Tootfinder

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/6]:
- StegOT: Trade-offs in Steganography via Optimal Transport
Chengde Lin, Xuezhu Gong, Shuxue Ding, Mingzhe Yang, Xijun Lu, Chengjun Mo

Security-Robustness Trade-offs in Diffusion Steganography: A Comparative Analysis of Pixel-Space and VAE-Based Architectures
Yuhua Xu, Wei Sun, Chengpei Tang, Jiaxing Lu, Jingying Zhou, Chen Gu
https://arxiv.org/abs/2510.07219

Security-Robustness Trade-offs in Diffusion Steganography: A Comparative Analysis of Pixel-Space and VAE-Based Architectures
Current generative steganography research mainly pursues computationally expensive mappings to perfect Gaussian priors within single diffusion model architectures. This work introduces an efficient framework based on approximate Gaussian mapping governed by a scale factor calibrated through capacity-aware adaptive optimization. Using this framework as a unified analytical tool, systematic comparative analysis of steganography in pixel-space models versus VAE-based latent-space systems is conduc…

Ultrafast Grid Impedance Identification in $dq$-Asymmetric Three-Phase Power Systems
Mohamed Abdalmoaty, Verena H\"aberle, Xiuqiang He, Florian D\"orfler
https://arxiv.org/abs/2510.12338

Ultrafast Grid Impedance Identification in $dq$-Asymmetric Three-Phase Power Systems
We propose a non-parametric frequency-domain method to identify small-signal $dq$-asymmetric grid impedances, over a wide frequency band, using grid-connected converters. Existing identification methods are faced with significant trade-offs: e.g., passive approaches rely on ambient harmonics and rare grid events and thus can only provide estimates at a few frequencies, while many active approaches that intentionally perturb grid operation require long time series measurement and specialized equ…

There are a lot of trade-offs with fossil fuel use that we definitely don't discuss enough. #PiperAlpha was the really big one I recall as a kid, but it was just the latest in a whole succession of oil platform accidents that cost a lot of lives and suffering in the North Sea. And then there was #DeepWaterHorizon.
And that's quite apart from the climate costs.
You wonder how many of these happen that we don't hear about too.
This is a good reminder.
https://www.theguardian.com/lifeandstyle/2025/oct/28/the-piper-alpha-oil-rig-exploded-and-collapsed-and-i-made-a-desperate-175ft-jump-into-the-sea

The Post-Electromagnetic Era: A Vision for Wireless Communication Beyond 6G
Shumaila Javaid, Nasir Saeed
https://arxiv.org/abs/2510.11097 https://arxiv.org…

The Post-Electromagnetic Era: A Vision for Wireless Communication Beyond 6G
Electromagnetic (EM) communication is nearing its physical and thermodynamic limits, where further performance gains through spectrum optimization alone have become increasingly unsustainable. Finite bandwidth, propagation loss at higher frequencies, and the inherent trade-offs between energy and information constrain the scalability of 6G and beyond systems. These limitations drive the search for alternative mechanisms for information transfer beyond conventional EM propagation. This work intr…

I’ve learned to effectively not see most of the ads that appear on web pages and videos that I look at. I really don’t see them somehow. I’ve been doing it since ads first appeared on the web, lo these many years ago. But I wonder occasionally if I might be missing something that’s not an ad that I probably should be seeing, or might want to see. Oh, well. There’s always trade-offs, I guess.

Comparing Open-Source and Commercial LLMs for Domain-Specific Analysis and Reporting: Software Engineering Challenges and Design Trade-offs
Theo Koraag, Niklas Wagner, Felix Dobslaw, Lucas Gren
https://arxiv.org/abs/2509.24344

Comparing Open-Source and Commercial LLMs for Domain-Specific Analysis and Reporting: Software Engineering Challenges and Design Trade-offs
Context: Large Language Models (LLMs) enable automation of complex natural language processing across domains, but research on domain-specific applications like Finance remains limited. Objectives: This study explored open-source and commercial LLMs for financial report analysis and commentary generation, focusing on software engineering challenges in implementation. Methods: Using Design Science Research methodology, an exploratory case study iteratively designed and evaluated two LLM-based sy…

