Tootfinder

The role of small-angle electron-electron scattering in transverse magnetic focusing experiment
Dmitry A. Egorov, Dmitriy A. Pokhabov, Evgeny Yu. Zhdanov, Andrey A. Shevyrin, Askhat K. Bakarov, Alexander A. Shklyaev, Arthur G. Pogosov
https://arxiv.org/abs/2506.09432

The role of small-angle electron-electron scattering in transverse magnetic focusing experiment
We demonstrate the crucial role of small-angle scattering in transverse magnetic focusing (TMF) in ballistic GaAs/AlGaAs heterostructures. Measurements in various samples show that the role significantly depends on their geometry. We propose a phenomenological model parameterizing this dependence with the angular acceptance of the detecting contact. This model is consistent with the diversity of experimental data and therefore enables accurate extraction of the key characteristic of inter-elect…

🚀 Getting Started
For Developers:
Quick API integration
client = OpenAI(base_url="https://api.moonshot.ai/v1") response = client.chat.completions.create( model="kimi-k2-instruct", messages=[{"role": "user", "content": "Build me a we…

VerIF: Verification Engineering for Reinforcement Learning in Instruction Following
Hao Peng, Yunjia Qi, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li
https://arxiv.org/abs/2506.09942 …

VerIF: Verification Engineering for Reinforcement Learning in Instruction Following
Reinforcement learning with verifiable rewards (RLVR) has become a key technique for enhancing large language models (LLMs), with verification engineering playing a central role. However, best practices for RL in instruction following remain underexplored. In this work, we explore the verification challenge in RL for instruction following and propose VerIF, a verification method that combines rule-based code verification with LLM-based verification from a large reasoning model (e.g., QwQ-32B). …

Growth of Structural Lengthscale in Kob Andersen Binary Mixtures: Role of medium range order
Sanket Kumawat, Mohit Sharma, Ujjwal Kumar Nandi, Indrajit Tah, Sarika Maitra Bhattacharyya
https://arxiv.org/abs/2507.07819

Growth of Structural Lengthscale in Kob Andersen Binary Mixtures: Role of medium range order
A central and extensively debated question in glass physics concerns whether a single, growing lengthscale fundamentally controls glassy dynamics, particularly in systems lacking obvious structural motifs or medium range crystalline order (MRCO). In this work, we investigate structural and dynamical lengthscales in supercooled liquids using the Kob Andersen binary Lennard Jones (KALJ) model in two compositions: 80:20 and 60:40. We compute the dynamical lengthscale from displacement displacement…

A First Look at Bugs in LLM Inference Engines
Mugeng Liu, Siqi Zhong, Weichen Bi, Yixuan Zhang, Zhiyang Chen, Zhenpeng Chen, Xuanzhe Liu, Yun Ma
https://arxiv.org/abs/2506.09713

A First Look at Bugs in LLM Inference Engines
Large language model-specific inference engines (in short as \emph{LLM inference engines}) have become a fundamental component of modern AI infrastructure, enabling the deployment of LLM-powered applications (LLM apps) across cloud and local devices. Despite their critical role, LLM inference engines are prone to bugs due to the immense resource demands of LLMs and the complexities of cross-platform compatibility. However, a systematic understanding of these bugs remains lacking. To bridge this…

Evolutionary model for energy trading in community microgrids using Hawk-Dove strategies
Viorica Rozina Chifu, Tudor Cioara, Cristina Bianca Pop, Ionut Anghel
https://arxiv.org/abs/2506.06325

Evolutionary model for energy trading in community microgrids using Hawk-Dove strategies
This paper proposes a decentralized model of energy cooperation between microgrids, in which decisions are made locally, at the level of the microgrid community. Each microgrid is modeled as an autonomous agent that adopts a Hawk or Dove strategy, depending on the level of energy stored in the battery and its role in the energy trading process. The interactions between selling and buying microgrids are modeled through an evolutionary algorithm. An individual in the algorithm population is repre…

Non-Equilibrium Origin of Native Ring Anisotropy in Amorphous Systems
Zihang Wang, Dirk Bouwmeester
https://arxiv.org/abs/2506.08491 https://

Non-Equilibrium Origin of Native Ring Anisotropy in Amorphous Systems
Native ring structures within amorphous networks play a critical role in determining structural and optical properties, in part due to their ability to host dopants such as rare earth ions in silicate systems. In this work, we demonstrate that the universal features of structural anisotropy in amorphous networks can be efficiently simulated using a model based on stochastically deformed, edge sharing N member native ring structures. This model isolates and characterizes the structural anisotrop…

This https://arxiv.org/abs/2505.17282 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Attention with Trained Embeddings Provably Selects Important Tokens
Token embeddings play a crucial role in language modeling but, despite this practical relevance, their theoretical understanding remains limited. Our paper addresses the gap by characterizing the structure of embeddings obtained via gradient descent. Specifically, we consider a one-layer softmax attention model with a linear head for binary classification, i.e., $\texttt{Softmax}( p^\top E_X^\top ) E_X v = \frac{ \sum_{i=1}^T \exp(p^\top E_{x_i}) E_{x_i}^\top v}{\sum_{j=1}^T \exp(p^\top E_{x_{j…

A Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering
Shahana Yasmin Chowdhury, Bithi Banik, Md Tamjidul Hoque, Shreya Banerjee
https://arxiv.org/abs/2507.07046

A Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering
Nowadays, speech emotion recognition (SER) plays a vital role in the field of human-computer interaction (HCI) and the evolution of artificial intelligence (AI). Our proposed DCRF-BiLSTM model is used to recognize seven emotions: neutral, happy, sad, angry, fear, disgust, and surprise, which are trained on five datasets: RAVDESS (R), TESS (T), SAVEE (S), EmoDB (E), and Crema-D (C). The model achieves high accuracy on individual datasets, including 97.83% on RAVDESS, 97.02% on SAVEE, 95.10% for …

Dominance or Fair Play in Social Networks? A Model of Influencer Popularity Dynamic
Franco Galante, Chiara Ravazzi, Luca Vassio, Michele Garetto, Emilio Leonardi
https://arxiv.org/abs/2507.03448

Dominance or Fair Play in Social Networks? A Model of Influencer Popularity Dynamic
This paper presents a data-driven mean-field approach to model the popularity dynamics of users seeking public attention, i.e., influencers. We propose a novel analytical model that integrates individual activity patterns, expertise in producing viral content, exogenous events, and the platform's role in visibility enhancement, ultimately determining each influencer's success. We analytically derive sufficient conditions for system ergodicity, enabling predictions of popularity distributions. A…

This https://arxiv.org/abs/2411.06564 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Distributionally Robust Adaptive Beamforming
As a fundamental technique in array signal processing, beamforming plays a crucial role in amplifying signals of interest (SoI) while mitigating interference plus noise (IPN). When uncertainties exist in the signal model or the data size of snapshots is limited, the performance of beamformers significantly degrades. In this article, we comprehensively study the conceptual system, theoretical analysis, and algorithmic design for robust beamforming against uncertainties in the assumed snapshot or…

Emulating compact binary population synthesis simulations with robust uncertainty quantification and model comparison: Bayesian normalizing flows
Anarya Ray
https://arxiv.org/abs/2506.05657

Emulating compact binary population synthesis simulations with robust uncertainty quantification and model comparison: Bayesian normalizing flows
Population synthesis simulations of compact binary coalescences~(CBCs) play a crucial role in extracting astrophysical insights from an ensemble of gravitational wave~(GW) observations. However, realistic simulations are costly to implement for a dense grid of initial conditions. Normalizing flows can emulate the distribution functions of a simulated population of binary parameters and thereby enable empirical constraints on the astrophysical initial conditions and branching fractions of variou…

Topological Freeze-out by Semi-Annihilation
Joe Davighi, Serah Moldovsky, Hitoshi Murayama, Christiane Scherb, Nudzeim Selimovic
https://arxiv.org/abs/2506.05468

