Tootfinder

🧢 Modeling the World in 280 Characters
#graphics

Modeling the World in 280 Characters | Codrops
An exploration of the mindset, methods, and motivations behind crafting tiny, expressive shaders that combine code, art, and constraint.

Modeling Data Diversity for Joint Instance and Verbalizer Selection in Cold-Start Scenarios
Mohna Chakraborty, Adithya Kulkarni, Qi Li
https://arxiv.org/abs/2507.00330

Modeling Data Diversity for Joint Instance and Verbalizer Selection in Cold-Start Scenarios
Prompt-based methods leverage the knowledge of pre-trained language models (PLMs) trained with a masked language modeling (MLM) objective; however, these methods are sensitive to template, verbalizer, and few-shot instance selection, particularly in cold-start settings with no labeled data. Existing studies overlook the dependency between instances and verbalizers, where instance-label probabilities depend on verbalizer token proximity in the embedding space. To address this, we propose COLDSEL…

"EV battery recycling key to easing future lithium supply constraints, modeling study shows"
#EV #ElectricVehicle #Cars

EV battery recycling key to easing future lithium supply constraints, modeling study shows
Lightweight, powerful lithium-ion batteries are crucial for the transition to electric vehicles, and global demand for lithium is set to grow rapidly over the next 25 years. A new analysis from the University of California, Davis, published May 29 in Nature Sustainability, looks at how new mining operations and battery recycling could meet that demand. Recycling could play a big role in easing supply constraints, the researchers found.

A generative modeling / Physics-Informed Neural Network approach to random differential equations
Georgios Arampatzis, Stylianos Katsarakis, Charalambos Makridakis
https://arxiv.org/abs/2507.01687

A generative modeling / Physics-Informed Neural Network approach to random differential equations
The integration of Scientific Machine Learning (SciML) techniques with uncertainty quantification (UQ) represents a rapidly evolving frontier in computational science. This work advances Physics-Informed Neural Networks (PINNs) by incorporating probabilistic frameworks to effectively model uncertainty in complex systems. Our approach enhances the representation of uncertainty in forward problems by combining generative modeling techniques with PINNs. This integration enables in a systematic fas…

Integrative, Scalable Modeling of Hydrological Systems with MBSE and HFGT
Megan Harris, Ehsanoddin Ghorbanichemazkati, Mohammad Mahdi Naderi, John C. Little, Amro M. Farid
https://arxiv.org/abs/2506.00696

Integrative, Scalable Modeling of Hydrological Systems with MBSE and HFGT
Worsening global challenges in the Anthropocene demand complex, adaptive solutions grounded in a systems-level understanding of coupled social and environmental dynamics. However, existing modeling approaches often fall short due to disciplinary silos, limited scalability, and the absence of shared ontological frameworks. Model-Based Systems Engineering (MBSE), when integrated with Hetero-functional Graph Theory (HFGT), offers a powerful methodology for modeling systems of systems while preserv…

PI-WAN: A Physics-Informed Wind-Adaptive Network for Quadrotor Dynamics Prediction in Unknown Environments
Mengyun Wang, Bo Wang, Yifeng Niu, Chang Wang
https://arxiv.org/abs/2507.00816

PI-WAN: A Physics-Informed Wind-Adaptive Network for Quadrotor Dynamics Prediction in Unknown Environments
Accurate dynamics modeling is essential for quadrotors to achieve precise trajectory tracking in various applications. Traditional physical knowledge-driven modeling methods face substantial limitations in unknown environments characterized by variable payloads, wind disturbances, and external perturbations. On the other hand, data-driven modeling methods suffer from poor generalization when handling out-of-distribution (OoD) data, restricting their effectiveness in unknown scenarios. To addres…

Modeling Group Actions on Stacks (Especially the Lubin-Tate Action)
Rin Ray
https://arxiv.org/abs/2507.00309 https://arxiv.org/pdf/25…

Modeling Group Actions on Stacks (Especially the Lubin-Tate Action)
Suppose we are given a profinite group $G$ acting on a formal moduli stack $\mathcal{M}$, and we want to understand the group action, and compute cohomology related to this group action. How can we do it? This prolegomenon surveys two methods of pinning down such an action: geometric modeling and the two tower method. We highlight their use on a specific action - the automorphisms of a formal group acting on its deformation space, called the Lubin-Tate action.

Three-Dimensional Channel Modeling for Molecular Communications in Tubular Environments with Heterogeneous Boundary Conditions
Yun-Feng Lo, Changmin Lee, Chan-Byoung Chae
https://arxiv.org/abs/2506.00803

Three-Dimensional Channel Modeling for Molecular Communications in Tubular Environments with Heterogeneous Boundary Conditions
Molecular communication (MC), one of the emerging techniques in the field of communication, is entering a new phase following several decades of foundational research. Recently, attention has shifted toward MC in liquid media, particularly within tubular environments, due to novel application scenarios. The spatial constraints of such environments make accurate modeling of molecular movement in tubes more challenging than in traditional free-space channels. In this paper, we propose a three-dim…

DV365: Extremely Long User History Modeling at Instagram
Wenhan Lyu, Devashish Tyagi, Yihang Yang, Ziwei Li, Ajay Somani, Karthikeyan Shanmugasundaram, Nikola Andrejevic, Ferdi Adeputra, Curtis Zeng, Arun K. Singh, Maxime Ransan, Sagar Jain
https://arxiv.org/abs/2506.00450

DV365: Extremely Long User History Modeling at Instagram
Long user history is highly valuable signal for recommendation systems, but effectively incorporating it often comes with high cost in terms of data center power consumption and GPU. In this work, we chose offline embedding over end-to-end sequence length optimization methods to enable extremely long user sequence modeling as a cost-effective solution, and propose a new user embedding learning strategy, multi-slicing and summarization, that generates highly generalizable user representation of …

WHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent Triggerability in Task-Oriented Dialogue
Yaoyao Qian, Jindan Huang, Yuanli Wang, Simon Yu, Kyrie Zhixuan Zhou, Jiayuan Mao, Mingfu Liang, Hanhan Zhou
https://arxiv.org/abs/2506.01881

WHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent Triggerability in Task-Oriented Dialogue
Task-oriented dialogue systems often face difficulties when user utterances seem semantically complete but lack necessary structural information for appropriate system action. This arises because users frequently do not fully understand their own needs, while systems require precise intent definitions. Current LLM-based agents cannot effectively distinguish between linguistically complete and contextually triggerable expressions, lacking frameworks for collaborative intent formation. We present…

The Coupling Effect of Sensing Targets on the Environment for 3GPP ISAC Channels: Observation, Modeling, and Validation
Yameng Liu, Jianhua Zhang, Yuxiang Zhang, Hongbo Xing, Yifeng Xiong, Zhiqiang Yuan, Guangyi Liu
https://arxiv.org/abs/2506.00480

The Coupling Effect of Sensing Targets on the Environment for 3GPP ISAC Channels: Observation, Modeling, and Validation
Integrated Sensing And Communication (ISAC) has been identified as a key 6G application by ITU and 3GPP, with standardization efforts already underway. Sensing tasks, such as target localization, demand more precise characterization of the sensing target (ST) in ISAC channel modeling. The ST couples complexly with environmental scatterers, potentially blocking some multipaths and generating new ones, resulting in power variations compared to the original channel. To accurately model this effect…

Towards a unified user modeling language for engineering human centered AI systems
Aaron Conrardy, Alfredo Capozucca, Jordi Cabot
https://arxiv.org/abs/2505.24697

Towards a unified user modeling language for engineering human centered AI systems
In today's digital society, personalization has become a crucial aspect of software applications, significantly impacting user experience and engagement. A new wave of intelligent user interfaces, such as AI-based conversational agents, has the potential to enable such personalization beyond what other types of interfaces could offer in the past. Personalization requires the ability to specify a complete user profile, covering as many dimensions as possible, such as potential accessibility cons…

