in best case scenarios that use the most qubit-efficient direct encodings. Closed-form resource counts, combined with recent device benchmarks, yield three decisive go/no-go figures of merit: the quantum feasibility point and the qubit- and gate…

Forget What's Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning
Bihao Zhan, Jie Zhou, Junsong Li, Yutao Yang, Shilian Chen, Qianjun Pan, Xin Li, Wen Wu, Xingjiao Wu, Qin Chen, Hang Yan, Liang He
https://arxiv.org/abs/2509.12958

Forget What's Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning
Continual Learning (CL) models, while adept at sequential knowledge acquisition, face significant and often overlooked privacy challenges due to accumulating diverse information. Traditional privacy methods, like a uniform Differential Privacy (DP) budget, indiscriminately protect all data, leading to substantial model utility degradation and hindering CL deployment in privacy-sensitive areas. To overcome this, we propose a privacy-enhanced continual learning (PeCL) framework that forgets what'…

Benchmarking and Improving LVLMs on Event Extraction from Multimedia Documents
Fuyu Xing, Zimu Wang, Wei Wang, Haiyang Zhang
https://arxiv.org/abs/2509.12876 https://

Benchmarking and Improving LVLMs on Event Extraction from Multimedia Documents
The proliferation of multimedia content necessitates the development of effective Multimedia Event Extraction (M2E2) systems. Though Large Vision-Language Models (LVLMs) have shown strong cross-modal capabilities, their utility in the M2E2 task remains underexplored. In this paper, we present the first systematic evaluation of representative LVLMs, including DeepSeek-VL2 and the Qwen-VL series, on the M2E2 dataset. Our evaluations cover text-only, image-only, and cross-media subtasks, assessed …

Practitioners' Perspectives on a Differential Privacy Deployment Registry
Priyanka Nanayakkara, Elena Ghazi, Salil Vadhan
https://arxiv.org/abs/2509.13509 https://

Practitioners' Perspectives on a Differential Privacy Deployment Registry
Differential privacy (DP) -- a principled approach to producing statistical data products with strong, mathematically provable privacy guarantees for the individuals in the underlying dataset -- has seen substantial adoption in practice over the past decade. Applying DP requires making several implementation decisions, each with significant impacts on data privacy and/or utility. Hence, to promote shared learning and accountability around DP deployments, Dwork, Kohli, and Mulligan (2019) propos…

Cut your cooling costs and reduce pollution with solar energy! Learn how to save money and the planet in the long run. #climatechange #climatesolutions #climate

Government analysis reveals projected increases in home cooling costs — here's how to beat quickly rising utility prices
As millions of homeowners watch their electricity bills rise with the blazing temperatures, experts have a recommendation to keep costs low — create a personal power plant with solar energy.

Quantum Visual Fields with Neural Amplitude Encoding
Shuteng Wang, Christian Theobalt, Vladislav Golyanik
https://arxiv.org/abs/2508.10900 https://arxiv.or…

Quantum Visual Fields with Neural Amplitude Encoding
Quantum Implicit Neural Representations (QINRs) include components for learning and execution on gate-based quantum computers. While QINRs recently emerged as a promising new paradigm, many challenges concerning their architecture and ansatz design, the utility of quantum-mechanical properties, training efficiency and the interplay with classical modules remain. This paper advances the field by introducing a new type of QINR for 2D image and 3D geometric field learning, which we collectively re…

A Practical Guide to using Pauli Path Simulators for Utility-Scale Quantum Experiments
Hrant Gharibyan, Siddharth Hariprakash, Mohammed Zuhair Mullath, Vincent P. Su
https://arxiv.org/abs/2507.10771

A Practical Guide to using Pauli Path Simulators for Utility-Scale Quantum Experiments
In this this paper we present an inexpensive protocol to perform runtime and memory estimation for large-scale experiments with Pauli Path simulators (PPS). Additionally, we propose a conceptually simple solution for studying whether PPS can be used as a scientific discovery tool, rather than reproducing existing answers. We start by analyzing the dynamics of the Pauli coefficients tracked in the Heisenberg picture. In addition to surprisingly generic convergence features of the Pauli coefficie…

