Tootfinder

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss
Petr Karnakov, Lucas Amoudruz, Petros Koumoutsakos
https://arxiv.org/abs/2506.15902

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss
Optimal path planning and control of microscopic devices navigating in fluid environments is essential for applications ranging from targeted drug delivery to environmental monitoring. These tasks are challenging due to the complexity of microdevice-flow interactions. We introduce a closed-loop control method that optimizes a discrete loss (ODIL) in terms of dynamics and path objectives. In comparison with reinforcement learning, ODIL is more robust, up to three orders faster, and excels in hig…

HOIDiNi: Human-Object Interaction through Diffusion Noise Optimization
Roey Ron, Guy Tevet, Haim Sawdayee, Amit H. Bermano
https://arxiv.org/abs/2506.15625

HOIDiNi: Human-Object Interaction through Diffusion Noise Optimization
We present HOIDiNi, a text-driven diffusion framework for synthesizing realistic and plausible human-object interaction (HOI). HOI generation is extremely challenging since it induces strict contact accuracies alongside a diverse motion manifold. While current literature trades off between realism and physical correctness, HOIDiNi optimizes directly in the noise space of a pretrained diffusion model using Diffusion Noise Optimization (DNO), achieving both. This is made feasible thanks to our ob…

Near-field optical mode engineering-enabled freeform nonlocal metasurfaces
Zhongjun Jiang, Tianxiang Dai, Shuwei Guo, Soyaib Sohag, Yixuan Shao, Chenkai Mao, Andrea Al\`u, Jonathan A. Fan, You Zhou
https://arxiv.org/abs/2506.15495

Near-field optical mode engineering-enabled freeform nonlocal metasurfaces
Nanophotonic technologies inherently rely on tailoring light-matter interactions through the excitation and interference of deeply confined optical resonances. However, existing concepts in optical mode engineering remain heuristic and are challenging to extend towards complex and multi-functional resonant phenomena. Here, we introduce an inverse design framework that optimizes near-field distributions, ideally suited to tailor Mie-type modes within dielectric nanophotonic structures, and we de…

Optimal Experimental Design Criteria for Data-Consistent Inversion
Troy Butler, John Jakeman, Michael Pilosov, Scott Walsh, Timothy Wildey
https://arxiv.org/abs/2506.12157

Optimal Experimental Design Criteria for Data-Consistent Inversion
The ability to design effective experiments is crucial for obtaining data that can substantially reduce the uncertainty in the predictions made using computational models. An optimal experimental design (OED) refers to the choice of a particular experiment that optimizes a particular design criteria, e.g., maximizing a utility function, which measures the information content of the data. However, traditional approaches for optimal experimental design typically require solving a large number of …

Techno-economic optimization of hybrid steam-electric energy systems with excess heat utilization and reserve market participation
Olav Galteland, Jacob Hadler-Jacobsen, Hanne Kauko
https://arxiv.org/abs/2506.14252

Techno-economic optimization of hybrid steam-electric energy systems with excess heat utilization and reserve market participation
This study investigates the economic viability and optimal configuration of a hybrid industrial energy system combining an electrode boiler, steam accumulator, and battery energy storage system (BESS). This study optimizes system operation for a specific configuration to minimize net energy costs, defined as energy costs minus profits from price arbitrage and reserve markets. The optimization uses load shifting, peak shaving, and frequency containment reserve market participation with hourly 20…

Reconfigurable Digital RRAM Logic Enables In-Situ Pruning and Learning for Edge AI
Songqi Wang, Yue Zhang, Jia Chen, Xinyuan Zhang, Yi Li, Ning Lin, Yangu He, Jichang Yang, Yingjie Yu, Yi Li, Zhongrui Wang, Xiaojuan Qi, Han Wang
https://arxiv.org/abs/2506.13151

Reconfigurable Digital RRAM Logic Enables In-Situ Pruning and Learning for Edge AI
The human brain simultaneously optimizes synaptic weights and topology by growing, pruning, and strengthening synapses while performing all computation entirely in memory. In contrast, modern artificial-intelligence systems separate weight optimization from topology optimization and depend on energy-intensive von Neumann architectures. Here, we present a software-hardware co-design that bridges this gap. On the algorithmic side, we introduce a real-time dynamic weight-pruning strategy that moni…