Nonlinear projection-based model order reduction with machine learning regression for closure error modeling in the latent space
S. Ares de Parga, Radek Tezaur, Carlos G. Hern\'andez, Charbel Farhat
https://arxiv.org/abs/2507.00634

Nonlinear projection-based model order reduction with machine learning regression for closure error modeling in the latent space
A significant advancement in nonlinear projection-based model order reduction (PMOR) is presented through a highly effective methodology. This methodology employs Gaussian process regression (GPR) and radial basis function (RBF) interpolation for closure error modeling in the latent space, offering notable gains in efficiency and expanding the scope of PMOR. Moving beyond the limitations of deep artificial neural networks (ANNs), previously used for this task, this approach provides crucial adv…

Physics-Informed Neural ODEs for Temporal Dynamics Modeling in Cardiac T1 Mapping
Nuno Capit\~ao, Yi Zhang, Yidong Zhao, Qian Tao
https://arxiv.org/abs/2507.00613

Physics-Informed Neural ODEs for Temporal Dynamics Modeling in Cardiac T1 Mapping
Spin-lattice relaxation time ($T_1$) is an important biomarker in cardiac parametric mapping for characterizing myocardial tissue and diagnosing cardiomyopathies. Conventional Modified Look-Locker Inversion Recovery (MOLLI) acquires 11 breath-hold baseline images with interleaved rest periods to ensure mapping accuracy. However, prolonged scanning can be challenging for patients with poor breathholds, often leading to motion artifacts that degrade image quality. In addition, $T_1$ mapping requi…

Capsule Network-Based Semantic Intent Modeling for Human-Computer Interaction
Shixiao Wang, Yifan Zhuang, Runsheng Zhang, Zhijun Song
https://arxiv.org/abs/2507.00540

Capsule Network-Based Semantic Intent Modeling for Human-Computer Interaction
This paper proposes a user semantic intent modeling algorithm based on Capsule Networks to address the problem of insufficient accuracy in intent recognition for human-computer interaction. The method represents semantic features in input text through a vectorized capsule structure. It uses a dynamic routing mechanism to transfer information across multiple capsule layers. This helps capture hierarchical relationships and part-whole structures between semantic entities more effectively. The mod…

Rapid yet accurate Tile-circuit and device modeling for Analog In-Memory Computing
J. Luquin, C. Mackin, S. Ambrogio, A. Chen, F. Baldi, G. Miralles, M. J. Rasch, J. B\"uchel, M. Lalwani, W. Ponghiran, P. Solomon, H. Tsai, G. W. Burr, P. Narayanan
https://arxiv.org/abs/2506.00004

Rapid yet accurate Tile-circuit and device modeling for Analog In-Memory Computing
Analog In-Memory Compute (AIMC) can improve the energy efficiency of Deep Learning by orders of magnitude. Yet analog-domain device and circuit non-idealities -- within the analog ``Tiles'' performing Matrix-Vector Multiply (MVM) operations -- can degrade neural-network task accuracy. We quantify the impact of low-level distortions and noise, and develop a mathematical model for Multiply-ACcumulate (MAC) operations mapped to analog tiles. Instantaneous-current IR-drop (the most significant circ…

Modeling Cosmic Ray Electron Spectra and Synchrotron Emission in the Multiphase ISM
Nora B. Linzer, Lucia Armillotta, Eve C. Ostriker, Eliot Quataert
https://arxiv.org/abs/2507.00142

Modeling Cosmic Ray Electron Spectra and Synchrotron Emission in the Multiphase ISM
We model the transport and spectral evolution of 1-100 GeV cosmic ray (CR) electrons (CREs) in TIGRESS MHD simulations of the magnetized, multiphase interstellar medium. We post-process a kpc-sized galactic disk patch representative of the solar neighborhood using a two-moment method for CR transport that includes advection, streaming, and diffusion. The diffusion coefficient is set by balancing wave growth via the CR streaming instability against wave damping (nonlinear Landau and ion-neutral …

2nd Conference on Multiscale Modeling of Complex Systems SCALES, at Johannes Gutenberg University Mainz, Sept 2-5, 2025.
#geology
https://model.uni-mainz.de/scales-conference-202…

Reluctant Interaction Inference after Additive Modeling
Yiling Huang, Snigdha Panigrahi, Guo Yu, Jacob Bien
https://arxiv.org/abs/2506.01219 https://

Reluctant Interaction Inference after Additive Modeling
Additive models enjoy the flexibility of nonlinear models while still being readily understandable to humans. By contrast, other nonlinear models, which involve interactions between features, are not only harder to fit but also substantially more complicated to explain. Guided by the principle of parsimony, a data analyst therefore may naturally be reluctant to move beyond an additive model unless it is truly warranted. To put this principle of interaction reluctance into practice, we formula…

Modeling the Cosmological Lyman-$\alpha$ Forest at the Field Level
Roger de Belsunce, Mikhail M. Ivanov, James M. Sullivan, Kazuyuki Akitsu, Shi-Fan Chen
https://arxiv.org/abs/2507.00284

Modeling the Cosmological Lyman-$α$ Forest at the Field Level
The distribution of absorption lines in the spectra of distant quasars, called the Lyman-$α$ (Ly-$α$) forest, is a unique probe of cosmology and the intergalactic medium at high redshifts and small scales. The statistical power of ongoing redshift surveys demands precise theoretical tools to model the Ly-$α$ forest. We address this challenge by developing an analytic, perturbative forward model to predict the Ly-$α$ forest at the field level for a given set of cosmological initial condition…

Quantum channel for modeling spin-motion dephasing in Rydberg chains
Christopher Wyenberg, Kent Ueno, Alexandre Cooper
https://arxiv.org/abs/2506.24082 htt…

Quantum channel for modeling spin-motion dephasing in Rydberg chains
We introduce a quantum channel to model the dissipative dynamics resulting from the coupling between spin and motional degrees of freedom in chains of neutral atoms with Rydberg interactions. The quantum channel acts on the reduced spin state obtained under the frozen gas approximation, modulating its elements with time-dependent coefficients. These coefficients can be computed exactly in the perturbative regime, enabling efficient modeling of spin-motion dephasing in systems too large for exac…

Dev.to author Karm Patel writes about different standardized ways to go about git branching for different software development workflows in 2025. From Git Flow to Forking Workflows, each is evaluated exposing their pros and cons weighting each approach's complexity vs its contribution to best modeling your workflow.
"Git Branching Strategies: A Comprehensive Guide"

Human sensory-musculoskeletal modeling and control of whole-body movements
Chenhui Zuo, Guohao Lin, Chen Zhang, Shanning Zhuang, Yanan Sui
https://arxiv.org/abs/2506.00071

Human sensory-musculoskeletal modeling and control of whole-body movements
Coordinated human movement depends on the integration of multisensory inputs, sensorimotor transformation, and motor execution, as well as sensory feedback resulting from body-environment interaction. Building dynamic models of the sensory-musculoskeletal system is essential for understanding movement control and investigating human behaviours. Here, we report a human sensory-musculoskeletal model, termed SMS-Human, that integrates precise anatomical representations of bones, joints, and muscle…

Generalized nearest-neighbor distance and Hawkes point process modeling applied to mining induced seismicity
Mohammadamin Sedghizadeh, Robert Shcherbakov, Matthew van den Berghe
https://arxiv.org/abs/2506.00768

Generalized nearest-neighbor distance and Hawkes point process modeling applied to mining induced seismicity
Modeling seismic activity rates and clustering plays an important role in studies of induced seismicity associated with mining and other resource extraction operations. This is critical for understanding the physical and statistical characteristics of seismicity and assessing the associated hazard. In this work, we introduce the generalization of the Nearest-Neighbor Distance (NND) method by incorporating an arbitrary distribution function for the frequency-magnitude statistics of seismic event…

Modeling temporal hypergraphs
J\"urgen Lerner, Marian-Gabriel H\^ancean, Matjaz Perc
https://arxiv.org/abs/2506.01408 https://ar…