Arizonans Protest State’s Largest Utility Abandoning Clean Energy Commitments - Inside Climate News
https://insideclimatenews.org/news/04092025/arizona-public-service-clean-energy-roll-back-protest/

Replaced article(s) found for econ.TH. https://arxiv.org/list/econ.TH/new
[1/1]:
- Beyond Scalars: Zonotope-Valued Utility for Representation of Multidimensional Incomplete Prefere...
Behrooz Moosavi Ramezanzadeh

Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest
Xiao Yang, Mehdi Ben Ayed, Longyu Zhao, Fan Zhou, Yuchen Shen, Abe Engle, Jinfeng Zhuang, Ling Leng, Jiajing Xu, Charles Rosenberg, Prathibha Deshikachar
https://arxiv.org/abs/2509.05292

Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest
The ranking utility function in an ad recommender system, which linearly combines predictions of various business goals, plays a central role in balancing values across the platform, advertisers, and users. Traditional manual tuning, while offering simplicity and interpretability, often yields suboptimal results due to its unprincipled tuning objectives, the vast amount of parameter combinations, and its lack of personalization and adaptability to seasonality. In this work, we propose a general…

MAUI: Reconstructing Private Client Data in Federated Transfer Learning
Ahaan Dabholkar, Atul Sharma, Z. Berkay Celik, Saurabh Bagchi
https://arxiv.org/abs/2509.11451 https://…

MAUI: Reconstructing Private Client Data in Federated Transfer Learning
Recent works in federated learning (FL) have shown the utility of leveraging transfer learning for balancing the benefits of FL and centralized learning. In this setting, federated training happens after a stable point has been reached through conventional training. Global model weights are first centrally pretrained by the server on a public dataset following which only the last few linear layers (the classification head) of the model are finetuned across clients. In this scenario, existing da…

Data-Efficient Quantum Noise Modeling via Machine Learning
Yanjun Ji, Marco Roth, David A. Kreplin, Ilia Polian, Frank K. Wilhelm
https://arxiv.org/abs/2509.12933 https://

Data-Efficient Quantum Noise Modeling via Machine Learning
Maximizing the computational utility of near-term quantum processors requires predictive noise models that inform robust, noise-aware compilation and error mitigation. Conventional models often fail to capture the complex error dynamics of real hardware or require prohibitive characterization overhead. We introduce a data-efficient, machine learning-based framework to construct accurate, parameterized noise models for superconducting quantum processors. Our approach circumvents costly character…

Replaced article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new
[1/1]:
- The Utility of the Virtual Imaging Trials Methodology for Objective Characterization of AI System...
Tushar, Dahal, Sotoudeh-Paima, Abadi, Segars, Samei, Lo

Coarse Addition and the St. Petersburg Paradox: A Heuristic Perspective
Takashi Izumo
https://arxiv.org/abs/2507.12475 https://arxiv.…

Coarse Addition and the St. Petersburg Paradox: A Heuristic Perspective
The St. Petersburg paradox presents a longstanding challenge in decision theory. It describes a game whose expected value is infinite, yet for which no rational finite stake can be determined. Traditional solutions introduce auxiliary assumptions, such as diminishing marginal utility, temporal discounting, or extended number systems. These methods often involve mathematical refinements that may not correspond to how people actually perceive or process numerical information. This paper explores …

RL-MoE: An Image-Based Privacy Preserving Approach In Intelligent Transportation System
Abdolazim Rezaei, Mehdi Sookhak, Mahboobeh Haghparast
https://arxiv.org/abs/2508.09186 ht…

RL-MoE: An Image-Based Privacy Preserving Approach In Intelligent Transportation System
The proliferation of AI-powered cameras in Intelligent Transportation Systems (ITS) creates a severe conflict between the need for rich visual data and the fundamental right to privacy. Existing privacy-preserving mechanisms, such as blurring or encryption, are often insufficient, creating an undesirable trade-off where either privacy is compromised against advanced reconstruction attacks or data utility is critically degraded. To resolve this impasse, we propose RL-MoE, a novel framework that …

Solar companies are stepping up their game to withstand severe hailstorms in Texas. Find out how they're improving panel durability and testing methods. #climatechange #climatesolutions #climate

Utility-Scale Solar Can Withstand Severe Hailstorms. Here’s How - Inside Climate News
With effective weather forecasting, testing with “hail cannons,” and an ability to go into “stow” mode, panels can tolerate run-ins with even large balls of ice.