Topological Freeze-out by Semi-Annihilation
We point out that a QCD-like dark sector can be coupled to the Standard Model by gauging the topological Skyrme current, which measures the dark baryon number in the infrared, to give a technically natural model for dark matter. This coupling allows for a semi-annihilation process $χχ\rightarrow χX_μ$, where $X_μ$ is the gauge boson mediator and $χ$ a dark pion field, which plays the dominant role in setting the dark matter relic abundance. The topological interaction is purely $p$-wave a…

This https://arxiv.org/abs/2109.08659 has been replaced.
link: https://scholar.google.com/scholar?q=a

Thermalization in asymmetric harmonic chains
The symmetry of the interparticle interaction potential (IIP) plays a critical role in determining the thermodynamic and transport properties of solids. This study investigates the isolated effect of IIP asymmetry on thermalization. Asymmetry and nonlinearity are typically intertwined. To isolate the effect of asymmetry, we introduce a one-dimensional asymmetric harmonic (AH) model whose IIP possesses asymmetry but no nonlinearity, evidenced by energy-independent vibrational frequencies. Extens…

Entanglement switching via mobility edges in a quasiperiodic chain
YouYoung Joung, Junmo Jeon, SungBin Lee
https://arxiv.org/abs/2507.06305 https://…

Entanglement switching via mobility edges in a quasiperiodic chain
We propose quasiperiodic chains with tunable mobility edge physics, as a promising platform for engineering long-range quantum entanglement. Using the generalized Aubry-André model, we show that the mobility edges play a key role in manipulating long-range indirect interactions in these systems. Near the mobility edge, critical states exhibit unexpectedly strong correlations between sites that share similar local structures, regardless of their spatial separation. Remarkably, by tuning the mob…

Memory-Driven Bounded Confidence Opinion Dynamics: A Hegselmann-Krause Model Based on Fractional-Order Methods
Meiru Jiang, Wei Su, Guojian Ren, Yongguang Yu
https://arxiv.org/abs/2506.04701

Memory-Driven Bounded Confidence Opinion Dynamics: A Hegselmann-Krause Model Based on Fractional-Order Methods
Memory effects play a crucial role in social interactions and decision-making processes. This paper proposes a novel fractional-order bounded confidence opinion dynamics model to characterize the memory effects in system states. Building upon the Hegselmann-Krause framework and fractional-order difference, a comprehensive model is established that captures the persistent influence of historical information. Through rigorous theoretical analysis, the fundamental properties including convergence …

Testing Hypotheses of Covariate Effects on Topics of Discourse
Gabriel Phelan, David A. Campbell
https://arxiv.org/abs/2506.05570 https://

Testing Hypotheses of Covariate Effects on Topics of Discourse
We introduce an approach to topic modelling with document-level covariates that remains tractable in the face of large text corpora. This is achieved by de-emphasizing the role of parameter estimation in an underlying probabilistic model, assuming instead that the data come from a fixed but unknown distribution whose statistical functionals are of interest. We propose combining a convex formulation of non-negative matrix factorization with standard regression techniques as a fast-to-compute and…

"Ideal" Topological Heavy Fermion Model in Two-dimensional Moir\'e Heterostructures with Type-II Band Alignment
Yunzhe Liu, Anoj Aryal, Dumitru Calugaru, Zhenyao Fang, Kaijie Yang, Haoyu Hu, Qimin Yan, B. Andrei Bernevig, Chao-xing Liu
https://arxiv.org/abs/2507.06168

"Ideal" Topological Heavy Fermion Model in Two-dimensional Moiré Heterostructures with Type-II Band Alignment
Topological flat bands play an essential role in inducing exotic interacting physics, ranging from fractional Chern insulators to superconductivity, in moiré materials. When topological flat bands possess concentrated quantum geometry, a topological heavy fermion (THF) model was proposed as the starting point to describe the interacting moiré physics. In this work, we propose a design principle for realizing "ideal" THF model, which can host an exact flat band with "ideal quantum geometry", n…

Not that America should hold us up as a role model, of course.
England turned 250 in the second half of the 12th century (depending on how you define "England" as a country), when kings and barons were romping around slaughtering each other and the population with gay abandon.
Great Britain turned 250 in 1957, at which point we'd only just finally got past post-WW2 rationing and still possibly clung on to some delusions of being an Empire.
And the UK has another 26 years to go before we get to have a 250th birthday. Maybe we'll be doing good by then (but probably not).

Self-organized first-order transition from foreshock to mainshock in earthquake sequences induced by heat, fluid pressure, and porosity
Takehito Suzuki, Hiroshi Matsukawa
https://arxiv.org/abs/2507.05546

Self-organized first-order transition from foreshock to mainshock in earthquake sequences induced by heat, fluid pressure, and porosity
Earthquake cycles are studied by taking into account the interactions among slip, fluid pressure, temperature, and porosity on the fault planes, which are known to play a crucial role in earthquake dynamics. The spring-block model with a single block is employed. A first-order transition from foreshock to mainshock occurring spontaneously in earthquake sequences is discovered both analytically and numerically. This transition is induced by these interactions. It is shown that the function of th…

Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis
Xintong Hu, Yixuan Chen, Rui Yang, Wenxiang Guo, Changhao Pan
https://arxiv.org/abs/2507.06116

Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis
Automatic speech quality assessment plays a crucial role in the development of speech synthesis systems, but existing models exhibit significant performance variations across different granularity levels of prediction tasks. This paper proposes an enhanced MOS prediction system based on self-supervised learning speech models, incorporating a Mixture of Experts (MoE) classification head and utilizing synthetic data from multiple commercial generation models for data augmentation. Our method buil…

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods
Yifan Hao, Xingyuan Pan, Hanning Zhang, Chenlu Ye, Rui Pan, Tong Zhang
https://arxiv.org/abs/2506.01901

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods
Supervised fine-tuning (SFT) on domain-specific data is the dominant approach for adapting foundation models to specialized tasks. However, it has been observed that SFT models tend to forget knowledge acquired during pretraining. In vision models, ensembling a pretrained model with its fine-tuned counterpart has been shown to mitigate this issue. In this work, we demonstrate that the same holds for language models, and, more strikingly, we observe an overadaptation phenomenon: the ensemble mod…

Experimental demonstration of the clock asynchrony model in space-borne gravitational wave detection
Ming-Yang Xua, Yu-Jie Tan, Ning Ma, Ao-Ting Fang, Yi-Jun Xia, Cheng-Gang Shao
https://arxiv.org/abs/2507.02286

Experimental demonstration of the clock asynchrony model in space-borne gravitational wave detection
Space-borne gravitational wave detection will open the observation window in the 0.1 mHz$-$1 Hz bandwidth, playing a crucial role in the development of cosmology and physics. Precise clock synchronization among satellites is essential for the accurate detection of gravitational wave signals. However, the independent clock counting mechanisms of each satellite pose a significant challenge. This work reports the mathematical model of clock asynchrony, which is mainly dominated by the constant ter…

Constraining $\Lambda$CDM, $\omega$CDM, and $\omega_0\omega_a$CDM models with DESI DR2 BAO: Redshift-Resolved Diagnostics and the Role of $r_d$
Seokcheon Lee
https://arxiv.org/abs/2507.01380

Constraining $Λ$CDM, $ω$CDM, and $ω_0ω_a$CDM models with DESI DR2 BAO: Redshift-Resolved Diagnostics and the Role of $r_d$
We present a detailed comparison of $Λ$CDM, $ω$CDM, and $ω_0ω_a$CDM cosmologies using the latest redshift-resolved BAO measurements from DESI DR2. By separating $r_d$-dependent and $r_d$-independent observables and applying both fixed and model-derived sound horizon values, we test the consistency of each model across 24 observables spanning redshift 0.2 to 2.3. Our results show that the $ω$CDM model is statistically favored over $Λ$CDM, with lower $χ^2$, AIC, and BIC values and reduced…

Assessing Future Wind Energy Potential under Climate Change: The Critical Role of Multi-Model Ensembles in Robustness Assessment
Andrea Lira-Loarca, Francesco Ferrari, Andrea Mazzino
https://arxiv.org/abs/2505.24463