Modeling temporal hypergraphs
Networks representing social, biological, technological or other systems are often characterized by higher-order interaction involving any number of nodes. Temporal hypergraphs are given by ordered sequences of hyperedges representing sets of nodes interacting at given points in time. In this paper we discuss how a recently proposed model family for time-stamped hyperedges - relational hyperevent models (RHEM) - can be employed to define tailored null distributions for temporal hypergraphs. RHE…

The SPHEREx Sky Simulator: Science Data Modeling for the First All-Sky Near-Infrared Spectral Survey
Brendan P. Crill, Yoonsoo P. Bach, Sean A. Bryan, Jean Choppin de Janvry, Ari J. Cukierman, C. Darren Dowell, Spencer W. Everett, Candice Fazar, Tatiana Goldina, Zhaoyu Huai, Howard Hui, Woong-Seob Jeong, Jae Hwan Kang, Phillip M. Korngut, Jae Joon Lee, Daniel C. Masters, Chi H. Nguyen, Jeonghyun Pyo, Teresa Symons, Yujin Yang, Michael Zemcov, Rachel Akeson, Matthew L. N. Ashby, James J…

The SPHEREx Sky Simulator: Science Data Modeling for the First All-Sky Near-Infrared Spectral Survey
We describe the SPHEREx Sky Simulator, a software tool designed to model science data for NASA's SPHEREx mission that will carry out a series of all-sky spectrophotometric surveys at $\sim$6'' spatial resolution in 102 spectral channels spanning 0.75 to 5 $μ$m. The Simulator software implements models for astrophysical emission, instrument characteristics, and survey strategy to generate realistic infrared sky scenes as they will be observed by SPHEREx. The simulated data includes a variety of…

How important is the dielectric constant in water modeling? Evaluation of the performance of the TIP4P/$\varepsilon$ force field and its compatibility with the Joung-Cheetham NaCl model
{\L}ukasz Baran, Cosmin A. Dicu-Gohoreanu, Luis G. MacDowell
https://arxiv.org/abs/2506.00153

How important is the dielectric constant in water modeling? Evaluation of the performance of the TIP4P/$\varepsilon$ force field and its compatibility with the Joung-Cheetham NaCl model
Efficient large scale computer simulations of complex aqueous solutions require the use of accurate but simple empirical force fields for water. However, the growing complexity of these systems is evidencing the difficulties to describe solution properties without due account of polarization. Conflicting strategies to remedy this problem are parametrizing water force fields to the dielectric constant or charge scaling of the solvated ions. In this work we compare results from TIP4P/$\varepsil…

Far beyond the Lounge, with Lounge Lizard EP-5’s new physical modeling
#MusicianTips #MusicTech

Far beyond the Lounge, with Lounge Lizard EP-5's new physical modeling - CDM Create Digital Music
AAS' Lounge Lizard EP-5 refreshes one of the definitive electric keyboards in software with a redesigned physical modeling core for tine and reed instruments. What sets this particular plug-in apart is deep modeling and diverse presets, which can take the instrument way beyond the lounge. Yes, it can be an accurate electric keyboard, but it can also imagine new keyboard instrument sounds that you've never heard before.

#today there has been tidying of some of the externals for the heat pump (but they were not entirely met with delight), I mowed our tiny lawns, and made a little progress on the #research modeling and at least have some mechanical (if slightly mind-bending) stuff to work through...

The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets
Shenzhe Zhu, Jiao Sun, Yi Nian, Tobin South, Alex Pentland, Jiaxin Pei
https://arxiv.org/abs/2506.00073

The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets
AI agents are increasingly used in consumer-facing applications to assist with tasks such as product search, negotiation, and transaction execution. In this paper, we explore a future scenario where both consumers and merchants authorize AI agents to fully automate negotiations and transactions. We aim to answer two key questions: (1) Do different LLM agents vary in their ability to secure favorable deals for users? (2) What risks arise from fully automating deal-making with AI agents in consum…

Modeling the Optical Properties of Biological Structures using Symbolic Regression
Julian Sierra-Velez, Alexandre Vial, Marina Inchaussandague, Diana Skigin, Demetrio Mac\'ias
https://arxiv.org/abs/2506.01862

Modeling the Optical Properties of Biological Structures using Symbolic Regression
We present a Machine Learning approach based on Symbolic Regression to derive, from either numerically generated or experimentally measured spectral data, closed-form expressions that model the optical properties of biological materials. To evaluate the performance of our approach, we consider three case studies with the aim of retrieving the refractive index of the materials that constitute the biological structures considered. The results obtained show that, in addition to retrieving readable…

From Sentences to Sequences: Rethinking Languages in Biological System
Ke Liu, Shuanke Shen, Hao Chen
https://arxiv.org/abs/2507.00953 https://

From Sentences to Sequences: Rethinking Languages in Biological System
The paradigm of large language models in natural language processing (NLP) has also shown promise in modeling biological languages, including proteins, RNA, and DNA. Both the auto-regressive generation paradigm and evaluation metrics have been transferred from NLP to biological sequence modeling. However, the intrinsic structural correlations in natural and biological languages differ fundamentally. Therefore, we revisit the notion of language in biological systems to better understand how NLP …

Can Large Language Models Trigger a Paradigm Shift in Travel Behavior Modeling? Experiences with Modeling Travel Satisfaction
Pengfei Xu, Donggen Wang
https://arxiv.org/abs/2505.23262

Can Large Language Models Trigger a Paradigm Shift in Travel Behavior Modeling? Experiences with Modeling Travel Satisfaction
As a specific domain of subjective well-being, travel satisfaction has attracted much research attention recently. Previous studies primarily use statistical models and, more recently, machine learning models to explore the determinants of travel satisfaction. Both approaches require data from sufficient sample sizes and correct prior statistical assumptions. The emergence of Large Language Models (LLMs) offers a new modeling approach that can overcome the shortcomings of the existing methods. …

Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets
Arkaprabha Ganguli, Nesar Ramachandra, Julie Bessac, Emil Constantinescu
https://arxiv.org/abs/2507.00298

Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets
This study addresses the challenge of statistically extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings. We investigate encoder-decoder-based generative models for nonlinear dimensionality reduction, focusing on disentangling low-dimensional latent variables corresponding to independent physical factors. Introducing Aux-VAE, a novel architecture within the classical Variational Autoencoder framework, we achieve disentanglement with m…

Investigating Stochastic Methods for Prosody Modeling in Speech Synthesis
Paul Mayer, Florian Lux, Alejandro P\'erez-Gonz\'alez-de-Martos, Angelina Elizarova, Lindsey Vanderlyn, Dirk V\"ath, Ngoc Thang Vu
https://arxiv.org/abs/2507.00227

Investigating Stochastic Methods for Prosody Modeling in Speech Synthesis
While generative methods have progressed rapidly in recent years, generating expressive prosody for an utterance remains a challenging task in text-to-speech synthesis. This is particularly true for systems that model prosody explicitly through parameters such as pitch, energy, and duration, which is commonly done for the sake of interpretability and controllability. In this work, we investigate the effectiveness of stochastic methods for this task, including Normalizing Flows, Conditional Flow…

Data-Driven Power Modeling and Monitoring via Hardware Performance Counter Tracking
Sergio Mazzola, Gabriele Ara, Thomas Benz, Bj\"orn Forsberg, Tommaso Cucinotta, Luca Benini
https://arxiv.org/abs/2506.23672

Data-Driven Power Modeling and Monitoring via Hardware Performance Counter Tracking
Energy-centric design is paramount in the current embedded computing era: use cases require increasingly high performance at an affordable power budget, often under real-time constraints. Hardware heterogeneity and parallelism help address the efficiency challenge, but greatly complicate online power consumption assessments, which are essential for dynamic hardware and software stack adaptations. We introduce a novel power modeling methodology with state-of-the-art accuracy, low overhead, and h…

How Students (Really) Use ChatGPT: Uncovering Experiences Among Undergraduate Students
Tawfiq Ammari, Meilun Chen, S M Mehedi Zaman, Kiran Garimella
https://arxiv.org/abs/2505.24126