Demonstration of a Logical Architecture Uniting Motion and In-Place Entanglement: Shor's Algorithm, Constant-Depth CNOT Ladder, and Many-Hypercube Code
Rich Rines, Benjamin Hall, Mariesa H. Teo, Joshua Viszlai, Daniel C. Cole, David Mason, Cameron Barker, Matt J. Bedalov, Matt Blakely, Tobias Bothwell, Caitlin Carnahan, Frederic T. Chong, Samuel Y. Eubanks, Brian Fields, Matthew Gillette, Palash Goiporia, Pranav Gokhale, Garrett T. Hickman, Marin Iliev, Eric B. Jones, Ryan A. Jones…

Demonstration of a Logical Architecture Uniting Motion and In-Place Entanglement: Shor's Algorithm, Constant-Depth CNOT Ladder, and Many-Hypercube Code
Logical qubits are considered an essential component for achieving utility-scale quantum computation. Multiple recent demonstrations of logical qubits on neutral atoms have relied on coherent qubit motion into entangling zones. However, this architecture requires motion prior to every entangling gate, incurring significant cost in wall clock runtime and motion-related error accumulation. We propose and experimentally realize an alternative architecture which unites qubit motion and in-place ent…

Computational Economics in Large Language Models: Exploring Model Behavior and Incentive Design under Resource Constraints
Sandeep Reddy, Kabir Khan, Rohit Patil, Ananya Chakraborty, Faizan A. Khan, Swati Kulkarni, Arjun Verma, Neha Singh
https://arxiv.org/abs/2508.10426

Computational Economics in Large Language Models: Exploring Model Behavior and Incentive Design under Resource Constraints
Large language models (LLMs) are limited by substantial computational cost. We introduce a "computational economics" framework that treats an LLM as an internal economy of resource-constrained agents (attention heads and neuron blocks) that must allocate scarce computation to maximize task utility. First, we show empirically that when computation is scarce, standard LLMs reallocate attention toward high-value tokens while preserving accuracy. Building on this observation, we propose an incentiv…

Human-Aligned Procedural Level Generation Reinforcement Learning via Text-Level-Sketch Shared Representation
In-Chang Baek, Seoyoung Lee, Sung-Hyun Kim, Geumhwan Hwang, KyungJoong Kim
https://arxiv.org/abs/2508.09860

Human-Aligned Procedural Level Generation Reinforcement Learning via Text-Level-Sketch Shared Representation
Human-aligned AI is a critical component of co-creativity, as it enables models to accurately interpret human intent and generate controllable outputs that align with design goals in collaborative content creation. This direction is especially relevant in procedural content generation via reinforcement learning (PCGRL), which is intended to serve as a tool for human designers. However, existing systems often fall short of exhibiting human-centered behavior, limiting the practical utility of AI-…

Adiabatic Cooling of Planar Motion in a Penning Trap Ion Crystal to Sub-Millikelvin Temperatures
Wes Johnson, Bryce Bullock, Athreya Shankar, John Zaris, John J. Bollinger, Scott E. Parker
https://arxiv.org/abs/2507.12429

Adiabatic Cooling of Planar Motion in a Penning Trap Ion Crystal to Sub-Millikelvin Temperatures
Two-dimensional planar ion crystals in a Penning trap are a platform for quantum information science experiments. However, the low-frequency planar modes of these crystals are not efficiently cooled by laser cooling, which can limit the utility of the drumhead modes for quantum information processing. Recently, it has been shown that nonlinear mode coupling can enhance the cooling of the low-frequency planar modes. Here, we demonstrate in numerical simulations that this coupling can be dynamica…