🌍 The climate crisis demands urgent action. But which actions are best?
Decision makers face tough trade-offs:
Policy A lowers emissions at home but increases reliance on imports.
Policy B cuts emissions long-term but raises unemployment short-term.
Policy C boosts jobs now but increases emissions in the near term.
None of these choices are simple. A policy that looks good locally may increase global emissions, or its effects may depend on what other countries d…

Optimizing Phase-Scheduling with Throughput Trade-offs in AQFP Digital Circuits
Robert S. Aviles, Peter A. Beerel
https://arxiv.org/abs/2510.03956 https://…

Optimizing Phase-Scheduling with Throughput Trade-offs in AQFP Digital Circuits
Adiabatic Quantum-Flux-Parametron (AQFP) logic is a promising emerging superconducting technology for ultra-low power digital circuits, offering orders of magnitude lower power consumption than CMOS. However, AQFP scalability is challenged by excessive buffer overhead due to path balancing technology constraints. Addressing this, recent AQFP works have proposed design solutions to reduce path balancing overhead using phase-skipping and phase-alignment. Phase-skipping is a circuit-level techniqu…

ECO: Enhanced Code Optimization via Performance-Aware Prompting for Code-LLMs
Su-Hyeon Kim, Joonghyuk Hahn, Sooyoung Cha, Yo-Sub Han
https://arxiv.org/abs/2510.10517 https://

ECO: Enhanced Code Optimization via Performance-Aware Prompting for Code-LLMs
Code runtime optimization-the task of rewriting a given code to a faster one-remains challenging, as it requires reasoning about performance trade-offs involving algorithmic and structural choices. Recent approaches employ code-LLMs with slow-fast code pairs provided as optimization guidance, but such pair-based methods obscure the causal factors of performance gains and often lead to superficial pattern imitation rather than genuine performance reasoning. We introduce ECO, a performance-aware …

Federated Computation of ROC and PR Curves
Xuefeng Xu, Graham Cormode
https://arxiv.org/abs/2510.04979 https://arxiv.org/pdf/2510.04979

Federated Computation of ROC and PR Curves
Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are fundamental tools for evaluating machine learning classifiers, offering detailed insights into the trade-offs between true positive rate vs. false positive rate (ROC) or precision vs. recall (PR). However, in Federated Learning (FL) scenarios, where data is distributed across multiple clients, computing these curves is challenging due to privacy and communication constraints. Specifically, the server cannot access raw …

Constructions of Efficiently Implementable Boolean Functions with Provable Nonlinearity/Resiliency/Algebraic Immunity Trade-Offs
Palash Sarkar
https://arxiv.org/abs/2510.01720 h…

Constructions of Efficiently Implementable Boolean Functions with Provable Nonlinearity/Resiliency/Algebraic Immunity Trade-Offs
We describe several families of efficiently implementable Boolean functions achieving provable trade-offs between resiliency, nonlinearity, and algebraic immunity. In concrete terms, the following result holds for each of the function families that we propose. Given integers $m_0\geq 0$, $x_0\geq 1$, and $a_0\geq 1$, it is possible to construct an $n$-variable function which has resiliency at least $m_0$, linear bias (which is an equivalent method of expressing nonlinearity) at most $2^{-x_0}$ …

Collective is different: Information exchange and speed-accuracy trade-offs in self-organized patterning
Ashutosh Tripathi, J\"orn Dunkel, Dominic J. Skinner
https://arxiv.org/abs/2510.01397

Collective is different: Information exchange and speed-accuracy trade-offs in self-organized patterning
During development, highly ordered structures emerge as cells collectively coordinate with each other. While recent advances have clarified how individual cells process and respond to external signals, understanding collective cellular decision making remains a major challenge. Here, we introduce a minimal, analytically tractable, model of cell patterning via local cell-cell communication. Using this framework, we identify a trade-off between the speed and accuracy of collective pattern formati…

man debian packaging is so cursed with modern package managers. It's so oriented around everything being C-shaped libfoo-version.tar.gz files at the core, and everything else is forced into that hole.
I get _why_ but the trade-offs seem really bad now. I can see why Canonical said 'screw it, we're doing snaps’ for a lot of stuff. I can see why flatpak is being used. Of course those just head straight for the cliff of "Security updates? Nah" cliff.