How Students (Really) Use ChatGPT: Uncovering Experiences Among Undergraduate Students
This study investigates how undergraduate students engage with ChatGPT in self directed learning contexts. Analyzing naturalistic interaction logs, we identify five dominant use categories of ChatGPT information seeking, content generation, language refinement, meta cognitive engagement, and conversational repair. Behavioral modeling reveals that structured, goal driven tasks like coding, multiple choice solving, and job application writing are strong predictors of continued use. Drawing on Sel…

Efficient Interleaved Speech Modeling through Knowledge Distillation
Mohammadmahdi Nouriborji, Morteza Rohanian
https://arxiv.org/abs/2506.23670 https://…

Efficient Interleaved Speech Modeling through Knowledge Distillation
Current speech language models exceed the size and latency constraints of many deployment environments. We build compact, expressive speech generation models through layer-aligned distillation, matching hidden states, attention maps, and softened logits to compress large multimodal transformers by 3x with minimal loss in performance. We introduce TinyWave, a family of 2B-parameter models for speech-to-speech and interleaved speech-text generation, trained on 50,000 hours of public audio. TinyWa…

Feasibility of spectral-element modeling of wave propagation through the anatomy of marine mammals
Carlos Garc\'ia A., Vladimiro Boselli, Aida Hejazi Nooghabi, Andrea Colombi, Lapo Boschi
https://arxiv.org/abs/2506.22944

Feasibility of spectral-element modeling of wave propagation through the anatomy of marine mammals
This study introduces the first 3D spectral-element method (SEM) simulation of ultrasonic wave propagation in a bottlenose dolphin (Tursiops truncatus) head. Unlike traditional finite-element methods (FEM), which struggle with high-frequency simulations due to costly linear-system inversions and slower convergence, SEM offers exponential convergence and efficient parallel computation. Using Computed Tomography (CT) scan data, we developed a detailed hexahedral mesh capturing complex anatomical …

Testing Paradox May Explain Increased Observed Prevalence of Bacterial STIs among MSM on HIV PrEP: A Modeling Study
Laura M\"uller, Piklu Mallick, Antonio B. Mar\'in-Carballo, Philipp D\"onges, Robyn J. N. Kettlitz, Carolina J. Klett-Tammen, Mirjam Kretzschmar, Viola Priesemann, Seba Contreras
https://arxiv.org/abs…

Testing Paradox May Explain Increased Observed Prevalence of Bacterial STIs among MSM on HIV PrEP: A Modeling Study
HIV pre-exposure Prophylaxis (PrEP) has become essential for global HIV control, but its implementation coincides with rising bacterial STI rates among men who have sex with men (MSM). While risk-compensation behavioral changes like reduced condom use are frequently reported, we examine whether intensified asymptomatic screening in PrEP programs creates surveillance artifacts that could be misinterpreted. We developed a compartmental model to represent the simultaneous spread of HIV and chlamyd…

Deep learning-derived arterial input function
Junyu Chen, Zirui Jiang, Jennifer M. Coughlin, Martin G. Pomper, Yong Du
https://arxiv.org/abs/2505.24166 htt…

Deep learning-derived arterial input function
Dynamic positron emission tomography (PET) imaging combined with radiotracer kinetic modeling is a powerful technique for visualizing biological processes in the brain, offering valuable insights into brain functions and neurological disorders such as Alzheimer's and Parkinson's diseases. Accurate kinetic modeling relies heavily on the use of a metabolite-corrected arterial input function (AIF), which typically requires invasive and labor-intensive arterial blood sampling. While alternative non…

🇺🇦 #NowPlaying on #BBC6Music's #LaurenLaverne
Handsome Boy Modeling School:
🎵 The Truth (feat. Róisín Murphy)
#HandsomeBoyModelingSchool
https://cantstopthemusic.bandcamp.com/track/the-truth-handsome-boy-modeling-school
https://open.spotify.com/track/5l7icgnicl7JToRxPkSXyy

Transferable Modeling Strategies for Low-Resource LLM Tasks: A Prompt and Alignment-Based
Shuangquan Lyu, Yingnan Deng, Guiran Liu, Zhen Qi, Ruotong Wang
https://arxiv.org/abs/2507.00601

Transferable Modeling Strategies for Low-Resource LLM Tasks: A Prompt and Alignment-Based
This paper addresses the limited transfer and adaptation capabilities of large language models in low-resource language scenarios. It proposes a unified framework that combines a knowledge transfer module with parameter-efficient fine-tuning strategies. The method introduces knowledge alignment loss and soft prompt tuning to guide the model in effectively absorbing the structural features of target languages or tasks under minimal annotation. This enhances both generalization performance and tr…

CrossPipe: Towards Optimal Pipeline Schedules for Cross-Datacenter Training
Tiancheng Chen, Ales Kubicek, Langwen Huang, Torsten Hoefler
https://arxiv.org/abs/2507.00217

CrossPipe: Towards Optimal Pipeline Schedules for Cross-Datacenter Training
Training large language models (LLMs) now requires resources that exceed a single datacenter, making cross-datacenter strategies increasingly crucial. We present CrossPipe, a framework designed to optimize model training across geographically distributed datacenters by explicitly modeling and mitigating the impact of network latency and limited bandwidth. It enables unified analysis and optimization incorporating both pipeline parallelism (PP) and opportunities for overlapping data parallelism …

A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study
Agnideep Aich, Sameera Hewage, Md Monzur Murshed, Ashit Baran Aich, Amanda Mayeaux, Asim K. Dey, Kumer P. Das, Bruce Wade
https://arxiv.org/abs/2505.24006

A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study
In this paper, we introduce the A2 Copula Spatial Bayesian Neural Network (A2-SBNN), a predictive spatial model designed to map coordinates to continuous fields while capturing both typical spatial patterns and extreme dependencies. By embedding the dual-tail novel Archimedean copula viz. A2 directly into the network's weight initialization, A2-SBNN naturally models complex spatial relationships, including rare co-movements in the data. The model is trained through a calibration-driven process …

Hybrid SIS Dynamics for Demand Modeling of Frequently Updated Products
Ian Walter, Jitesh H. Panchal, Philip E. Par\'e
https://arxiv.org/abs/2506.01866

Hybrid SIS Dynamics for Demand Modeling of Frequently Updated Products
We propose a hybrid spreading process model to capture the dynamics of demand for software-based products. We introduce discontinuous jumps in the state to model sudden surges in demand that can be seen immediately after a product update is released. After each update, the modeled demand evolves according to a continuous-time susceptible-infected-susceptible (SIS) epidemic model. We identify the necessary and sufficient conditions for estimating the hybrid model's parameters for an arbitrary fi…

This https://arxiv.org/abs/2401.03276 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Robust Binary and Multinomial Logit Models for Classification with Data Uncertainties
Binary logit (BNL) and multinomial logit (MNL) models are the two most widely used discrete choice models for travel behavior modeling and prediction. However, in many scenarios, the collected data for those models are subject to measurement errors. Previous studies on measurement errors mostly focus on "better estimating model parameters" with training data. In this study, we focus on using BNL and MNL for classification problems, that is, to ``better predict the behavior of new samples'' when…

This https://arxiv.org/abs/2503.15391 has been replaced.
initial toot: https://mastoxiv.page/@a…

Modeling crystal defects using defect-informed neural networks
Most AI-for-Materials research to date has focused on ideal crystals, whereas real-world materials inevitably contain defects that play a critical role in modern functional technologies. The defects break geometric symmetry and increase interaction complexity, posing particular challenges for traditional ML models. Here, we introduce Defect-Informed Equivariant Graph Neural Network (DefiNet), a model specifically designed to accurately capture defect-related interactions and geometric configura…

This https://arxiv.org/abs/2402.17874 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Mixed Strategy Constraints in Continuous Games
When modeling robot interactions as Nash equilibrium problems, it is desirable to place coupled constraints which restrict these interactions to be safe and acceptable (for instance, to avoid collisions). Such games are continuous with potential mixed strategy equilibria, and this combination of characteristics means special care must be given to setting coupled constraints in a way that respects mixed strategies while remaining compatible with continuous game solution methods. Here, we investi…