Assessing Future Wind Energy Potential under Climate Change: The Critical Role of Multi-Model Ensembles in Robustness Assessment
Accurate projections of wind energy potential under climate change are critical for effective long-term energy planning. While previous studies have highlighted the value of multi-model ensembles, they often fall short in capturing the full spectrum of uncertainties and temporal dynamics relevant to wind resource reliability. This paper presents one of the most comprehensive assessments to date, leveraging a large ensemble of 21 high-resolution RCM-GCM combinations from the EURO-CORDEX initiati…

MMOC: Self-Supervised EEG Emotion Recognition Framework with Multi-Model Online Collaboration
Hanqi Wang, Yang Liu, Peng Ye, Liang Song
https://arxiv.org/abs/2507.03977

MMOC: Self-Supervised EEG Emotion Recognition Framework with Multi-Model Online Collaboration
Electroencephalography (EEG) emotion recognition plays a crucial role in human-computer interaction, particularly in healthcare and neuroscience. While supervised learning has been widely used, its reliance on manual annotations introduces high costs and potential bias. Self-supervised learning (SSL) offers a promising alternative by generating labels through pretext tasks. However, high inter-subject variability in EEG signals leads to significant data drift, limiting self-supervised models' g…

AGNI: A radiative-convective model for lava planet atmospheres
Harrison Nicholls, Raymond Pierrehumbert, Tim Lichtenberg
https://arxiv.org/abs/2506.00091 h…

AGNI: A radiative-convective model for lava planet atmospheres
It is important that we are able to accurately model the atmospheres of (exo)planets. This is because atmospheres play a central role in setting a planet's thermochemical environment at a given point in time, and also in regulating how it evolves over geological timescales. Additionally, it is primarily by observation of their atmospheres that we are able to characterise exoplanets. There is particular demand for accurate models in the context of so-called lava worlds: planets with molten inter…

Towards Efficient Speech-Text Jointly Decoding within One Speech Language Model
Haibin Wu, Yuxuan Hu, Ruchao Fan, Xiaofei Wang, Kenichi Kumatani, Bo Ren, Jianwei Yu, Heng Lu, Lijuan Wang, Yao Qian, Jinyu Li
https://arxiv.org/abs/2506.04518

Towards Efficient Speech-Text Jointly Decoding within One Speech Language Model
Speech language models (Speech LMs) enable end-to-end speech-text modelling within a single model, offering a promising direction for spoken dialogue systems. The choice of speech-text jointly decoding paradigm plays a critical role in performance, efficiency, and alignment quality. In this work, we systematically compare representative joint speech-text decoding strategies-including the interleaved, and parallel generation paradigms-under a controlled experimental setup using the same base lan…

This https://arxiv.org/abs/2505.09999 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

ServeGen: Workload Characterization and Generation of Large Language Model Serving in Production
With the widespread adoption of Large Language Models (LLMs), serving LLM inference requests has become an increasingly important task, attracting active research advancements. Practical workloads play an essential role in this process: they are critical for motivating and benchmarking serving techniques and systems. However, the existing understanding of real-world LLM serving workloads is limited due to the lack of a comprehensive workload characterization. Prior analyses remain insufficient …

The Dynamic Role of Aerosol and Exudate Transport in the Diffusion of Lung Infection in Respiratory Infectious Diseases (taking SARS-CoV-2 as an example): A Hypothesis Model
Shi Qiru
https://arxiv.org/abs/2505.24057

The Dynamic Role of Aerosol and Exudate Transport in the Diffusion of Lung Infection in Respiratory Infectious Diseases (taking SARS-CoV-2 as an example): A Hypothesis Model
This paper proposes a hypothetical model for the dual role of respiratory aerosols and inflammatory exudates in the dynamics and progression of SARS-CoV-2 lung infection. Starting from a new paradigm in infectious disease transmission, we reflect on the often-overlooked role of physical transmission media within the host individual. The hypothesis posits that tiny aerosols (including those inhaled externally and those self-generated and re-inhaled by the host) play a crucial role in the initial…

Beyond water limitation in vegetation-autotoxicity patterning: a cross-diffusion model
Francesco Giannino, Annalisa Iuorio, Cinzia Soresina
https://arxiv.org/abs/2506.03981

Beyond water limitation in vegetation-autotoxicity patterning: a cross-diffusion model
Many mathematical models describing vegetation patterns are based on biomass--water interactions, due to the impact of this limited resource in arid and semi-arid environments. However, in recent years, a novel biological factor called autotoxicity has proved to play a key role in vegetation spatiotemporal dynamics, particularly by inhibiting biomass growth and increasing its natural mortality rate. In a standard reaction-diffusion framework, biomass-toxicity dynamics alone are unable to suppor…

Agentic AI in Product Management: A Co-Evolutionary Model
Nishant A. Parikh
https://arxiv.org/abs/2507.01069 https://arxiv.org/pdf/25…

Agentic AI in Product Management: A Co-Evolutionary Model
This study explores agentic AI's transformative role in product management, proposing a conceptual co-evolutionary framework to guide its integration across the product lifecycle. Agentic AI, characterized by autonomy, goal-driven behavior, and multi-agent collaboration, redefines product managers (PMs) as orchestrators of socio-technical ecosystems. Using systems theory, co-evolutionary theory, and human-AI interaction theory, the framework maps agentic AI capabilities in discovery, scoping, b…

SMART: Simulated Students Aligned with Item Response Theory for Question Difficulty Prediction
Alexander Scarlatos, Nigel Fernandez, Christopher Ormerod, Susan Lottridge, Andrew Lan
https://arxiv.org/abs/2507.05129

SMART: Simulated Students Aligned with Item Response Theory for Question Difficulty Prediction
Item (question) difficulties play a crucial role in educational assessments, enabling accurate and efficient assessment of student abilities and personalization to maximize learning outcomes. Traditionally, estimating item difficulties can be costly, requiring real students to respond to items, followed by fitting an item response theory (IRT) model to get item difficulty estimates. This approach cannot be applied to the cold-start setting for previously unseen items either. In this work, we pr…

Continuous spin superparticle model
I. L. Buchbinder, S. A. Fedoruk
https://arxiv.org/abs/2506.19709 https://arxiv.org/pdf/2506.19709…

Continuous spin superparticle model
We construct a new model of a particle propagating in $4D$, ${\cal N}=1$ superspace that describes the dynamics of a continuous spin irreducible representation of the Poincaré supergroup. The model is characterized by two-component Weyl spinor additional even variables playing the role of extra coordinates. A canonical formulation, specific local fermionic $κ$-symmetry, and a compete system of bosonic and fermionic constraints are derived. All bosonic constrains are first-class, while fermion…

A semi-analytical solution for the lubrication force between two spheres approaching in viscoelastic fluids described by the Oldroyd-B model under small Deborah numbers
Alan Rosales-Romero, Adolfo V\'azquez-Quesada, Marco Ellero, J. Esteban L\'opez-Aguilar
https://arxiv.org/abs/2507.02266…

A semi-analytical solution for the lubrication force between two spheres approaching in viscoelastic fluids described by the Oldroyd-B model under small Deborah numbers
Viscoelastic fluids play a critical role in various engineering and biological applications, where their lubrication properties are strongly influenced by relaxation times ranging from microseconds to minutes. Although the lubrication mechanism for Newtonian fluids is well-established, its extension to viscoelastic materials - particularly under squeezing flow conditions - remains less explored. This study presents a semi-analytical solution for the lubrication force between two spheres approac…

V-Associated Production & Vector Boson Fusion as an LHC Signature of CP Violation
Rodrigo Capucha, \'Alvaro Lozano-Onrubia, Luca Merlo, Jos\'e Miguel No, Rui Santos
https://arxiv.org/abs/2507.05942

V-Associated Production & Vector Boson Fusion as an LHC Signature of CP Violation
We investigate the role of vector boson fusion (VBF) and associated production with an electroweak boson (V-AP) of beyond-the-Standard-Model Higgses at the LHC in probing CP violation in extended Higgs sectors. Resonant production of a new Higgs boson through V-AP/VBF subsequently decaying into a Z boson and a 125 GeV Higgs boson h would be a robust sign of CP violation, since h has been measured to be (predominantly) a CP-even state. After identifying this and other sets of signatures which re…