Investigating Matrix Repartitioning to Address the Over- and Undersubscription Challenge for a GPU-based CFD Solver
Gregor Olenik, Marcel Koch, Hartwig Anzt
https://arxiv.org/abs/2510.08536

Investigating Matrix Repartitioning to Address the Over- and Undersubscription Challenge for a GPU-based CFD Solver
Modern high-performance computing (HPC) increasingly relies on GPUs, but integrating GPU acceleration into complex scientific frameworks like OpenFOAM remains a challenge. Existing approaches either fully refactor the codebase or use plugin-based GPU solvers, each facing trade-offs between performance and development effort. In this work, we address the limitations of plugin-based GPU acceleration in OpenFOAM by proposing a repartitioning strategy that better balances CPU matrix assembly and GP…

Near-optimal Rank Adaptive Inference of High Dimensional Matrices
Fr\'ed\'eric Zheng, Yassir Jedra, Alexandre Proutiere
https://arxiv.org/abs/2510.08117 https://

Near-optimal Rank Adaptive Inference of High Dimensional Matrices
We address the problem of estimating a high-dimensional matrix from linear measurements, with a focus on designing optimal rank-adaptive algorithms. These algorithms infer the matrix by estimating its singular values and the corresponding singular vectors up to an effective rank, adaptively determined based on the data. We establish instance-specific lower bounds for the sample complexity of such algorithms, uncovering fundamental trade-offs in selecting the effective rank: balancing the precis…

Constraint-Level Design of zkEVMs: Architectures, Trade-offs, and Evolution
Yahya Hassanzadeh-Nazarabadi, Sanaz Taheri-Boshrooyeh
https://arxiv.org/abs/2510.05376 https://

Constraint-Level Design of zkEVMs: Architectures, Trade-offs, and Evolution
Zero-knowledge Ethereum Virtual Machines (zkEVMs) must reconcile a fundamental contradiction: the Ethereum Virtual Machine was designed for transparent sequential execution, while zero-knowledge proofs require algebraic circuit representations. This survey provides the first systematic analysis of how existing major production zkEVM implementations resolve this tension through distinct constraint engineering strategies. We develop a comparative framework that maps the design space across three …

LABELING COPILOT: A Deep Research Agent for Automated Data Curation in Computer Vision
Debargha Ganguly, Sumit Kumar, Ishwar Balappanawar, Weicong Chen, Shashank Kambhatla, Srinivasan Iyengar, Shivkumar Kalyanaraman, Ponnurangam Kumaraguru, Vipin Chaudhary
https://arxiv.org/abs/2509.22631

LABELING COPILOT: A Deep Research Agent for Automated Data Curation in Computer Vision
Curating high-quality, domain-specific datasets is a major bottleneck for deploying robust vision systems, requiring complex trade-offs between data quality, diversity, and cost when researching vast, unlabeled data lakes. We introduce Labeling Copilot, the first data curation deep research agent for computer vision. A central orchestrator agent, powered by a large multimodal language model, uses multi-step reasoning to execute specialized tools across three core capabilities: (1) Calibrated Di…

Learning to Parallel: Accelerating Diffusion Large Language Models via Adaptive Parallel Decoding
Wenrui Bao, Zhiben Chen, Dan Xu, Yuzhang Shang
https://arxiv.org/abs/2509.25188

Learning to Parallel: Accelerating Diffusion Large Language Models via Adaptive Parallel Decoding
Autoregressive decoding in large language models (LLMs) requires $\mathcal{O}(n)$ sequential steps for $n$ tokens, fundamentally limiting inference throughput. Recent diffusion-based LLMs (dLLMs) enable parallel token generation through iterative denoising. However, current parallel decoding strategies rely on fixed, input-agnostic heuristics (e.g., confidence thresholds), which fail to adapt to input-specific characteristics, resulting in suboptimal speed-quality trade-offs across diverse NLP …

Performance Index Shaping for Closed-loop Optimal Control
Ayush Rai, Shaoshuai Mou, Brian D. O. Anderson
https://arxiv.org/abs/2510.10202 https://arxiv.org…

Performance Index Shaping for Closed-loop Optimal Control
The design of the performance index, also referred to as cost or reward shaping, is central to both optimal control and reinforcement learning, as it directly determines the behaviors, trade-offs, and objectives that the resulting control laws seek to achieve. A commonly used approach for this inference task in recent years is differentiable trajectory optimization, which allows gradients to be computed with respect to cost parameters by differentiating through an optimal control solver. Howeve…