Solar Surface Magnetic Field Simulation from 2010 to 2024 and Anomalous Southern Poleward Flux Transport in Cycle 24
Ruihui Wang, Jie Jiang, Yukun Luo
https://arxiv.org/abs/2506.01416

Solar Surface Magnetic Field Simulation from 2010 to 2024 and Anomalous Southern Poleward Flux Transport in Cycle 24
The solar surface magnetic field is fundamental for modeling the coronal magnetic field, studying the solar dynamo, and predicting solar cycle strength. We perform a continuous simulation of the surface magnetic field from 2010 to 2024, covering solar cycle 24 and the ongoing cycle 25, using the surface flux transport model with assimilated observed active regions (ARs) as the source. The simulation reproduces the evolution of the axial dipole strength, polar field reversal timing, and magnetic…

Modeling blazar broadband emission with convolutional neural networks -- III. proton synchrotron and hybrid models
N. Sahakyan, D. B\'egu\'e, A. Casotto, H. Dereli-B\'egu\'e, V. Vardanyan, M. Khachatryan, P. Giommi, A. Pe'er
https://arxiv.org/abs/2506.23885

Modeling blazar broadband emission with convolutional neural networks -- III. proton synchrotron and hybrid models
Modeling the broadband emission of blazars has become increasingly challenging with the advent of multimessenger observations. Building upon previous successes in applying convolutional neural networks (CNNs) to leptonic emission scenarios, we present an efficient CNN-based approach for modeling blazar emission under proton synchrotron and hybrid lepto-hadronic frameworks. Our CNN is trained on extensive numerical simulations generated by SOPRANO, which span a comprehensive parameter space acco…

Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System
Chao Wang, Yue Zheng, Yujing Zhang, Yan Feng, Zhe Wang, Xiaowei Shi, An You, Yu Chen
https://arxiv.org/abs/2506.00828

Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System
In a single-slot recommendation system, users are only exposed to one item at a time, and the system cannot collect user feedback on multiple items simultaneously. Therefore, only pointwise modeling solutions can be adopted, focusing solely on modeling the likelihood of clicks or conversions for items by users to learn user-item preferences, without the ability to capture the ranking information among different items directly. However, since user-side information is often much more abundant tha…

CarbonClarity: Understanding and Addressing Uncertainty in Embodied Carbon for Sustainable Computing
Xuesi Chen, Leo Han, Anvita Bhagavathula, Udit Gupta
https://arxiv.org/abs/2507.01145

CarbonClarity: Understanding and Addressing Uncertainty in Embodied Carbon for Sustainable Computing
Embodied carbon footprint modeling has become an area of growing interest due to its significant contribution to carbon emissions in computing. However, the deterministic nature of the existing models fail to account for the spatial and temporal variability in the semiconductor supply chain. The absence of uncertainty modeling limits system designers' ability to make informed, carbon-aware decisions. We introduce CarbonClarity, a probabilistic framework designed to model embodied carbon footpri…

Systemic Constraints of Undecidability
Seth Bulin
https://arxiv.org/abs/2507.01036 https://arxiv.org/pdf/2507.01036

Systemic Constraints of Undecidability
This paper presents a theory of systemic undecidability, reframing incomputability as a structural property of systems rather than a localized feature of specific functions or problems. We define a notion of causal embedding and prove a closure principle: any subsystem that participates functionally in the computation of an undecidable system inherits its undecidability. This result positions undecidability as a pervasive constraint on prediction, modeling, and epistemic access in both natural …

DuAL-Net: A Hybrid Framework for Alzheimer's Disease Prediction from Whole-Genome Sequencing via Local SNP Windows and Global Annotations
Eun Hye Lee, Taeho Jo
https://arxiv.org/abs/2506.00673

DuAL-Net: A Hybrid Framework for Alzheimer's Disease Prediction from Whole-Genome Sequencing via Local SNP Windows and Global Annotations
Alzheimer's disease (AD) dementia is the most common form of dementia. With the emergence of disease-modifying therapies, predicting disease risk before symptom onset has become critical. We introduce DuAL-Net, a hybrid deep learning framework for AD dementia prediction using whole genome sequencing (WGS) data. DuAL-Net integrates two components: local probability modeling, which segments the genome into non-overlapping windows, and global annotation-based modeling, which annotates SNPs and reo…

Modeling European Electricity Market Integration during turbulent times
Francesco Ravazzolo, Luca Rossini, Andrea Viselli
https://arxiv.org/abs/2506.23289 …

Modeling European Electricity Market Integration during turbulent times
This paper introduces a novel Bayesian reverse unrestricted mixed-frequency model applied to a panel of nine European electricity markets. Our model analyzes the impact of daily fossil fuel prices and hourly renewable energy generation on hourly electricity prices, employing a hierarchical structure to capture cross-country interdependencies and idiosyncratic factors. The inclusion of random effects demonstrates that electricity market integration both mitigates and amplifies shocks. Our result…

Avoiding (photo-$z$) Catastrophe
A. J. Battisti, E. da Cunha, S. Jin, J. A. Hodge
https://arxiv.org/abs/2507.01251 https://arxiv.org/…

Avoiding (photo-$z$) Catastrophe
Spectral modeling codes that estimate photometric redshifts (photo-$z$) are a powerful and often reliable method for determining redshifts of galaxies. However, there are notable instances where degeneracies in spectral energy distribution (SED) colors lead to `catastrophic' failures. We highlight the case of COSBO-7, a dusty, intermediate-$z$ galaxy that masqueraded as a high-$z$ source, because it demonstrates a unique scenario where photo-$z$ codes run into issues despite extensive multi-wav…

This https://arxiv.org/abs/2505.19452 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Nonsymmorphic symmetry adapted finite element modeling of glide-symmetric photonic structures
Space group theory is pivotal in the design of nanophotonics devices, enabling the characterization of periodic optical structures such as photonic crystals. The aim of this study is to extend the application of nonsymmorphic space groups in the field of numerical analysis for research and design of nanophotonics devices. In this work, we introduce the nonsymmorphic symmetry adapted finite element method, and provide a systematic approach for efficient band structure analysis of photonic struct…

Performance of LLMs on Stochastic Modeling Operations Research Problems: From Theory to Practice
Akshit Kumar, Tianyi Peng, Yuhang Wu, Assaf Zeevi
https://arxiv.org/abs/2506.23924

Performance of LLMs on Stochastic Modeling Operations Research Problems: From Theory to Practice
Large language models (LLMs) have exhibited expert-level capabilities across various domains. However, their abilities to solve problems in Operations Research (OR) -- the analysis and optimization of mathematical models derived from real-world problems or their verbal descriptions -- remain underexplored. In this work, we take a first step toward evaluating LLMs' abilities to solve stochastic modeling problems, a core class of OR problems characterized by uncertainty and typically involving to…

This https://arxiv.org/abs/2310.04310 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Spreading of information on a network: a quantum view
This paper concerns the modeling of the spread of information through a complex, multi-layered network, where the information is transferred from an initial transmitter to a final receiver. The mathematical model is deduced within the framework of operatorial methods, according to the formal mathematical apparatus typical of quantum mechanics. Two different approaches are considered: one based on the ($H,ρ$)-induced dynamics and one on the Gorini--Kossakowski--Sudarshan--Lindblad (GKSL) equati…

Interpretable Spatio-Temporal Features Extraction based Industrial Process Modeling and Monitoring by Soft Sensor
Qianchao Wang, Peng Sha, Leena Heistrene, Yuxuan Ding, Yaping Du
https://arxiv.org/abs/2506.00858

Interpretable Spatio-Temporal Features Extraction based Industrial Process Modeling and Monitoring by Soft Sensor
Data-driven soft sensors have been widely applied in complex industrial processes. However, the interpretable spatio-temporal features extraction by soft sensors remains a challenge. In this light, this work introduces a novel method termed spatio-temporal consistent and interpretable model (STCIM). First, temporal and spatial features are captured and aligned by a far topological spatio-temporal consistency extraction block. Then, the features are mapped into an interpretable latent space for …