This https://arxiv.org/abs/2505.01199 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning
Medical audio signals, such as heart and lung sounds, play a crucial role in clinical diagnosis. However, analyzing these signals remains challenging: traditional methods rely on handcrafted features or supervised deep learning models that demand extensive labeled datasets, limiting their scalability and applicability. To address these issues, we propose CaReAQA, an audio-language model that integrates a foundation audio model with the reasoning capabilities of large language models, enabling c…

EIM-TRNG: Obfuscating Deep Neural Network Weights with Encoding-in-Memory True Random Number Generator via RowHammer
Ranyang Zhou, Abeer Matar A. Almalky, Gamana Aragonda, Sabbir Ahmed, Filip Roth Tr{\o}nnes-Christensen, Adnan Siraj Rakin, Shaahin Angizi
https://arxiv.org/abs/2507.02206 …

EIM-TRNG: Obfuscating Deep Neural Network Weights with Encoding-in-Memory True Random Number Generator via RowHammer
True Random Number Generators (TRNGs) play a fundamental role in hardware security, cryptographic systems, and data protection. In the context of Deep NeuralNetworks (DNNs), safeguarding model parameters, particularly weights, is critical to ensure the integrity, privacy, and intel-lectual property of AI systems. While software-based pseudo-random number generators are widely used, they lack the unpredictability and resilience offered by hardware-based TRNGs. In this work, we propose a novel an…

The model fitting of Gaia DR3 Classical Cepheid light and radial velocity curves
R. Molinaro, M. Marconi, G. De Somma, V. Ripepi, S. Leccia, I. Musella, T. Sicignano, E. Trentin, M. Gatto
https://arxiv.org/abs/2506.22635

The model fitting of Gaia DR3 Classical Cepheid light and radial velocity curves
Classical Cepheids are key astrophysical laboratories for studying stellar structure and evolution, and for calibrating the cosmic distance scale. Despite major progress, uncertainties remain regarding their masses, luminosities, distances, and the role of processes like core overshooting, rotation, and mass loss. The high-precision data from ESA Gaia's third data release offer an opportunity to address these issues. This study aims to estimate the structural parameters and distances of a sampl…

Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives
Vu Thi Huong, Ida Litzel, Thorsten Koch
https://arxiv.org/abs/2506.04045

Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives
Fuzzy clustering, which allows an article to belong to multiple clusters with soft membership degrees, plays a vital role in analyzing publication data. This problem can be formulated as a constrained optimization model, where the goal is to minimize the discrepancy between the similarity observed from data and the similarity derived from a predicted distribution. While this approach benefits from leveraging state-of-the-art optimization algorithms, tailoring them to work with real, massive dat…

Xray2Xray: World Model from Chest X-rays with Volumetric Context
Zefan Yang, Xinrui Song, Xuanang Xu, Yongyi Shi, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
https://arxiv.org/abs/2506.19055

Xray2Xray: World Model from Chest X-rays with Volumetric Context
Chest X-rays (CXRs) are the most widely used medical imaging modality and play a pivotal role in diagnosing diseases. However, as 2D projection images, CXRs are limited by structural superposition, which constrains their effectiveness in precise disease diagnosis and risk prediction. To address the limitations of 2D CXRs, this study introduces Xray2Xray, a novel World Model that learns latent representations encoding 3D structural information from chest X-rays. Xray2Xray captures the latent rep…

Consciousness via MIPT?
Alexander Gorsky
https://arxiv.org/abs/2506.04875 https://arxiv.org/pdf/2506.04875

Consciousness via MIPT?
The measurement-induced phase transition (MIPT) is a recently formulated phenomenon in out-of-equilibrium systems. The competition between unitary evolutions and measurement-induced non-unitaries leads to the transition between the entangled and disentangled phases at some critical measurement rate. We conjecture that self-organized MIPT plays a key role in the generative model of cognitive networks and the formation of the state of consciousness in the "newborn-adult" transition. To this aim, …

Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends
Tian-Yu Xiang, Ao-Qun Jin, Xiao-Hu Zhou, Mei-Jiang Gui, Xiao-Liang Xie, Shi-Qi Liu, Shuang-Yi Wang, Sheng-Bin Duan, Fu-Chao Xie, Wen-Kai Wang, Si-Cheng Wang, Ling-Yun Li, Tian Tu, Zeng-Guang Hou
https://arxiv.org/abs/2506.20966…

Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends
Vision-language-action (VLA) models extend vision-language models (VLM) by integrating action generation modules for robotic manipulation. Leveraging strengths of VLM in vision perception and instruction understanding, VLA models exhibit promising generalization across diverse manipulation tasks. However, applications demanding high precision and accuracy reveal performance gaps without further adaptation. Evidence from multiple domains highlights the critical role of post-training to align fou…

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…

The exchange-correlation dipole moment dispersion method
Kyle R Bryenton, Erin R Johnson
https://arxiv.org/abs/2506.02352 https://arx…

The exchange-correlation dipole moment dispersion method
Density-functional theory (DFT) has become the workhorse of modern computational chemistry, with dispersion corrections such as the exchange-hole dipole moment (XDM) model playing a key role in high-accuracy modelling of large-scale systems. Here, we introduce a new physics-guided XDM variant, termed the exchange-correlation dipole moment (XCDM) model, which supplements XDM with same- and opposite-spin dynamical correlation terms, substantially improving accuracy for molecular $C_6$ dispersion …

Aggregation of vortex structures in 2D: the blob-wave system and its role in zonal flows
Franco Flandoli, Matteo Palmieri, Milo Viviani
https://arxiv.org/abs/2505.22700

Aggregation of vortex structures in 2D: the blob-wave system and its role in zonal flows
We give a rigorous mathematical result, supported by accurate numerical simulations, of the aggregation of a concentrated vortex blob with an underlying non constant vorticity field: the blob moves in the direction of the gradient of the field. It is a unique example of Lagrangian explanation of aggregation of vortex structures of the same sign in 2D inviscid fluids. A conceptual model of zonal flow maintenance and modulation based on this idea is then developed, which produces shapes in accord…

Nested importance sampling for Bayesian inference: error bounds and the role of dimension
Fabi\'an Gonz\'alez, V\'ictor Elvira, Joaqu\'in Miguez
https://arxiv.org/abs/2507.04163

Nested importance sampling for Bayesian inference: error bounds and the role of dimension
Many Bayesian inference problems involve high dimensional models for which only a subset of the model variables are actual estimation targets. All other variables are just nuisance variables that one would ideally like to integrate out analytically. Unfortunately, such integration is often impossible. However, there are several computational methods that have been proposed over the past 15 years that replace intractable analytical marginalisation by numerical integration, typically using differ…

Memory as a Service (MaaS): Rethinking Contextual Memory as Service-Oriented Modules for Collaborative Agents
Haichang Li
https://arxiv.org/abs/2506.22815 …

Memory as a Service (MaaS): Rethinking Contextual Memory as Service-Oriented Modules for Collaborative Agents
This position paper aims to rethink the role and design of memory in Large Language Model (LLM)-based agent systems. We observe that while current memory practices have begun to transcend the limitations of single interactions, they remain conceptually grounded in "bound memory" in terms of design concept-where memory is treated as local state attached to specific context or entities, forming "memory silos" that impede cross-entity collaboration. To overcome this architectural bottleneck, this …

This https://arxiv.org/abs/2310.00468 has been replaced.
link: https://scholar.google.com/scholar?q=a

When Should a Leader Act Suboptimally? The Role of Inferability in Repeated Stackelberg Games
When interacting with other decision-making agents in non-adversarial scenarios, it is critical for an autonomous agent to have inferable behavior: The agent's actions must convey their intention and strategy. We model the inferability problem using Stackelberg games with observations where a leader and a follower repeatedly interact. During the interactions, the leader uses a fixed mixed strategy. The follower does not know the leader's strategy and dynamically reacts to the statistically infe…

This https://arxiv.org/abs/2412.13807 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

The role of accreted and in situ populations in shaping the stellar halos of low-mass galaxies
The stellar halos of dwarf galaxies are becoming an object of interest in the extragalactic community due to their detection in some recent observations. Additionally, new cosmological simulations of very high resolution were performed, allowing their study. These stellar halos could help shed light on our understanding of the assembly of dwarf galaxies and their evolution, and allow us to test the hierarchical model for the formation of structures at small scales. We aim to characterise the st…