Pareto-optimality of Majoranas in hybrid platforms
Juan Daniel Torres Luna, Sebastian Miles, A. Mert Bozkurt, Chun-Xiao Liu, Antonio L. R. Manesco, Anton R. Akhmerov, Michael Wimmer
https://arxiv.org/abs/2510.07406

Pareto-optimality of Majoranas in hybrid platforms
To observe Majorana bound states, and especially to use them as a qubit, requires careful optimization of competing quality metrics. We systematically compare Majorana quality in proximitized semiconductor nanowires and quantum dot chains. Using multi-objective optimization, we analyze the fundamental trade-offs between topological gap and localization length, two key metrics that determine MBS coherence and operational fidelity. We demonstrate that these quantities cannot be simultaneously opt…

Multi-Objective Multi-Agent Path Finding with Lexicographic Cost Preferences
Pulkit Rustagi, Kyle Hollins Wray, Sandhya Saisubramanian
https://arxiv.org/abs/2510.07276 https://

Multi-Objective Multi-Agent Path Finding with Lexicographic Cost Preferences
Many real-world scenarios require multiple agents to coordinate in shared environments, while balancing trade-offs between multiple, potentially competing objectives. Current multi-objective multi-agent path finding (MO-MAPF) algorithms typically produce conflict-free plans by computing Pareto frontiers. They do not explicitly optimize for user-defined preferences, even when the preferences are available, and scale poorly with the number of objectives. We propose a lexicographic framework for m…

Fair Minimum Labeling: Efficient Temporal Network Activations for Reachability and Equity
Lutz Oettershagen, Othon Michail
https://arxiv.org/abs/2510.03899 https://

Fair Minimum Labeling: Efficient Temporal Network Activations for Reachability and Equity
Balancing resource efficiency and fairness is critical in networked systems that support modern learning applications. We introduce the Fair Minimum Labeling (FML) problem: the task of designing a minimum-cost temporal edge activation plan that ensures each group of nodes in a network has sufficient access to a designated target set, according to specified coverage requirements. FML captures key trade-offs in systems where edge activations incur resource costs and equitable access is essential,…

Multi-bit Audio Watermarking
Luca A. Lanzend\"orfer, Kyle Fearne, Florian Gr\"otschla, Roger Wattenhofer
https://arxiv.org/abs/2510.01968 https://

Multi-bit Audio Watermarking
We present Timbru, a post-hoc audio watermarking model that achieves state-of-the-art robustness and imperceptibility trade-offs without training an embedder-detector model. Given any 44.1 kHz stereo music snippet, our method performs per-audio gradient optimization to add imperceptible perturbations in the latent space of a pretrained audio VAE, guided by a combined message and perceptual loss. The watermark can then be extracted using a pretrained CLAP model. We evaluate 16-bit watermarking o…

Normalizing Flows are Capable Visuomotor Policy Learning Models
Simon Kristoffersson Lind, Jialong Li, Maj Stenmark, Volker Kr\"uger
https://arxiv.org/abs/2509.21073 https:…

Normalizing Flows are Capable Visuomotor Policy Learning Models
The field of general purpose robotics has recently embraced powerful probabilistic models, such as diffusion models, to model and learn complex behaviors. However, these models often come with significant trade-offs, namely high computational costs for inference and a fundamental inability to quantify output uncertainty. We argue that a model's trustworthiness, a critical factor for reliable, general-purpose robotics, is inherently linked to its ability to provide confidence measures. In this…

Managing ride-sourcing drivers at transportation terminals: a lottery-based queueing approach
Tianming Liu, Yafeng Yin, Vijay Subramanian
https://arxiv.org/abs/2509.25071 https:…

Managing ride-sourcing drivers at transportation terminals: a lottery-based queueing approach
Problem definition: Transportation terminals such as airports often experience persistent oversupply of idle ride-sourcing drivers, resulting in long driver waiting times and inducing externalities such as curbside congestion. While platforms now employ virtual queues with control levers like dynamic pricing, information provision, and direct admission control to manage this issue, all existing levers involve significant trade-offs and side effects. This limitation highlights the need for an al…