Aim High, Stay Private: Differentially Private Synthetic Data Enables Public Release of Behavioral Health Information with High Utility
Mohsen Ghasemizade, Juniper Lovato, Christopher M. Danforth, Peter Sheridan Dodds, Laura S. P. Bloomfield, Matthew Price, Team LEMURS, Joseph P. Near
https://arxiv.org/abs/2507.02971

Aim High, Stay Private: Differentially Private Synthetic Data Enables Public Release of Behavioral Health Information with High Utility
Sharing health and behavioral data raises significant privacy concerns, as conventional de-identification methods are susceptible to privacy attacks. Differential Privacy (DP) provides formal guarantees against re-identification risks, but practical implementation necessitates balancing privacy protection and the utility of data. We demonstrate the use of DP to protect individuals in a real behavioral health study, while making the data publicly available and retaining high utility for downst…

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…

Quantum Functional Information through the Evolution of Random Circuits
Rodrigo Pasti, Jonas Krause
https://arxiv.org/abs/2509.11409 https://arxiv.org/pdf/…

Quantum Functional Information through the Evolution of Random Circuits
We introduce Quantum Functional Information (QFI), a new metric to quantify the rarity and utility of quantum states and circuits. Unlike standard measures such as fidelity or entropy, QFI captures the balance between functionality and rarity within the Hilbert space. We validate QFI through two approaches: random circuit sampling and evolutionary algorithms guided by fidelity or QFI objectives. Random sampling shows that states with near-perfect fidelity are less informational than s…

A Game-Theoretic Foundation for Bitcoin's Price: A Security-Utility Equilibrium
Liang Chen
https://arxiv.org/abs/2508.06071 https://arxiv.org/pdf/2508.…

A Game-Theoretic Foundation for Bitcoin's Price: A Security-Utility Equilibrium
This paper introduces a structural game-theoretic model to value decentralized digital assets like Bitcoin. Instead of relying on speculative beliefs, it frames the asset's price within a Rational-Expectations Security-Utility Nash Equilibrium (RESUNE). This equilibrium is a fixed point where the market-clearing price dictates the hash rate through a free-entry mining model, which in turn endogenously sets the network's security. The security, defined as one minus the probability of a 51% attac…

Benchmarking quantum computers with any quantum algorithm
Stefan K. Seritan, Aditya Dhumuntarao, Aidan Q. Wilber-Gauthier, Kenneth M. Rudinger, Antonio E. Russo, Robin Blume-Kohout, Andrew D. Baczewski, Timothy Proctor
https://arxiv.org/abs/2508.05754

Benchmarking quantum computers with any quantum algorithm
Application-based benchmarks are increasingly used to quantify and compare quantum computers' performance. However, because contemporary quantum computers cannot run utility-scale computations, these benchmarks currently test this hardware's performance on ``small'' problem instances that are not necessarily representative of utility-scale problems. Furthermore, these benchmarks often employ methods that are unscalable, limiting their ability to track progress towards utility-scale applications…

A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProx
Rodrigo Tertulino
https://arxiv.org/abs/2508.10017 https…

A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProx
Federated Learning (FL) presents a groundbreaking approach for collaborative health research, allowing model training on decentralized data while safeguarding patient privacy. FL offers formal security guarantees when combined with Differential Privacy (DP). The integration of these technologies, however, introduces a significant trade-off between privacy and clinical utility, a challenge further complicated by the severe class imbalance often present in medical datasets. The research presented…

Comparative Studies of Quantum Annealing, Digital Annealing, and Classical Solvers for Reaction Network Pathway Analysis and mRNA Codon Selection
Milind Upadhyay, Mark Nicholas Jones
https://arxiv.org/abs/2509.09862

Comparative Studies of Quantum Annealing, Digital Annealing, and Classical Solvers for Reaction Network Pathway Analysis and mRNA Codon Selection
For various optimization problems, the classical time to solution is super-polynomial and intractable to solve with classical bit-based computing hardware to date. Digital and quantum annealers have the potential to identify near-optimal solutions for such optimization problems using a quadratic unconstrained binary optimization (QUBO) problem formulation. This work benchmarks two use cases to evaluate the utility of QUBO solvers for combinatorial optimization problems, in order to determine if…