Decentralized Uplink Adaptive Compression for Cell-Free MIMO with Limited Fronthaul
Zehua Li, Jingjie Wei, Raviraj Adve
https://arxiv.org/abs/2506.11284 ht…

Decentralized Uplink Adaptive Compression for Cell-Free MIMO with Limited Fronthaul
We study the problem of uplink compression for cell-free multi-input multi-output networks with limited fronthaul capacity. In compress-forward mode, remote radio heads (RRHs) compress the received signal and forward it to a central unit for joint processing. While previous work has focused on a transform-based approach, which optimizes the transform matrix that reduces signals of high dimension to a static pre-determined lower dimension, we propose a rate-based approach that simultaneously fin…

Efficient Modular Multiplier over GF (2^m) for ECPM
Ruby Kumari, Gaurav Purohit, Abhijit Karmakar
https://arxiv.org/abs/2506.09464 https://

Efficient Modular Multiplier over GF (2^m) for ECPM
Elliptic curve cryptography (ECC) has emerged as the dominant public-key protocol, with NIST standardizing parameters for binary field GF(2^m) ECC systems. This work presents a hardware implementation of a Hybrid Multiplication technique for modular multiplication over binary field GF(2m), targeting NIST B-163, 233, 283, and 571 parameters. The design optimizes the combination of conventional multiplication (CM) and Karatsuba multiplication (KM) to enhance elliptic curve point multiplication (E…

This https://arxiv.org/abs/2503.09409 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

AI-based Framework for Robust Model-Based Connector Mating in Robotic Wire Harness Installation
Despite the widespread adoption of industrial robots in automotive assembly, wire harness installation remains a largely manual process, as it requires precise and flexible manipulation. To address this challenge, we design a novel AI-based framework that automates cable connector mating by integrating force control with deep visuotactile learning. Our system optimizes search-and-insertion strategies using first-order optimization over a multimodal transformer architecture trained on visual, ta…

A Branch-and-Cut Algorithm for the Optimal Design of Parking Lots with One-way and Two-way Lanes
Helen Thomas, Tarun Rambha
https://arxiv.org/abs/2506.09961

A Branch-and-Cut Algorithm for the Optimal Design of Parking Lots with One-way and Two-way Lanes
We address the problem of maximizing the number of stalls in parking lots where vehicles park perpendicular to the driveways. Building on recent research, we first formulate a mixed integer program to maximize the number of parking stalls using a flow-based approach. Parking lots are rasterized into a grid, and the proposed MIP model optimizes them in a generic manner, adapting to the grid resolution and stall size without the need for custom formulations. The constraints ensure the connectivit…

Application of the optimized-basis generator coordinate method to low-lying excited states of sd-shell nuclei
Moemi Matsumoto, Yusuke Tanimura, Kouichi Hagino
https://arxiv.org/abs/2506.13147

Application of the optimized-basis generator coordinate method to low-lying excited states of sd-shell nuclei
We apply the optimized-basis generator coordinate method (OptGCM) to sd-shell nuclei, $^{20}$Ne, $^{24}$Mg, and $^{28}$Si. This method variationally optimizes both the basis Slater determinants in the generator coordinate method (GCM) and the corresponding weight coefficients. To analyze the low-lying excited states of those nuclei, we implement the angular momentum projection. With the Skyrme interaction, we show that the simultaneous optimzation of the basis functions and the weight factors l…

Complex-Valued Holographic Radiance Fields
Yicheng Zhan, Dong-Ha Shin, Seung-Hwan Baek, Kaan Ak\c{s}it
https://arxiv.org/abs/2506.08350 https://

Complex-Valued Holographic Radiance Fields
Modeling the full properties of light, including both amplitude and phase, in 3D representations is crucial for advancing physically plausible rendering, particularly in holographic displays. To support these features, we propose a novel representation that optimizes 3D scenes without relying on intensity-based intermediaries. We reformulate 3D Gaussian splatting with complex-valued Gaussian primitives, expanding support for rendering with light waves. By leveraging RGBD multi-view images, our …

This https://arxiv.org/abs/2505.01875 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