Bias-Reduced Estimation of Structural Equation Models
Haziq Jamil, Yves Rosseel, Oliver Kemp, Ioannis Kosmidis
https://arxiv.org/abs/2509.25419 https://arx…

Bias-Reduced Estimation of Structural Equation Models
Finite-sample bias is a pervasive challenge in the estimation of structural equation models (SEMs), especially when sample sizes are small or measurement reliability is low. A range of methods have been proposed to improve finite-sample bias in the SEM literature, ranging from analytic bias corrections to resampling-based techniques, with each carrying trade-offs in scope, computational burden, and statistical performance. We apply the reduced-bias M-estimation framework (RBM, Kosmidis & Lunard…

Preference-Guided Multi-Objective UI Adaptation
Yao Song, Christoph Gebhardt, Yi-Chi Liao, Christian Holz
https://arxiv.org/abs/2509.18960 https://arxiv.or…

Balancing Cost Savings and Import Dependence in Germany's Industry Transformation
Toni Seibold, Fabian Neumann, Falko Ueckerdt, Tom Brown
https://arxiv.org/abs/2510.00918 ht…

Balancing Cost Savings and Import Dependence in Germany's Industry Transformation
Greenhouse gas emissions from the steel, fertiliser and plastic industries can be mitigated by producing their precursors with green hydrogen. In Germany, green production may be economically unviable due to high energy costs. This study quantifies the 'renewables pull' of cheaper production abroad and high-lights trade-offs between cost savings and import dependence. Using a detailed European energy system model coupled to global supply curves for hydrogen and industry precursors (hot briquett…

Lumos: Performance Characterization of WebAssembly as a Serverless Runtime in the Edge-Cloud Continuum
Cynthia Marcelino, Noah Krennmair, Thomas Pusztai, Stefan Nastic
https://arxiv.org/abs/2510.05118 …

Lumos: Performance Characterization of WebAssembly as a Serverless Runtime in the Edge-Cloud Continuum
WebAssembly has emerged as a lightweight and portable runtime to execute serverless functions, particularly in heterogeneous and resource-constrained environments such as the Edge Cloud Continuum. However, the performance benefits versus trade-offs remain insufficiently understood. This paper presents Lumos, a performance model and benchmarking tool for characterizing serverless runtimes. Lumos identifies workload, system, and environment-level performance drivers in the Edge-Cloud Continuum. W…

DELM: a Python toolkit for Data Extraction with Language Models
Eric Fithian, Kirill Skobelev
https://arxiv.org/abs/2509.20617 https://arxiv.org/pdf/2509.2…

DELM: a Python toolkit for Data Extraction with Language Models
Large Language Models (LLMs) have become powerful tools for annotating unstructured data. However, most existing workflows rely on ad hoc scripts, making reproducibility, robustness, and systematic evaluation difficult. To address these challenges, we introduce DELM (Data Extraction with Language Models), an open-source Python toolkit designed for rapid experimental iteration of LLM-based data extraction pipelines and for quantifying the trade-offs between them. DELM minimizes boilerplate code …

How to Get Actual Privacy and Utility from Privacy Models: the k-Anonymity and Differential Privacy Families
Josep Domingo-Ferrer, David S\'anchez
https://arxiv.org/abs/2510.11299

How to Get Actual Privacy and Utility from Privacy Models: the k-Anonymity and Differential Privacy Families
Privacy models were introduced in privacy-preserving data publishing and statistical disclosure control with the promise to end the need for costly empirical assessment of disclosure risk. We examine how well this promise is kept by the main privacy models. We find they may fail to provide adequate protection guarantees because of problems in their definition or incur unacceptable trade-offs between privacy protection and utility preservation. Specifically, k-anonymity may not entirely exclude …

The Pareto Frontier of Resilient Jet Tagging
Rikab Gambhir, Matt LeBlanc, Yuanchen Zhou
https://arxiv.org/abs/2509.19431 https://arxiv.org/pdf/2509.19431…

The Pareto Frontier of Resilient Jet Tagging
Classifying hadronic jets using their constituents' kinematic information is a critical task in modern high-energy collider physics. Often, classifiers are designed by targeting the best performance using metrics such as accuracy, AUC, or rejection rates. However, the use of a single metric can lead to the use of architectures that are more model-dependent than competitive alternatives, leading to potential uncertainty and bias in analysis. We explore such trade-offs and demonstrate the consequ…