This https://arxiv.org/abs/2504.10545 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

HSTU-BLaIR: Lightweight Contrastive Text Embedding for Generative Recommender
Recent advances in recommender systems have underscored the complementary strengths of generative modeling and pretrained language models. We propose HSTU-BLaIR, a hybrid framework that augments the Hierarchical Sequential Transduction Unit (HSTU)-based generative recommender with BLaIR, a lightweight contrastive text embedding model. This integration enriches item representations with semantic signals from textual metadata while preserving HSTU's powerful sequence modeling capabilities. We e…

An Efficient and Interpretable Autoregressive Model for High-Dimensional Tensor-Valued Time Series
Yuxi Cai, Lan Li, Yize Wang, Guodong Li
https://arxiv.org/abs/2506.01658

An Efficient and Interpretable Autoregressive Model for High-Dimensional Tensor-Valued Time Series
In autoregressive modeling for tensor-valued time series, Tucker decomposition, when applied to the coefficient tensor, provides a clear interpretation of supervised factor modeling but loses its efficiency rapidly with increasing tensor order. Conversely, canonical polyadic (CP) decomposition maintains efficiency but lacks a precise statistical interpretation. To attain both interpretability and powerful dimension reduction, this paper proposes a novel approach under the supervised factor mode…

IMPACT: Iterative Mask-based Parallel Decoding for Text-to-Audio Generation with Diffusion Modeling
Kuan-Po Huang, Shu-wen Yang, Huy Phan, Bo-Ru Lu, Byeonggeun Kim, Sashank Macha, Qingming Tang, Shalini Ghosh, Hung-yi Lee, Chieh-Chi Kao, Chao Wang
https://arxiv.org/abs/2506.00736

IMPACT: Iterative Mask-based Parallel Decoding for Text-to-Audio Generation with Diffusion Modeling
Text-to-audio generation synthesizes realistic sounds or music given a natural language prompt. Diffusion-based frameworks, including the Tango and the AudioLDM series, represent the state-of-the-art in text-to-audio generation. Despite achieving high audio fidelity, they incur significant inference latency due to the slow diffusion sampling process. MAGNET, a mask-based model operating on discrete tokens, addresses slow inference through iterative mask-based parallel decoding. However, its aud…

Mechanical Intelligence-Aware Curriculum Reinforcement Learning for Humanoids with Parallel Actuation
Yusuke Tanaka, Alvin Zhu, Quanyou Wang, Dennis Hong
https://arxiv.org/abs/2507.00273

Mechanical Intelligence-Aware Curriculum Reinforcement Learning for Humanoids with Parallel Actuation
Reinforcement learning (RL) has enabled significant advances in humanoid robot locomotion, yet most learning frameworks do not account for mechanical intelligence embedded in parallel actuation mechanisms due to limitations in simulator support for closed kinematic chains. This omission can lead to inaccurate motion modeling and suboptimal policies, particularly for robots with high actuation complexity. This paper presents an end-to-end curriculum RL framework for BRUCE, a kid-sized humanoid r…

This https://arxiv.org/abs/2412.16772 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?
The ongoing revolution in language modeling has led to various novel applications, some of which rely on the emerging social abilities of large language models (LLMs). Already, many turn to the new cyber friends for advice during the pivotal moments of their lives and trust them with the deepest secrets, implying that accurate shaping of the LLM's personality is paramount. To this end, state-of-the-art approaches exploit a vast variety of training data, and prompt the model to adopt a particula…

This https://arxiv.org/abs/2504.11711 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

The Hitchhiker's Guide to Program Analysis, Part II: Deep Thoughts by LLMs
Static analysis plays a crucial role in software vulnerability detection, yet faces a persistent precision-scalability tradeoff. In large codebases like the Linux kernel, traditional static analysis tools often generate excessive false positives due to simplified vulnerability modeling and overapproximation of path and data constraints. While large language models (LLMs) demonstrate promising code understanding capabilities, their direct application to program analysis remains unreliable due to…

This https://arxiv.org/abs/2505.08070 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Polarforming Antenna Enhanced Sensing and Communication: Modeling and Optimization
In this paper, we propose a novel polarforming antenna (PA) to achieve cost-effective wireless sensing and communication. Specifically, the PA can enable polarforming to adaptively control the antenna's polarization electrically as well as tune its position/rotation mechanically, so as to effectively exploit polarization and spatial diversity to reconfigure wireless channels for improving sensing and communication performance. We study an PA-enhanced integrated sensing and communication (ISAC) …

Near-feasible Fair Allocations in Two-sided Markets
Javier Cembrano, Andr\'es Moraga, Victor Verdugo
https://arxiv.org/abs/2506.01178 https://

Near-feasible Fair Allocations in Two-sided Markets
We study resource allocation in two-sided markets from a fundamental perspective and introduce a general modeling and algorithmic framework to effectively incorporate the complex and multidimensional aspects of fairness. Our main technical contribution is to show the existence of a range of near-feasible resource allocations parameterized in different model primitives to give flexibility when balancing the different policymaking requirements, allowing policy designers to fix these values accord…

This https://arxiv.org/abs/2503.02041 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

Interpolating Neural Network-Tensor Decomposition (INN-TD): a scalable and interpretable approach for large-scale physics-based problems
Deep learning has been extensively employed as a powerful function approximator for modeling physics-based problems described by partial differential equations (PDEs). Despite their popularity, standard deep learning models often demand prohibitively large computational resources and yield limited accuracy when scaling to large-scale, high-dimensional physical problems. Their black-box nature further hinders the application in industrial problems where interpretability and high precision are cr…

Hemispheric-Specific Coupling Improves Modeling of Functional Connectivity Using Wilson-Cowan Dynamics
Ramiro Pl\"uss, Hern\'an Villota, Patricio Orio
https://arxiv.org/abs/2506.22951

Hemispheric-Specific Coupling Improves Modeling of Functional Connectivity Using Wilson-Cowan Dynamics
Large-scale neural mass models have been widely used to simulate resting-state brain activity from structural connectivity. In this work, we extend a well-established Wilson--Cowan framework by introducing a novel hemispheric-specific coupling scheme that differentiates between intra-hemispheric and inter-hemispheric structural interactions. We apply this model to empirical cortical connectomes and resting-state fMRI data from matched control and schizophrenia groups. Simulated functional conne…

Energetic variational modeling of active nematics: coupling the Toner-Tu model with ATP hydrolysis
Yiwei Wang
https://arxiv.org/abs/2506.23043 https://

Energetic variational modeling of active nematics: coupling the Toner-Tu model with ATP hydrolysis
We present a thermodynamically consistent energetic variational model for active nematics driven by ATP hydrolysis, with a focus on the coupling between chemical reactions and mechanical dynamics. Extending the classical Toner-Tu framework, we introduce a chemo-mechanical coupling mechanism in which the self-advection and polarization dynamics are modulated by the ATP hydrolysis rate. The model is derived using an energetic variational approach that integrates both chemical free energy and mech…

This https://arxiv.org/abs/2505.17879 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

LLM4SP: Large Language Models for Scatterer Prediction via Synesthesia of Machines
Guided by Synesthesia of Machines (SoM), the nonlinear mapping relationship between sensory and communication information serves as a powerful tool to enhance both the accuracy and generalization of vehicle-to-vehicle (V2V) multi-modal intelligent channel modeling (MMICM) in intelligent transportation systems (ITSs). To explore the general mapping relationship between physical environment and electromagnetic space, a new intelligent sensing-communication integration dataset, named V2V-M3, is co…

This https://arxiv.org/abs/2209.03350 has been replaced.
link: https://scholar.google.com/scholar?q=a