Tribrid Inflation, Type II Leptogenesis, and Observable Gravitational Waves in $SU(3)_c \times SU(2)_L \times SU(2)_R \times U(1)_{B-L}$
Waqas Ahmed, Saleh O. Allehabi, Farishta Israr, Mansoor Ur Rehman
https://arxiv.org/abs/2507.05564

Tribrid Inflation, Type II Leptogenesis, and Observable Gravitational Waves in $SU(3)_c \times SU(2)_L \times SU(2)_R \times U(1)_{B-L}$
We present a concrete realization of tribrid inflation within the framework of the gauge group $SU(3)_c \times SU(2)_L \times SU(2)_R \times U(1)_{B-L}$. In this model, inflation is driven by the neutral components of left-handed Higgs triplets, which also play a central role in generating the observed baryon asymmetry of the universe via non-thermal leptogenesis. Tiny neutrino masses arise naturally through a type-II seesaw mechanism, facilitated by right-handed triplet fields. Supergravity co…

Transport Theory and Correlation Measurements: Coming to Terms on Emission Sources
Pierre Nzabahimana, Pawel Danielewicz, Giuseppe Verde
https://arxiv.org/abs/2506.01271

Transport Theory and Correlation Measurements: Coming to Terms on Emission Sources
Two-particle correlations play a pivotal role in understanding the space-time characteristics of particle emission in Heavy-ion collisions. These characteristics are typically represented by a relative emission source and can be obtained using transport model simulations such as the Boltzmann- Uehling-Uhlenbeck (BUU) transport model. In this paper, we utilize the BUU transport model to simulate the p-p source. Subsequently, we integrate this source and the p-p kernel within the KP formula to ca…

This https://arxiv.org/abs/2406.19157 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

How to build your latent Markov model -- the role of time and space
Statistical models that involve latent Markovian state processes have become immensely popular tools for analysing time series and other sequential data. However, the plethora of model formulations, the inconsistent use of terminology, and the various inferential approaches and software packages can be overwhelming to practitioners, especially when they are new to this area. With this review-like paper, we thus aim to provide guidance for both statisticians and practitioners working with latent…

Towards Structure-aware Model for Multi-modal Knowledge Graph Completion
Linyu Li, Zhi Jin, Yichi Zhang, Dongming Jin, Chengfeng Dou, Yuanpeng He, Xuan Zhang, Haiyan Zhao
https://arxiv.org/abs/2505.21973

Towards Structure-aware Model for Multi-modal Knowledge Graph Completion
Knowledge graphs (KGs) play a key role in promoting various multimedia and AI applications. However, with the explosive growth of multi-modal information, traditional knowledge graph completion (KGC) models cannot be directly applied. This has attracted a large number of researchers to study multi-modal knowledge graph completion (MMKGC). Since MMKG extends KG to the visual and textual domains, MMKGC faces two main challenges: (1) how to deal with the fine-grained modality information interacti…

InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
Daniya Najiha Abdul Kareem, Abdul Hannan, Mubashir Noman, Jean Lahoud, Mustansar Fiaz, Hisham Cholakkal
https://arxiv.org/abs/2506.12208

InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
Accurate microscopic medical image segmentation plays a crucial role in diagnosing various cancerous cells and identifying tumors. Driven by advancements in deep learning, convolutional neural networks (CNNs) and transformer-based models have been extensively studied to enhance receptive fields and improve medical image segmentation task. However, they often struggle to capture complex cellular and tissue structures in challenging scenarios such as background clutter and object overlap. Moreove…

This https://arxiv.org/abs/2410.03874 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

A differentiable N-body code for transit timing and dynamical modelling -- II. Photodynamics
Exoplanet transits contain substantial information about the architecture of a system. By fitting transit light curves we can extract dynamical parameters and place constraints on the properties of the planets and their host star. Having a well-defined probabilistic model plays a crucial role in making robust measurements of these parameters, and the ability to differentiate the model provides access to more robust inference tools. Gradient-based inference methods can allow for more rapid and a…

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
Junli Shao, Jing Dong, Dingzhou Wang, Kowei Shih, Dannier Li, Chengrui Zhou
https://arxiv.org/abs/2506.11421

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
With the rapid growth of Internet services, recommendation systems play a central role in delivering personalized content. Faced with massive user requests and complex model architectures, the key challenge for real-time recommendation systems is how to reduce inference latency and increase system throughput without sacrificing recommendation quality. This paper addresses the high computational cost and resource bottlenecks of deep learning models in real-time settings by proposing a combined s…

A Gaussian process approach for rapid evaluation of skin tension
Matt Nagle, Hannah Conroy Broderick, Christelle Vedel, Michel Destrade, Michael Fop, Aisling Ni Annaidh
https://arxiv.org/abs/2506.05118

A Gaussian process approach for rapid evaluation of skin tension
Skin tension plays a pivotal role in clinical settings, it affects scarring, wound healing and skin necrosis. Despite its importance, there is no widely accepted method for assessing in vivo skin tension or its natural pre-stretch. This study aims to utilise modern machine learning (ML) methods to develop a model that uses non-invasive measurements of surface wave speed to predict clinically useful skin properties such as stress and natural pre-stretch. A large dataset consisting of simulated w…

Determination of Effect of the Movement of a Finite, Dip-slip Fault in Viscoelastic Half-space of Fractional Burger Rheology
Pabita Mahato (Mondal), Seema Sarkar (Mondal)
https://arxiv.org/abs/2506.03977

Determination of Effect of the Movement of a Finite, Dip-slip Fault in Viscoelastic Half-space of Fractional Burger Rheology
The seismically active regions often correlate with fault lines, and the movement of these faults plays a crucial role in defining how stress is stored or released in these areas. To investigate the deformation and accumulation/release of stress and strain in seismically active regions during the aseismic period, a mathematical model has been developed by considering a finite, creeping dip-slip fault inclined in the viscoelastic half-space of a fractional Burger rheology. Laplace transformation…

Model Reduction of Homogeneous Polynomial Dynamical Systems via Tensor Decomposition
Xin Mao, Can Chen
https://arxiv.org/abs/2506.19165 https://

Model Reduction of Homogeneous Polynomial Dynamical Systems via Tensor Decomposition
Model reduction plays a critical role in system control, with established methods such as balanced truncation widely used for linear systems. However, extending these methods to nonlinear settings, particularly polynomial dynamical systems that are often used to model higher-order interactions in physics, biology, and ecology, remains a significant challenge. In this article, we develop a novel model reduction method for homogeneous polynomial dynamical systems (HPDSs) with linear input and out…

Collaborative Editable Model
Kaiwen Tang, Aitong Wu, Yao Lu, Guangda Sun
https://arxiv.org/abs/2506.14146 https://arxiv.org/pdf/2506.…

Collaborative Editable Model
Vertical-domain large language models (LLMs) play a crucial role in specialized scenarios such as finance, healthcare, and law; however, their training often relies on large-scale annotated data and substantial computational resources, impeding rapid development and continuous iteration. To address these challenges, we introduce the Collaborative Editable Model (CoEM), which constructs a candidate knowledge pool from user-contributed domain snippets, leverages interactive user-model dialogues c…

This https://arxiv.org/abs/2502.08425 has been replaced.
initial toot: https://mastoxiv.page/@a…

Interlayer interactions in $\text{La}_3\text{Ni}_2\text{O}_7$ under pressure: from $s^{\pm}$ to $d_{xy}$-wave superconductivity
We investigate the role of \emph{interlayer} interaction terms in the competition between different superconducting gap symmetries in the bilayer nickelate $\text{La}_3\text{Ni}_2\text{O}_7$ under high pressure. We study a two-layer, two-orbital electron model that encompasses both intra- and interlayer Coulomb interaction terms within the matrix random-phase approximation. We find that interlayer interactions favor a $d_{xy}$-wave superconducting pairing symmetry over the $s^{\pm}$-wave symmet…