Game-Theoretic Understandings of Multi-Agent Systems with Multiple Objectives
Yue Wang
https://arxiv.org/abs/2509.23026 https://arxiv.org/pdf/2509.23026

Game-Theoretic Understandings of Multi-Agent Systems with Multiple Objectives
In practical multi-agent systems, agents often have diverse objectives, which makes the system more complex, as each agent's performance across multiple criteria depends on the joint actions of all agents, creating intricate strategic trade-offs. To address this, we introduce the Multi-Objective Markov Game (MOMG), a framework for multi-agent reinforcement learning with multiple objectives. We propose the Pareto-Nash Equilibrium (PNE) as the primary solution concept, where no agent can unilater…

Data-driven modeling of wind farm wake flow based on multi-scale feature recognition
Dong Xu, Zhaobin Li, Xiaolei Yang, Peng Hou, Bruno Carmo, Xuerui Mao
https://arxiv.org/abs/2509.21005

Data-driven modeling of wind farm wake flow based on multi-scale feature recognition
Accurate, efficient prediction of wind flow with wake effects is crucial for wind-farm layout and power forecasting. Existing approaches-physical measurements, numerical simulations, physics-based models, and data-driven models-face trade-offs: the first two are time- and resource-intensive; physics-based models can lack accuracy due to limited physics; data-driven methods leverage abundant, high-quality data and are increasingly popular. We propose a rapid, data-driven wake-flow model inspired…

Challenges and Solutions in Selecting Optimal Lossless Data Compression Algorithms
Md. Atiqur Rahman, MM Fazle Rabbi
https://arxiv.org/abs/2509.25219 https://

Challenges and Solutions in Selecting Optimal Lossless Data Compression Algorithms
The rapid growth of digital data has heightened the demand for efficient lossless compression methods. However, existing algorithms exhibit trade-offs: some achieve high compression ratios, others excel in encoding or decoding speed, and none consistently perform best across all dimensions. This mismatch complicates algorithm selection for applications where multiple performance metrics are simultaneously critical, such as medical imaging, which requires both compact storage and fast retrieval.…

OptPipe: Memory- and Scheduling-Optimized Pipeline Parallelism for LLM Training
Hongpei Li, Han Zhang, Huikang Liu, Dongdong Ge, Yinyu Ye
https://arxiv.org/abs/2510.05186 https:…

OptPipe: Memory- and Scheduling-Optimized Pipeline Parallelism for LLM Training
Pipeline parallelism (PP) has become a standard technique for scaling large language model (LLM) training across multiple devices. However, despite recent progress in reducing memory consumption through activation offloading, existing approaches remain largely heuristic and coarse-grained, often overlooking the fine-grained trade-offs between memory, computation, and scheduling latency. In this work, we revisit the pipeline scheduling problem from a principled optimization perspective. We obser…

Crosslisted article(s) found for nlin.AO. https://arxiv.org/list/nlin.AO/new
[1/1]:
- Collective is different: Information exchange and speed-accuracy trade-offs in self-organized pat...
Ashutosh Tripathi, J\"orn Dunkel, Dominic J. Skinner

Bilayer Cuprate Antiferromagnets Enable Programmable Cavity Optomagnonics
Tahereh Sadat Parvini
https://arxiv.org/abs/2509.17173 https://arxiv.org/pdf/2509…

Bilayer Cuprate Antiferromagnets Enable Programmable Cavity Optomagnonics
Hybrid platforms that couple microwave photons to collective spin excitations offer promising routes for coherent information processing, yet conventional magnets face inherent trade-offs among coupling strength, coherence, and tunability. We demonstrate that bilayer cuprate antiferromagnets, exemplified by YBa2Cu3O6+x, provide an alternative approach enabled by their unique magnon spectrum. Using a neutron-constrained bilayer spin model, we obtain the complete Gamma-point spectrum and identify…

Selective Classifier-free Guidance for Zero-shot Text-to-speech
John Zheng, Farhad Maleki
https://arxiv.org/abs/2509.19668 https://arxiv.org/pdf/2509.19668…