Modeling Polarized Radio Sounding Observations of a Coronal Mass Ejection
Coronal Mass Ejections (CMEs) evolve significantly as they propagate from the Sun to the Earth, so remote observations of their changes in speed, strength of the magnetic field, density, and overall structure are critical for predicting their arrival time and geoeffectiveness. Radio line-of-sight observations of Faraday rotation and Total Electron Content combined with white-light observations enables the measurement of these properties with careful analyses. This paper describes the analysis t…

Estimating dynamic transmission rates with a Black-Karasinski process in stochastic SIHR models using particle MCMC
Avery Drennan, Jeffrey Covington, Dan Han, Andrew Attilio, Jaechoul Lee, Richard Posner, Eck Doerry, Joseph Mihaljevic, Ye Chen
https://arxiv.org/abs/2505.24127

Estimating dynamic transmission rates with a Black-Karasinski process in stochastic SIHR models using particle MCMC
Compartmental models are effective in modeling the spread of infectious pathogens, but have remaining weaknesses in fitting to real datasets exhibiting stochastic effects. We propose a stochastic SIHR model with a dynamic transmission rate, where the rate is modeled by the Black-Karasinski (BK) process - a mean-reverting stochastic process with a stable equilibrium distribution, making it well-suited for modeling long-term epidemic dynamics. To generate sample paths of the BK process and estima…

Assembling and Modeling Stacked Disordered Metasurfaces
Miao Chen, Amit Sharma, Johann Michler, Xavier Maeder, Philippe Lalanne, Angelos Xomalis
https://arxiv.org/abs/2506.23666

Assembling and Modeling Stacked Disordered Metasurfaces
Disordered metasurfaces offer unique properties unattainable with periodic or ordered metasurfaces, notably the absence of deterministic interference effects at specific wavelengths and angles. In this work, we introduce a lithography-free nanofabrication approach to realize cascaded disordered plasmonic metasurfaces with sub-micron total thickness. We experimentally characterize their angle-resolved specular and diffuse reflections using the bidirectional reflection distribution function (BRDF…

This https://arxiv.org/abs/2405.08719 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However, recent work has demonstrated that model misspecification can compromise the reliability of SBI, preventing its adoption in important applications where only misspecified simulators are available. This work introduces robust posterior estimation~(RoPE), a framework that overcomes model misspecification with a small re…

Learning thermodynamic master equations for open quantum systems
Peter Sentz, Stanley Nicholson, Yujin Cho, Sohail Reddy, Brendan Keith, Stefanie G\"unther
https://arxiv.org/abs/2506.01882

Learning thermodynamic master equations for open quantum systems
The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and other applications. Scientific machine learning techniques have been applied to this problem in a variety of ways, including by modeling with deep neural networks. However, the majority of mathematical models describing open quantum systems are linear, and the natural nonlinearities in learnable models have not been incorporated using physical principles. We …

Multi-Objective Neural Network Assisted Design Optimization of Soft Fin-Ray Grippers for Enhanced Grasping Performance
Ali Ghanizadeh, Ali Ahmadi, Arash Bahrami
https://arxiv.org/abs/2506.00494

Multi-Objective Neural Network Assisted Design Optimization of Soft Fin-Ray Grippers for Enhanced Grasping Performance
Soft Fin-Ray grippers can perform delicate and careful manipulation, which has caused notable attention in different fields. These grippers can handle objects of various forms and sizes safely. The internal structure of the Fin-Ray finger plays a significant role in its adaptability and grasping performance. However, modeling the non-linear grasp force and deformation behaviors for design purposes is challenging. Moreover, when the Fin-Ray finger becomes more rigid and capable of exerting highe…

Supporting Long-term Transactions in Smart Contracts Generated from Business Process Model and Notation (BPMN) Models
Christian Gang Liu
https://arxiv.org/abs/2505.24309

Supporting Long-term Transactions in Smart Contracts Generated from Business Process Model and Notation (BPMN) Models
To alleviate difficulties in writing smart contracts for distributed blockchain applications, as other research, we propose transformation of Business Process Model and Notation (BPMN) models into blockchain smart contracts. Unlike other research, we use Discrete Event Hierarchical State Machine (DE-HSM) multi-modal modeling to identify collaborative trade transactions that need to be supported by the smart contract and describe how the trade transactions, that may be nested, are supported by a…

Ken Utilization Layer: Hebbian Replay Within a Student's Ken for Adaptive Knowledge Tracing
Grey Kuling, Marinka Zitnik
https://arxiv.org/abs/2507.00032

Ken Utilization Layer: Hebbian Replay Within a Student's Ken for Adaptive Knowledge Tracing
We introduce KUL-KT, a biologically inspired architecture for knowledge tracing (KT), combining Hebbian memory encoding with gradient-based consolidation in a scalable, input-agnostic framework. KUL-KT adapts the principle of memory consolidation in neural systems, to student modeling by introducing two key innovations: (i) a time-decaying Hebbian memory update that enables graceful forgetting, and (ii) a novel Loss-aligned Internal Target (LIT) method to compute an ideal internal state, allowi…

Reasoning Like an Economist: Post-Training on Economic Problems Induces Strategic Generalization in LLMs
Yufa Zhou, Shaobo Wang, Xingyu Dong, Xiangqi Jin, Yifang Chen, Yue Min, Kexin Yang, Xingzhang Ren, Dayiheng Liu, Linfeng Zhang
https://arxiv.org/abs/2506.00577

Reasoning Like an Economist: Post-Training on Economic Problems Induces Strategic Generalization in LLMs
Directly training Large Language Models (LLMs) for Multi-Agent Systems (MAS) remains challenging due to intricate reward modeling, dynamic agent interactions, and demanding generalization requirements. This paper explores whether post-training techniques, specifically Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Rewards (RLVR), can effectively $\textit{generalize}$ to multi-agent scenarios. We use economic reasoning as a testbed, leveraging its strong foundations in m…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[3/6]:
- Know You First and Be You Better: Modeling Human-Like User Simulators via Implicit Profiles
Kuang Wang, Xianfei Li, Shenghao Yang, Li Zhou, Feng Jiang, Haizhou Li

This https://arxiv.org/abs/2505.14314 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

Low-Cost FlashAttention with Fused Exponential and Multiplication Hardware Operators
Attention mechanisms, particularly within Transformer architectures and large language models (LLMs), have revolutionized sequence modeling in machine learning and artificial intelligence applications. To compute attention for increasingly long sequences, specialized accelerators have been proposed to execute key attention steps directly in hardware. Among the various recently proposed architectures, those based on variants of the FlashAttention algorithm, originally designed for GPUs, stand ou…

This https://arxiv.org/abs/2503.08894 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Modeling and computation of the effective elastic behavior of parallelogram origami metamaterials
Origami metamaterials made of repeating unit cells of parallelogram panels joined at folds dramatically change their shape through a collective motion of their cells. Here we develop an effective elastic model and numerical method to study the large deformation response of these metamaterials under a broad class of loads. The model builds on an effective plate theory derived in our prior work [64]. The theory captures the overall shape change of all slightly stressed parallelogram origami defor…

Act-With-Think: Chunk Auto-Regressive Modeling for Generative Recommendation
Yifan Wang, Weinan Gan, Longtao Xiao, Jieming Zhu, Heng Chang, Haozhao Wang, Rui Zhang, Zhenhua Dong, Ruiming Tang, Ruixuan Li
https://arxiv.org/abs/2506.23643

Act-With-Think: Chunk Auto-Regressive Modeling for Generative Recommendation
Generative recommendation (GR) typically encodes behavioral or semantic aspects of item information into discrete tokens, leveraging the standard autoregressive (AR) generation paradigm to make predictions. However, existing methods tend to overlook their intrinsic relationship, that is, the semantic usually provides some reasonable explainability "$\textbf{why}$" for the behavior "$\textbf{what}$", which may constrain the full potential of GR. To this end, we present Chunk AutoRegressive Model…

This https://arxiv.org/abs/2503.03480 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning
Vision-language-action models (VLAs) show potential as generalist robot policies. However, these models pose extreme safety challenges during real-world deployment, including the risk of harm to the environment, the robot itself, and humans. How can safety constraints be explicitly integrated into VLAs? We address this by exploring an integrated safety approach (ISA), systematically modeling safety requirements, then actively eliciting diverse unsafe behaviors, effectively constraining VLA poli…