A simulation model investigation of neutron-oxygen inelastic scattering and subsequent nucleus deexcitation based on experimental data
Y. Hino, Y. Ashida, T. Tano, Y. Koshio
https://arxiv.org/abs/2506.14388

A simulation model investigation of neutron-oxygen inelastic scattering and subsequent nucleus deexcitation based on experimental data
The hadron interaction model for light nuclei plays an important role in understanding the event topology of neutrino-nucleus interaction in a large-scale neutrino detector. In the Super-Kamiokande experiment, the neutral current quasi-elastic interaction induced by atmospheric neutrinos introduces 68-82% uncertainty in the search for diffuse supernova neutrino background. This uncertainty stems from the mismodeling of the interaction between oxygen and fast neutrons generated by the neutral cu…

This https://arxiv.org/abs/2503.09117 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

GRU: Mitigating the Trade-off between Unlearning and Retention for Large Language Models
Large language model (LLM) unlearning has demonstrated its essential role in removing privacy and copyright-related responses, crucial for their legal and safe applications. However, the pursuit of complete unlearning often comes with substantial costs due to its compromises in their general functionality, leading to a notorious trade-off between unlearning and retention. In examining the update process for unlearning dynamically, we find gradients hold essential information for revealing this …

A Standing-Wave Model for the Low-Frequency Dynamics of a Turbulent Separation Bubble
Lukas M. Fuchs, Ben Steinfurth, Jakob G. R. von Saldern, Julien Weiss, Kilian Oberleithner
https://arxiv.org/abs/2505.24723

A Standing-Wave Model for the Low-Frequency Dynamics of a Turbulent Separation Bubble
This study investigates the low-frequency dynamics of a turbulent separation bubble (TSB) forming over a backward-facing ramp, with a focus on very-large-scale coherent structures associated with the so-called 'breathing motion'. Using time-resolved particle image velocimetry (PIV) in both streamwise and spanwise planes, we examine the role of sidewall confinement, an aspect largely overlooked in previous research. Spectral proper orthogonal decomposition (SPOD) of the streamwise velocity field…

This https://arxiv.org/abs/2307.14984 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

S$^3$: Social-network Simulation System with Large Language Model-Empowered Agents
Social network simulation plays a crucial role in addressing various challenges within social science. It offers extensive applications such as state prediction, phenomena explanation, and policy-making support, among others. In this work, we harness the formidable human-like capabilities exhibited by large language models (LLMs) in sensing, reasoning, and behaving, and utilize these qualities to construct the S$^3$ system (short for $\textbf{S}$ocial network $\textbf{S}$imulation $\textbf{S}$y…

System Prompt Extraction Attacks and Defenses in Large Language Models
Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu
https://arxiv.org/abs/2505.23817 https…

System Prompt Extraction Attacks and Defenses in Large Language Models
The system prompt in Large Language Models (LLMs) plays a pivotal role in guiding model behavior and response generation. Often containing private configuration details, user roles, and operational instructions, the system prompt has become an emerging attack target. Recent studies have shown that LLM system prompts are highly susceptible to extraction attacks through meticulously designed queries, raising significant privacy and security concerns. Despite the growing threat, there is a lack of…

Mott metal-insulator transition in a modified periodic Anderson model: Insights from entanglement entropy and role of short-range spatial correlations
Ankur Majumder, Sudeshna Sen
https://arxiv.org/abs/2506.16533

Mott metal-insulator transition in a modified periodic Anderson model: Insights from entanglement entropy and role of short-range spatial correlations
The Mott-Hubbard metal-insulator transition is a paradigmatic phenomenon where Coulomb interactions between electrons drive a metal-insulator phase transition. It has been extensively studied within the Hubbard model, where a quantum critical transition occurs at a finite temperature second-order critical point. This work investigates the Mott metal-insulator transition in a modified periodic Anderson model that may be viewed as a three-orbital lattice model including an interacting, localized …

Constraining parameters of spinor field dark energy: An alternative to $\Lambda$CDM model under the spherically symmetric FLRW space-time
Bijan Saha, Mahendra Goray
https://arxiv.org/abs/2506.16323

Constraining parameters of spinor field dark energy: An alternative to $Λ$CDM model under the spherically symmetric FLRW space-time
This study constrains the cosmological parameters within the scope of a spherically symmetric FLRW cosmological model, the role of a nonlinear spinor field in the universe's evolution. To test this approach, we incorporate the recent Cosmic chronometers, Supernova, and Sloan Digital Sky Survey data. It is found that if spherical coordinates give the FLRW model, the energy-momentum tensor (EMT) of the spinor field possesses nontrivial non-diagonal components. These non-diagonal components of EMT…

Bridging Equilibrium and Kinetics Prediction with a Data-Weighted Neural Network Model of Methane Steam Reforming
Zofia Pizo\'n, Shinji Kimijima, Grzegorz Brus
https://arxiv.org/abs/2506.17224

Bridging Equilibrium and Kinetics Prediction with a Data-Weighted Neural Network Model of Methane Steam Reforming
Hydrogen's role is growing as an energy carrier, increasing the need for efficient production, with methane steam reforming being the most widely used technique. This process is crucial for applications like fuel cells, where hydrogen is converted into electricity, pushing for reactor miniaturization and optimized process control through numerical simulations. Existing models typically address either kinetic or equilibrium regimes, limiting their applicability. Here we show a surrogate model ca…

Susceptibility for extremely low external fluctuations and critical behaviour of Greenberg-Hastings neuronal model
Joaquin Almeira, Daniel A. Martin, Dante R. Chialvo, Sergio A. Cannas
https://arxiv.org/abs/2506.22629

Susceptibility for extremely low external fluctuations and critical behaviour of Greenberg-Hastings neuronal model
We consider the scaling behaviour of the fluctuation susceptibility associated with the average activation in the Greenberg-Hastings neural network model and its relation to microscopic spontaneous activation. We found that, as the spontaneous activation probability tends to zero, a clear finite size scaling behaviour in the susceptibility emerges, characterized by critical exponents which follow already known scaling laws. This shows that the spontaneous activation probability plays the role o…

Spatiotemporal hierarchy of thermal avalanches
Vladimir Yu. Rudyak, Dor Shohat, Yoav Lahini
https://arxiv.org/abs/2507.02573 https://…

Spatiotemporal hierarchy of thermal avalanches
In amorphous solids, frustrated elastic interactions conspire with thermal noise to trigger anomalously slow sequences of plastic rearrangements, termed "thermal avalanches", which play an important role during creep and glassy relaxations. Here we uncover the complex spatiotemporal structure of thermal avalanches in simulations of a model amorphous solid. We systematically disentangle mechanical and thermal triggering during logarithmic creep, revealing a hierarchy of fast localized cascades l…

Data Efficacy for Language Model Training
Yalun Dai, Yangyu Huang, Xin Zhang, Wenshan Wu, Chong Li, Wenhui Lu, Shijie Cao, Li Dong, Scarlett Li
https://arxiv.org/abs/2506.21545

Data Efficacy for Language Model Training
Data is fundamental to the training of language models (LM). Recent research has been dedicated to data efficiency, which aims to maximize performance by selecting a minimal or optimal subset of training data. Techniques such as data filtering, sampling, and selection play a crucial role in this area. To complement it, we define Data Efficacy, which focuses on maximizing performance by optimizing the organization of training data and remains relatively underexplored. This work introduces a gene…

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…

Slip-flow theory for thermo-osmosis based on a kinetic model with near-wall potential
Tetsuro Tsuji, Koichiro Takita, Satoshi Taguchi
https://arxiv.org/abs/2506.20229

Slip-flow theory for thermo-osmosis based on a kinetic model with near-wall potential
In this paper, thermal-slip coefficients in slip boundary conditions of the Stokes equation are derived using the generalized slip-flow theory, with special interest in the role of near-wall potential in micro- and nanoscale flows. As the model of fluids and fluid-solid interaction, we employ the model Boltzmann equation for dilute gases and the diffuse-reflection boundaries with near-wall potential, respectively. It is found that, when the mean free path of gas molecules and the effective rang…