MolQAE: Quantum Autoencoder for Molecular Representation Learning
We introduce Quantum Molecular Autoencoder (MolQAE), the first quantum autoencoder to leverage the complete molecular structures. MolQAE uniquely maps SMILES strings directly to quantum states using parameterized rotation gates, preserving vital structural information. Its quantum encoder-decoder framework enables latent space compression and reconstruction. A dual-objective strategy optimizes fidelity and minimizes trash state deviation. Our evaluations demonstrate effective capture of molecul…

This https://arxiv.org/abs/2501.03836 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor Diagnosis
Brain tumors can lead to neurological dysfunction, cognitive and psychological changes, increased intracranial pressure, and seizures, posing significant risks to health. The You Only Look Once (YOLO) series has shown superior accuracy in medical imaging object detection. This paper presents a novel SCC-YOLO architecture that integrates the SCConv module into YOLOv9. The SCConv module optimizes convolutional efficiency by reducing spatial and channel redundancy, enhancing image feature learning…

Digital Twin-based Smart Manufacturing: Dynamic Line Reconfiguration for Disturbance Handling
Bo Fu, Mingjie Bi, Shota Umeda, Takahiro Nakano, Youichi Nonaka, Quan Zhou, Takaharu Matsui, Dawn M. Tilbury, Kira Barton
https://arxiv.org/abs/2506.07332

Digital Twin-based Smart Manufacturing: Dynamic Line Reconfiguration for Disturbance Handling
The increasing complexity of modern manufacturing, coupled with demand fluctuation, supply chain uncertainties, and product customization, underscores the need for manufacturing systems that can flexibly update their configurations and swiftly adapt to disturbances. However, current research falls short in providing a holistic reconfigurable manufacturing framework that seamlessly monitors system disturbances, optimizes alternative line configurations based on machine capabilities, and automate…

Adaptive recycled plastic architecture: Vacuum-Sealed Chainmail Structures Through Computational Design
Yi Xu, Farzin Lotfi-Jam, Mustafa Faruki
https://arxiv.org/abs/2506.04660

Adaptive recycled plastic architecture: Vacuum-Sealed Chainmail Structures Through Computational Design
The construction industry is a major consumer of raw materials, accounting for nearly half of global material usage annually, while generating significant waste that poses sustainability challenges. This paper explores the untapped potential of recycled plastics as a primary construction material, leveraging their lightweight, flexible, and customizable properties for advanced applications in modular chainmail systems. Through a computational workflow, the study optimizes the design, testing, a…

Interference-Aware Multiuser Hybrid Precoding for Coexistence with LEO Satellite Communication
Nima Razavi, Murat Bayraktar, Nuria Gonzalez Prelcic, Robert W. Heath Jr
https://arxiv.org/abs/2506.03438

Interference-Aware Multiuser Hybrid Precoding for Coexistence with LEO Satellite Communication
Interference from terrestrial networks can reduce the communication rate for low Earth orbit (LEO) satellites in the upper mid-band. To coexist in frequency, MIMO precoding can be used to reduce the signal that impinges on the LEO satellite. We present a beamforming algorithm designed for the hybrid architecture that incorporates a satellite interference penalty while optimizing the analog and digital precoders. Our algorithm optimizes the precoding at the base station (BS) within the set of pr…

Trilevel Memetic Algorithm for the Electric Vehicle Routing Problem
Ivan Milinovi\'c, Leon Stjepan Uroi\'c, Marko {\DJ}urasevi\'c
https://arxiv.org/abs/2506.01065

Trilevel Memetic Algorithm for the Electric Vehicle Routing Problem
The Electric Vehicle Routing Problem (EVRP) extends the capacitated vehicle routing problem by incorporating battery constraints and charging stations, posing significant optimization challenges. This paper introduces a Trilevel Memetic Algorithm (TMA) that hierarchically optimizes customer sequences, route assignments, and charging station insertions. The method combines genetic algorithms with dynamic programming, ensuring efficient and high-quality solutions. Benchmark tests on WCCI2020 inst…

This https://arxiv.org/abs/2502.17057 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance
Large language models (LLMs) have demonstrated significant potential in enhancing dense retrieval through query augmentation. However, most existing methods treat the LLM and the retriever as separate modules, overlooking the alignment between generation and ranking objectives. In this work, we propose ExpandR, a unified LLM-augmented dense retrieval framework that jointly optimizes both the LLM and the retriever. ExpandR employs the LLM to generate semantically rich query expansions, which are…