Selective Classifier-free Guidance for Zero-shot Text-to-speech
In zero-shot text-to-speech, achieving a balance between fidelity to the target speaker and adherence to text content remains a challenge. While classifier-free guidance (CFG) strategies have shown promising results in image generation, their application to speech synthesis are underexplored. Separating the conditions used for CFG enables trade-offs between different desired characteristics in speech synthesis. In this paper, we evaluate the adaptability of CFG strategies originally developed f…

A Multiobjective Mathematical Model for Optimal Irrigation Water Allocation
Nahid Sultana, M. M. Rizvi, G. M. Wali Ullah
https://arxiv.org/abs/2509.19629 https://

A Multiobjective Mathematical Model for Optimal Irrigation Water Allocation
Water resource management is vital for sustainable irrigation and food security under growing climatic and economic pressures. In this study, we develop two single-objective optimization models for irrigation planning, one that maximizes net benefit and the other that minimizes environmental flow deficiency, and benchmark them against previous works. We then extend the analysis to a multiobjective linear programming formulation solved through different approaches to evaluate trade-offs. Numeric…

Secret Leader Election in Ethereum PoS: An Empirical Security Analysis of Whisk and Homomorphic Sortition under DoS on the Leader and Censorship Attacks
Tereza Burianov\'a, Martin Pere\v{s}\'ini, Ivan Homoliak
https://arxiv.org/abs/2509.24955

Secret Leader Election in Ethereum PoS: An Empirical Security Analysis of Whisk and Homomorphic Sortition under DoS on the Leader and Censorship Attacks
Proposer anonymity in Proof-of-Stake (PoS) blockchains is a critical concern due to the risk of targeted attacks such as malicious denial-of-service (DoS) and censorship attacks. While several Secret Single Leader Election (SSLE) mechanisms have been proposed to address these threats, their practical impact and trade-offs remain insufficiently explored. In this work, we present a unified experimental framework for evaluating SSLE mechanisms under adversarial conditions, grounded in a simplified…

Methods for Multi-objective Optimization PID Controller for quadrotor UAVs
Andrea Vaiuso, Gabriele Immordino, Ludovica Onofri, Giuliano Coppotelli, Marcello Righi
https://arxiv.org/abs/2509.17423

Methods for Multi-objective Optimization PID Controller for quadrotor UAVs
Integrating unmanned aerial vehicles into daily use requires controllers that ensure stable flight, efficient energy use, and reduced noise. Proportional integral derivative controllers remain standard but are highly sensitive to gain selection, with manual tuning often yielding suboptimal trade-offs. This paper studies different optimization techniques for the automated tuning of quadrotor proportional integral derivative gains under a unified simulation that couples a blade element momentum b…

Pagoda: An Energy and Time Roofline Study for DNN Workloads on Edge Accelerators
Prashanthi S. K., Kunal Kumar Sahoo, Amartya Ranjan Saikia, Pranav Gupta, Atharva Vinay Joshi, Priyanshu Pansari, Yogesh Simmhan
https://arxiv.org/abs/2509.20189

Pagoda: An Energy and Time Roofline Study for DNN Workloads on Edge Accelerators
Edge accelerators such as Nvidia Jetsons are becoming an integral part of the computing continuum, and are often used for DNN inferencing and training. Nvidia Jetson edge devices have $2000$+ CUDA cores within a $70$W power envelope and offer $1000$s of power modes to customize CPU, GPU and memory frequencies. Their widely varying power--performance trade-offs can be exploited for energy and power-constrained deployments. While data-driven methods to predict the power and latency of DNN workloa…

Not My Agent, Not My Boundary? Elicitation of Personal Privacy Boundaries in AI-Delegated Information Sharing
Bingcan Guo, Eryue Xu, Zhiping Zhang, Tianshi Li
https://arxiv.org/abs/2509.21712

Not My Agent, Not My Boundary? Elicitation of Personal Privacy Boundaries in AI-Delegated Information Sharing
Aligning AI systems with human privacy preferences requires understanding individuals' nuanced disclosure behaviors beyond general norms. Yet eliciting such boundaries remains challenging due to the context-dependent nature of privacy decisions and the complex trade-offs involved. We present an AI-powered elicitation approach that probes individuals' privacy boundaries through a discriminative task. We conducted a between-subjects study that systematically varied communication roles and delegat…