When Less Is More: Binary Feedback Can Outperform Ordinal Comparisons in Ranking Recovery
Shirong Xu, Jingnan Zhang, Junhui Wang
https://arxiv.org/abs/2507.01613

When Less Is More: Binary Feedback Can Outperform Ordinal Comparisons in Ranking Recovery
Paired comparison data, where users evaluate items in pairs, play a central role in ranking and preference learning tasks. While ordinal comparison data intuitively offer richer information than binary comparisons, this paper challenges that conventional wisdom. We propose a general parametric framework for modeling ordinal paired comparisons without ties. The model adopts a generalized additive structure, featuring a link function that quantifies the preference difference between two items and…

An Integrated Platform for LEED Certification Automation Using Computer Vision and LLM-RAG
Jooyeol Lee
https://arxiv.org/abs/2506.00888 https://

An Integrated Platform for LEED Certification Automation Using Computer Vision and LLM-RAG
The Leadership in Energy and Environmental Design (LEED) certification process is characterized by labor-intensive requirements for data handling, simulation, and documentation. This paper presents an automated platform designed to streamline key aspects of LEED certification. The platform integrates a PySide6-based user interface, a review Manager for process orchestration, and multiple analysis engines for credit compliance, energy modeling via EnergyPlus, and location-based evaluation. Key c…

Hierarchical Bayesian Knowledge Tracing in Undergraduate Engineering Education
Yiwei Sun
https://arxiv.org/abs/2506.00057 https://arx…

Hierarchical Bayesian Knowledge Tracing in Undergraduate Engineering Education
Educators teaching entry-level university engineering modules face the challenge of identifying which topics students find most difficult and how to support diverse student needs effectively. This study demonstrates a rigorous yet interpretable statistical approach -- hierarchical Bayesian modeling -- that leverages detailed student response data to quantify both skill difficulty and individual student abilities. Using a large-scale dataset from an undergraduate Statics course, we identified cl…

Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa RYu
https://arxiv.org/abs/2506.00056

Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
Artificial intelligence (AI) is reshaping inverse design across manufacturing domain, enabling high-performance discovery in materials, products, and processes. However, purely data-driven approaches often struggle in realistic settings characterized by sparse data, high-dimensional design spaces, and nontrivial physical constraints. This perspective argues for a new generation of design systems that transcend black-box modeling by integrating domain knowledge, physics-informed learning, and in…

Equivalence of Left- and Right-Invariant Extended Kalman Filters on Matrix Lie Groups
Finn G. Maurer, Erlend A. Basso, Henrik M. Schmidt-Didlaukies, Torleiv H. Bryne
https://arxiv.org/abs/2506.01514

Equivalence of Left- and Right-Invariant Extended Kalman Filters on Matrix Lie Groups
This paper derives the extended Kalman filter (EKF) for continuous-time systems on matrix Lie groups observed through discrete-time measurements. By modeling the system noise on the Lie algebra and adopting a Stratonovich interpretation for the stochastic differential equation (SDE), we ensure that solutions remain on the manifold. The derivation of the filter follows classical EKF principles, naturally integrating a necessary full-order covariance reset post-measurement update. A key contribut…

Emerging ML-AI Techniques for Analog and RF EDA
Zhengfeng Wu, Ziyi Chen, Nnaemeka Achebe, Vaibhav V. Rao, Pratik Shrestha, Ioannis Savidis
https://arxiv.org/abs/2506.00007

Emerging ML-AI Techniques for Analog and RF EDA
This survey explores the integration of machine learning (ML) into EDA workflows for analog and RF circuits, addressing challenges unique to analog design, which include complex constraints, nonlinear design spaces, and high computational costs. State-of-the-art learning and optimization techniques are reviewed for circuit tasks such as constraint formulation, topology generation, device modeling, sizing, placement, and routing. The survey highlights the capability of ML to enhance automation, …

This https://arxiv.org/abs/2501.18221 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Network Weighted Functional Regression: a method for modeling dependencies between functional data in a network
This paper focuses on predicting continuous signals in a sensor lab network, particularly studying microclimate changes. We propose two novel concepts: Network Functional Data (NFD), which represents time series signals as functions on network nodes, and the Network Weighted Functional Regression (NWFR) model, which analyzes relationships between functional responses and predictors in a weighted network. Additionally, we introduce a functional conformal method to provide prediction bands with g…

Quantifying Student Success with Generative AI: A Monte Carlo Simulation Informed by Systematic Review
Seyma Yaman Kayadibi
https://arxiv.org/abs/2507.01062

Quantifying Student Success with Generative AI: A Monte Carlo Simulation Informed by Systematic Review
The exponential development of generative artificial intelligence (GenAI) technologies like ChatGPT has raised increasing curiosity about their use in higher education, specifically with respect to how students view them, make use of them, and the implications for learning outcomes. This paper employs a hybrid methodological approach involving a systematic literature review and simulation-based modeling to explore student perceptions of GenAI use in the context of higher education. A total of n…

SPOT-Trip: Dual-Preference Driven Out-of-Town Trip Recommendation
Yinghui Liu, Hao Miao, Guojiang Shen, Yan Zhao, Xiangjie Kong, Ivan Lee
https://arxiv.org/abs/2506.01705

SPOT-Trip: Dual-Preference Driven Out-of-Town Trip Recommendation
Out-of-town trip recommendation aims to generate a sequence of Points of Interest (POIs) for users traveling from their hometowns to previously unvisited regions based on personalized itineraries, e.g., origin, destination, and trip duration. Modeling the complex user preferences--which often exhibit a two-fold nature of static and dynamic interests--is critical for effective recommendations. However, the sparsity of out-of-town check-in data presents significant challenges in capturing such us…

Hybrid Generative Modeling for Incomplete Physics: Deep Grey-Box Meets Optimal Transport
Gurjeet Sangra Singh, Maciej Falkiewicz, Alexandros Kalousis
https://arxiv.org/abs/2506.22204

Hybrid Generative Modeling for Incomplete Physics: Deep Grey-Box Meets Optimal Transport
Physics phenomena are often described by ordinary and/or partial differential equations (ODEs/PDEs), and solved analytically or numerically. Unfortunately, many real-world systems are described only approximately with missing or unknown terms in the equations. This makes the distribution of the physics model differ from the true data-generating process (DGP). Using limited and unpaired data between DGP observations and the imperfect model simulations, we investigate this particular setting by c…

This https://arxiv.org/abs/2310.16207 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Propensity weighting plus adjustment in proportional hazards model is not doubly robust
Recently, it has become common for applied works to combine commonly used survival analysis modeling methods, such as the multivariable Cox model and propensity score weighting, with the intention of forming a doubly robust estimator of an exposure effect hazard ratio that is unbiased in large samples when either the Cox model or the propensity score model is correctly specified. This combination does not, in general, produce a doubly robust estimator, even after regression standardization, whe…

This https://arxiv.org/abs/2505.17549 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

EGA: A Unified End-to-End Generative Framework for Industrial Advertising Systems
Online industrial advertising system is fundamentally constrained by the inefficiency of multi-stage cascaded architectures, which filter out high-potential candidates early and fragment business decision logic across independent modules. Although recent advances in generative recommendation offer end-to-end solutions, they fall short of practical advertising requirements, lacking explicit modeling of bidding, creative selection, allocation mechanism, and payment computation that are essential …

Flexible Selective Inference with Flow-based Transport Maps
Sifan Liu, Snigdha Panigrahi
https://arxiv.org/abs/2506.01150 https://arx…

Flexible Selective Inference with Flow-based Transport Maps
Data-carving methods perform selective inference by conditioning the distribution of data on the observed selection event. However, existing data-carving approaches typically require an analytically tractable characterization of the selection event. This paper introduces a new method that leverages tools from flow-based generative modeling to approximate a potentially complex conditional distribution, even when the underlying selection event lacks an analytical description -- take, for example,…