Hints of noncold dark matter? Observational constraints on barotropic dark matter with a constant equation of state parameter
Yan-Hong Yao, Jian-Qi Liu
https://arxiv.org/abs/2507.00478

Hints of noncold dark matter? Observational constraints on barotropic dark matter with a constant equation of state parameter
This study investigates the potential of a cosmological model termed $Λw$DM, in which a cosmological constant play the role of dark energy and dark matter is barotropic and has a constant equation of state parameter ($w_{\rm dm}$), to address the $S_8$ tension between early- and late- universe observations. By incorporating the latest cosmological datasets -- including Planck Cosmic Microwave Background (CMB), Baryon Acoustic Oscillation (BAO), Ia supernovae (SNe Ia), Redshift Space Distortion…

This https://arxiv.org/abs/2412.07901 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Homophily Within and Across Groups
Homophily-the tendency to connect with similar others-plays a central role in shaping network structure and function. It is often treated as a uniform, global parameter, independent from other structural features. Here, we propose a maximum-entropy framework that decomposes homophily into contributions within and across groups, with the stochastic block model emerging as a special case. Our exponential-family formulation, parameterized by group size, fits real-world social networks well and all…

This https://arxiv.org/abs/2404.14500 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

The SAGA Survey. V. Modeling Satellite Systems around Milky Way-mass Galaxies with Updated UniverseMachine
Environment plays a critical role in shaping the assembly of low-mass galaxies. Here, we use the UniverseMachine (UM) galaxy-halo connection framework and the Data Release 3 of the Satellites Around Galactic Analogs (SAGA) Survey to place dwarf galaxy star formation and quenching into a cosmological context. UM is a data-driven forward model that flexibly parameterizes galaxy star formation rates (SFR) using only halo mass and assembly history. We add a new quenching model to UM, tailored for g…

Pok\'eAI: A Goal-Generating, Battle-Optimizing Multi-agent System for Pokemon Red
Zihao Liu, Xinhang Sui, Yueran Song, Siwen Wang
https://arxiv.org/abs/2506.23689

PokéAI: A Goal-Generating, Battle-Optimizing Multi-agent System for Pokemon Red
We introduce PokéAI, the first text-based, multi-agent large language model (LLM) framework designed to autonomously play and progress through Pokémon Red. Our system consists of three specialized agents-Planning, Execution, and Critique-each with its own memory bank, role, and skill set. The Planning Agent functions as the central brain, generating tasks to progress through the game. These tasks are then delegated to the Execution Agent, which carries them out within the game environment. Up…

Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis
Pengyu Han, Zeyi Liu, Shijin Chen, Dongliang Zou, Xiao He
https://arxiv.org/abs/2506.17740

Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis
Multi-condition fault diagnosis is prevalent in industrial systems and presents substantial challenges for conventional diagnostic approaches. The discrepancy in data distributions across different operating conditions degrades model performance when a model trained under one condition is applied to others. With the recent advancements in deep learning, transfer learning has been introduced to the fault diagnosis field as a paradigm for addressing multi-condition fault diagnosis. Among these me…

Validity of generalized Gibbs ensemble in a random matrix model with a global $\mathbb{Z}_2$-symmetry
Adway Kumar Das
https://arxiv.org/abs/2506.16176 http…

Validity of generalized Gibbs ensemble in a random matrix model with a global $\mathbb{Z}_2$-symmetry
$\mathbb{Z}_2$ symmetry is ubiquitous in quantum mechanical systems where it drives various phase transitions and emergent physics. To understand the role of $\mathbb{Z}_2$ symmetry in the thermalization of a local observable in a disordered system, we consider random symmetric centrosymmetric (SC) matrices where the exchange symmetry is conserved. Such a conservation law splits the Hilbert space into decoupled subspaces such that the energy spectrum of a SC matrix is a superposition of two pur…

Attention Is Not Always the Answer: Optimizing Voice Activity Detection with Simple Feature Fusion
Kumud Tripathi, Chowdam Venkata Kumar, Pankaj Wasnik
https://arxiv.org/abs/2506.01365

Attention Is Not Always the Answer: Optimizing Voice Activity Detection with Simple Feature Fusion
Voice Activity Detection (VAD) plays a key role in speech processing, often utilizing hand-crafted or neural features. This study examines the effectiveness of Mel-Frequency Cepstral Coefficients (MFCCs) and pre-trained model (PTM) features, including wav2vec 2.0, HuBERT, WavLM, UniSpeech, MMS, and Whisper. We propose FusionVAD, a unified framework that combines both feature types using three fusion strategies: concatenation, addition, and cross-attention (CA). Experimental results reveal that …

This https://arxiv.org/abs/2404.04146 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Nonlocally coupled moisture model for convective self-aggregation
Clouds play a central role in climate physics by interacting with precipitation, radiation, and circulation. Despite being a fundamental issue in convective organization, the self-aggregation of clouds lacks a theoretical explanation due to its complexity. In this study, we introduce an idealized mathematical model where the system's state is represented solely by the vertically integrated water vapor content of atmospheric columns under the weak temperature gradient approximation. By analyzing…

On the existence of consistent adversarial attacks in high-dimensional linear classification
Matteo Vilucchio, Lenka Zdeborov\'a, Bruno Loureiro
https://arxiv.org/abs/2506.12454

On the existence of consistent adversarial attacks in high-dimensional linear classification
What fundamentally distinguishes an adversarial attack from a misclassification due to limited model expressivity or finite data? In this work, we investigate this question in the setting of high-dimensional binary classification, where statistical effects due to limited data availability play a central role. We introduce a new error metric that precisely capture this distinction, quantifying model vulnerability to consistent adversarial attacks -- perturbations that preserve the ground-truth l…

Testing Hypotheses regarding Covariance and Correlation matrices with the R package CovCorTest
Paavo Sattler, Svenja Jedhoff
https://arxiv.org/abs/2507.03406

Testing Hypotheses regarding Covariance and Correlation matrices with the R package CovCorTest
In addition to the commonly analyzed measures of location, dispersion measurements such as variance and correlation provide many valuable information. Consequently, they play a crucial role in multivariate statistics, which leads to tests regarding covariance and correlation matrices. Furthermore, also the structure of these matrices leads to important hypotheses of interest, since it contains substantial information about the underlying model. In fact, assumptions regarding the structures of c…

Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks
Masudul Hasan Masud Bhuiyan, Gianluca De Stefano, Giancarlo Pellegrino, Cristian-Alexandru Staicu
https://arxiv.org/abs/2506.18191

Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks
Static analysis plays a key role in finding bugs, including security issues. A critical step in static analysis is building accurate call graphs that model function calls in a program. However, due to hard-to-analyze language features, existing call graph construction algorithms for JavaScript are neither sound nor complete. Prior work shows that even advanced solutions produce false edges and miss valid ones. In this work, we assist these tools by identifying missed call edges. Our main idea i…

U-R-VEDA: Integrating UNET, Residual Links, Edge and Dual Attention, and Vision Transformer for Accurate Semantic Segmentation of CMRs
Racheal Mukisa, Arvind K. Bansal
https://arxiv.org/abs/2506.20689

U-R-VEDA: Integrating UNET, Residual Links, Edge and Dual Attention, and Vision Transformer for Accurate Semantic Segmentation of CMRs
Artificial intelligence, including deep learning models, will play a transformative role in automated medical image analysis for the diagnosis of cardiac disorders and their management. Automated accurate delineation of cardiac images is the first necessary initial step for the quantification and automated diagnosis of cardiac disorders. In this paper, we propose a deep learning based enhanced UNet model, U-R-Veda, which integrates convolution transformations, vision transformer, residual links…

Role of bubble positioning in force induced melting of DNA
Bidisha Mukherjee, Amit Raj Singh, Garima Mishra
https://arxiv.org/abs/2506.18821 https://

Role of bubble positioning in force induced melting of DNA
We investigate the role of bubble positioning in the force-induced melting of double-stranded DNA using two distinct approaches: Brownian Dynamics simulations and the Gaussian Network Model. We isolate the effect of bubble positioning by using DNA molecules with 50% AT - 50% GC base-pair composition which ensures constant enthalpy. Our results reveal that it is not just the sequence itself, but its specific arrangement that influences DNA stability. We examine two types of DNA sequences contain…