This https://arxiv.org/abs/2412.03621 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services
Large Language Models (LLMs) are increasingly integrated into mobile services over wireless networks to support complex user requests. This trend has led to longer prompts, which improve LLMs' performance but increase data transmission costs and require more processing time, thereby reducing overall system efficiency and negatively impacting user experience. To address these challenges, we propose Joint Prompt and Power Optimization (JPPO), a framework that jointly optimizes prompt compression …

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

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

Human-in-the-loop: Real-time Preference Optimization
Wenbin Wang, Wenjie Xu, Colin N. Jones
https://arxiv.org/abs/2506.02225 https://…

Human-in-the-loop: Real-time Preference Optimization
Human-aware controllers play an important role in engineering systems for improving productivity, efficiency, and sustainability. It is essential to design such a controller that optimizes user utility while adhering to plant dynamics. While most online optimization algorithms rely on first-order or zeroth-order oracles, human feedback often appears as pairwise comparisons. In this work, we propose an online feedback optimization algorithm that leverages such preference feedback. We design a co…

This https://arxiv.org/abs/2505.20685 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

GIT-BO: High-Dimensional Bayesian Optimization with Tabular Foundation Models
Bayesian optimization (BO) effectively optimizes expensive black-box functions but faces significant challenges in high-dimensional spaces (dimensions exceeding 100) due to the curse of dimensionality. Existing high-dimensional BO methods typically leverage low-dimensional embeddings or structural assumptions to mitigate this challenge, yet these approaches frequently incur considerable computational overhead and rigidity due to iterative surrogate retraining and fixed assumptions. To address t…

Linear Discriminant Analysis with Gradient Optimization on Covariance Inverse
Cencheng Shen, Yuexiao Dong
https://arxiv.org/abs/2506.06845 https://

Linear Discriminant Analysis with Gradient Optimization on Covariance Inverse
Linear discriminant analysis (LDA) is a fundamental method in statistical pattern recognition and classification, achieving Bayes optimality under Gaussian assumptions. However, it is well-known that classical LDA may struggle in high-dimensional settings due to instability in covariance estimation. In this work, we propose LDA with gradient optimization (LDA-GO), a new approach that directly optimizes the inverse covariance matrix via gradient descent. The algorithm parametrizes the inverse co…

Robust Distribution Network Reconfiguration Using Mapping-based Column-and-Constraint Generation
Runjie Zhang, Kaiping Qu, Changhong Zhao, Wanjun Huang
https://arxiv.org/abs/2505.24677

Robust Distribution Network Reconfiguration Using Mapping-based Column-and-Constraint Generation
The integration of intermittent renewable energy sources into distribution networks introduces significant uncertainties and fluctuations, challenging their operational security, stability, and efficiency. This paper considers robust distribution network reconfiguration (RDNR) with renewable generator resizing, modeled as a two-stage robust optimization (RO) problem with decision-dependent uncertainty (DDU). Our model optimizes resizing decisions as the upper bounds of renewable generator outpu…

This https://arxiv.org/abs/2505.07872 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Revenue Optimization in Video Caching Networks with Privacy-Preserving Demand Predictions
Performance of video streaming, which accounts for most of the traffic in wireless communication, can be significantly improved by caching popular videos at the wireless edge. Determining the cache content that optimizes performance (defined via a revenue function) is thus an important task, and prediction of the future demands based on past history can make this process much more efficient. However, since practical video caching networks involve various parties (e.g., users, isp, and csp) that…

GIT-BO: High-Dimensional Bayesian Optimization with Tabular Foundation Models
Rosen Ting-Ying Yu, Cyril Picard, Faez Ahmed
https://arxiv.org/abs/2505.20685

GIT-BO: High-Dimensional Bayesian Optimization with Tabular Foundation Models
Bayesian optimization (BO) effectively optimizes expensive black-box functions but faces significant challenges in high-dimensional spaces (dimensions exceeding 100) due to the curse of dimensionality. Existing high-dimensional BO methods typically leverage low-dimensional embeddings or structural assumptions to mitigate this challenge, yet these approaches frequently incur considerable computational overhead and rigidity due to iterative surrogate retraining and fixed assumptions. To address t…