The dominant role of jetting in micron- and sub-micron sea spray produced by bubble bursting: a revised model and comparison with measurements
Alfonso M. Ganan-Calvo, Miguel A. Herrada, Jose M. Lopez-Herrera
https://arxiv.org/abs/2506.17836

The dominant role of jetting in micron- and sub-micron sea spray produced by bubble bursting: a revised model and comparison with measurements
The primary physical mechanism governing the production of sub-micron sea spray aerosols (SSA) -- specifically the competition between film and jet droplets from bubble bursting -- has remained a subject of intense debate. This work presents a revised, first-principles model that establishes the overwhelming dominance of jetting for producing micron and sub-micron aerosols. Our approach first rules out the film droplet mechanism as a primary contributor for this size range by demonstrating thro…

What is the best shape of a city
Tobias Batik, Guillermo Prieto-Viertel, Jiaqi Liang, Liuhuaying Yang, D\'aniel Kondor, Rafael Prieto-Curiel
https://arxiv.org/abs/2507.00100

What is the best shape of a city
Urban form plays a crucial role in shaping transportation patterns, accessibility, energy consumption, and more. Our study examines the relationship between urban form and transportation energy use by developing a parametric model that simulates city structures and their impact on travel distances. We explore various urban morphologies, including sprawling, elongated, compact, and vertically concentrated cities, and consider five urban profiles: "needle," "pyramid," "pancake," "bowl," and "ring…

Relativistic excitation of compact stars
Zhiqiang Miao, Xuefeng Feng, Zhen Pan, Huan Yang
https://arxiv.org/abs/2506.23176 https://ar…

Relativistic excitation of compact stars
In this work, we study the excitation of a compact star under the influence of external gravitational driving in the relativistic regime. Using a model setup in which a wave with constant frequency is injected from past null infinity and scattered by the star to future null infinity, we show that the scattering coefficient encodes rich information of the star. For example, the analytical structure of the scattering coefficient implies that the decay rate of a mode generally plays the role of ``…

A Factorized Mass Structure of Fermions and Its Fit
Qingfeng Cao, Ying Zhang
https://arxiv.org/abs/2507.01480 https://arxiv.org/pdf/2…

A Factorized Mass Structure of Fermions and Its Fit
The structure of the mass matrix, a challenging problem in the Standard Model, is closely related to flavor phenomenology and the understanding of the Yukawa interaction. We derive a factorized mass structure based on observed fermion mass hierarchies, investigating the role of $SO(2)^f$ family symmetry in explaining the approximate degeneracy of light quark generations and its connection to flavor mixing. Our calculation includes the slightest modification from the lightest fermions by $\mathc…

Hatevolution: What Static Benchmarks Don't Tell Us
Chiara Di Bonaventura, Barbara McGillivray, Yulan He, Albert Mero\~no-Pe\~nuela
https://arxiv.org/abs/2506.12148

Hatevolution: What Static Benchmarks Don't Tell Us
Language changes over time, including in the hate speech domain, which evolves quickly following social dynamics and cultural shifts. While NLP research has investigated the impact of language evolution on model training and has proposed several solutions for it, its impact on model benchmarking remains under-explored. Yet, hate speech benchmarks play a crucial role to ensure model safety. In this paper, we empirically evaluate the robustness of 20 language models across two evolving hate speec…

Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription
Anna Hamberger, Sebastian Murgul, Jochen Schmidt, Michael Heizmann
https://arxiv.org/abs/2506.14223

Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription
Music transcription plays a pivotal role in Music Information Retrieval (MIR), particularly for stringed instruments like the guitar, where symbolic music notations such as MIDI lack crucial playability information. This contribution introduces the Fretting-Transformer, an encoderdecoder model that utilizes a T5 transformer architecture to automate the transcription of MIDI sequences into guitar tablature. By framing the task as a symbolic translation problem, the model addresses key challenges…

A Deep Learning Algorithm Based on CNN-LSTM Framework for Predicting Cancer Drug Sales Volume
Yinghan Li, Yilin Yao, Junghua Lin, Nanxi Wang
https://arxiv.org/abs/2506.21927

A Deep Learning Algorithm Based on CNN-LSTM Framework for Predicting Cancer Drug Sales Volume
This study explores the application potential of a deep learning model based on the CNN-LSTM framework in forecasting the sales volume of cancer drugs, with a focus on modeling complex time series data. As advancements in medical technology and cancer treatment continue, the demand for oncology medications is steadily increasing. Accurate forecasting of cancer drug sales plays a critical role in optimizing production planning, supply chain management, and healthcare policy formulation. The data…

Extrinsic Orbital Hall Effect and Orbital Relaxation in Mesoscopic Devices
Anderson L. R. Barbosa, Hyun-Woo Lee, Tatiana G. Rappoport
https://arxiv.org/abs/2507.01941

Extrinsic Orbital Hall Effect and Orbital Relaxation in Mesoscopic Devices
Despite recent advances in orbitronics, the influence of disorder on the orbital Hall effect and orbital relaxation mechanisms remains poorly understood. In this work, we numerically investigate the role of disorder in orbital transport within mesoscopic devices using a real-space tight-binding model on a two-dimensional square lattice that hosts atomic orbitals capable of carrying atomic orbital angular momentum. By considering devices with varying geometries--square and rectangular--and syste…

Interaction-induced magnetoconductivity, magnetoresistivity, and the Hall effect in massive-massless fermion mixtures
Yuping Huang, D. S. Eliseev, V. M. Kovalev, O. V. Kibis, Yu. Yu. Illarionov, I. G. Savenko
https://arxiv.org/abs/2507.01435

Interaction-induced magnetoconductivity, magnetoresistivity, and the Hall effect in massive-massless fermion mixtures
The presence of two types of holes, namely the Dirac holes and the heavy holes, in a two-dimensional sample exposed to a weak external permanent magnetic field leads to the emergence of the temperature and magnetic field-dependent contribution to the resistivity due to their interactions. Taking a HgTe-based two-dimensional semimetal as a testbed, we develop a theoretical model describing the role of interactions between the degenerate massive and Dirac particles for the resistivity in the pres…

Decoding Noise in Nanofluidic Systems: Adsorption versus Diffusion Signatures in Power Spectra
Anna Drummond Young, Alice L. Thorneywork, Sophie Marbach
https://arxiv.org/abs/2506.23867

Decoding Noise in Nanofluidic Systems: Adsorption versus Diffusion Signatures in Power Spectra
Adsorption processes play a fundamental role in molecular transport through nanofluidic systems, but their signatures in measured signals are often hard to distinguish from other processes like diffusion. In this paper, we derive an expression for the power spectral density (PSD) of particle number fluctuations in a channel, accounting for diffusion and adsorption/desorption to a wall. Our model, validated by Brownian dynamics simulations, is set in a minimal yet adaptable geometry, allowing us…

When Does Meaning Backfire? Investigating the Role of AMRs in NLI
Junghyun Min, Xiulin Yang, Shira Wein
https://arxiv.org/abs/2506.14613 https://

When Does Meaning Backfire? Investigating the Role of AMRs in NLI
Natural Language Inference (NLI) relies heavily on adequately parsing the semantic content of the premise and hypothesis. In this work, we investigate whether adding semantic information in the form of an Abstract Meaning Representation (AMR) helps pretrained language models better generalize in NLI. Our experiments integrating AMR into NLI in both fine-tuning and prompting settings show that the presence of AMR in fine-tuning hinders model generalization while prompting with AMR leads to sligh…

This https://arxiv.org/abs/2501.16162 has been replaced.
initial toot: https://mastoxiv.page/@a…

Quantum Transport with Spin Orbit Coupling: New Developments in TranSIESTA
We present the implementation of spinor quantum transport within the non-equilibrium Green's function (NEGF) code TranSIESTA based on Density Functional Theory (DFT). First-principles methods play an essential role in molecular and material modelling, and the DFT+NEGF approach has become a widely-used tool for quantum transport simulation. Exisiting (open source) DFT-based quantum transport codes either model non-equilibrium/finite-bias cases in an approximate way or rely on the collinear spin …