Tootfinder

USAD: An Unsupervised Data Augmentation Spatio-Temporal Attention Diffusion Network
Ying Yu, Hang Xiao, Siyao Li, Jiarui Li, Haotian Tang, Hanyu Liu, Chao Li
https://arxiv.org/abs/2507.02827

USAD: An Unsupervised Data Augmentation Spatio-Temporal Attention Diffusion Network
The primary objective of human activity recognition (HAR) is to infer ongoing human actions from sensor data, a task that finds broad applications in health monitoring, safety protection, and sports analysis. Despite proliferating research, HAR still faces key challenges, including the scarcity of labeled samples for rare activities, insufficient extraction of high-level features, and suboptimal model performance on lightweight devices. To address these issues, this paper proposes a comprehensi…

cVLA: Towards Efficient Camera-Space VLAs
Max Argus, Jelena Bratulic, Houman Masnavi, Maxim Velikanov, Nick Heppert, Abhinav Valada, Thomas Brox
https://arxiv.org/abs/2507.02190

cVLA: Towards Efficient Camera-Space VLAs
Vision-Language-Action (VLA) models offer a compelling framework for tackling complex robotic manipulation tasks, but they are often expensive to train. In this paper, we propose a novel VLA approach that leverages the competitive performance of Vision Language Models (VLMs) on 2D images to directly infer robot end-effector poses in image frame coordinates. Unlike prior VLA models that output low-level controls, our model predicts trajectory waypoints, making it both more efficient to train and…

This https://arxiv.org/abs/2409.04108 has been replaced.
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An Extension of the Adversarial Threat Model in Quantitative Information Flow
In this paper, we propose an extended framework for quantitative information flow (QIF), aligned with the previously proposed core-concave generalization of entropy measures, to include adversaries that use Kolmogorov-Nagumo $f$-mean to infer secrets in a private system. Specifically, in our setting, an adversary uses Kolmogorov-Nagumo $f$-mean to compute its best actions before and after observing the system's randomized outputs. This leads to generalized notions of prior and posterior vulnera…

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition
Stefano Bae, Dario Bocchi, Luca Maria Del Bono, Luca Leuzzi
https://arxiv.org/abs/2507.02574

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition
Inverse problems arise in situations where data is available, but the underlying model is not. It can therefore be necessary to infer the parameters of the latter starting from the former. Statistical mechanics offers a toolbox of techniques to address this challenge. In this work, we illustrate three of the main methods: the Maximum Likelihood, Maximum Pseudo-Likelihood, and Mean-Field approaches. We begin with a thorough theoretical introduction to these methods, followed by their application…

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT
Wenqi Zeng, Lu Zhang, Yuan Yao
https://arxiv.org/abs/2507.02734 h…

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT
Long-term biomolecular dynamics are critical for understanding key evolutionary transformations in molecular systems. However, capturing these processes requires extended simulation timescales that often exceed the practical limits of conventional models. To address this, shorter simulations, initialized with diverse perturbations, are commonly used to sample phase space and explore a wide range of behaviors. Recent advances have leveraged language models to infer long-term behavior from short …

This https://arxiv.org/abs/2410.17341 has been replaced.
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A 3D Model of the Local Bubble's Magnetic Field: Insights from Dust and Starlight Polarization
Clustered stellar feedback creates expanding voids in the magnetized interstellar medium known as superbubbles. Although theory suggests that superbubble expansion is influenced by interstellar magnetic fields, direct observational data on 3D superbubble magnetic field geometry is limited. The Sun's location inside the Local Bubble provides a unique opportunity to infer a superbubble's 3D magnetic field orientation, under the assumptions that: $\mathrm{I}$) the Local Bubble's surface is the pri…

Universal Bound on the Eigenvalues of 2-Positive Trace-Preserving Maps
Frederik vom Ende, Dariusz Chru\'sci\'nski, Gen Kimura, Paolo Muratore-Ginanneschi
https://arxiv.org/abs/2506.02145

Universal Bound on the Eigenvalues of 2-Positive Trace-Preserving Maps
We prove an upper bound on the trace of any 2-positive, trace-preserving map in terms of its smallest eigenvalue. We show that this spectral bound is tight, and that 2-positivity is necessary for this inequality to hold in general. Moreover, we use this to infer a similar bound for generators of one-parameter semigroups of 2-positive trace-preserving maps. With this approach we generalize known results for completely positive trace-preserving dynamics while providing a significantly simpler pro…

This https://arxiv.org/abs/2505.11197 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Modeling Cell Dynamics and Interactions with Unbalanced Mean Field Schrödinger Bridge
Modeling the dynamics from sparsely time-resolved snapshot data is crucial for understanding complex cellular processes and behavior. Existing methods leverage optimal transport, Schrödinger bridge theory, or their variants to simultaneously infer stochastic, unbalanced dynamics from snapshot data. However, these approaches remain limited in their ability to account for cell-cell interactions. This integration is essential in real-world scenarios since intercellular communications are fundamen…

This https://arxiv.org/abs/2505.21863 has been replaced.
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GETReason: Enhancing Image Context Extraction through Hierarchical Multi-Agent Reasoning
Publicly significant images from events hold valuable contextual information, crucial for journalism and education. However, existing methods often struggle to extract this relevance accurately. To address this, we introduce GETReason (Geospatial Event Temporal Reasoning), a framework that moves beyond surface-level image descriptions to infer deeper contextual meaning. We propose that extracting global event, temporal, and geospatial information enhances understanding of an image's significanc…

This https://arxiv.org/abs/2505.16978 has been replaced.
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HyGenar: An LLM-Driven Hybrid Genetic Algorithm for Few-Shot Grammar Generation
Grammar plays a critical role in natural language processing and text/code generation by enabling the definition of syntax, the creation of parsers, and guiding structured outputs. Although large language models (LLMs) demonstrate impressive capabilities across domains, their ability to infer and generate grammars has not yet been thoroughly explored. In this paper, we aim to study and improve the ability of LLMs for few-shot grammar generation, where grammars are inferred from sets of a small …

Enhancing Speech Instruction Understanding and Disambiguation in Robotics via Speech Prosody
David Sasu, Kweku Andoh Yamoah, Benedict Quartey, Natalie Schluter
https://arxiv.org/abs/2506.02057

Enhancing Speech Instruction Understanding and Disambiguation in Robotics via Speech Prosody
Enabling robots to accurately interpret and execute spoken language instructions is essential for effective human-robot collaboration. Traditional methods rely on speech recognition to transcribe speech into text, often discarding crucial prosodic cues needed for disambiguating intent. We propose a novel approach that directly leverages speech prosody to infer and resolve instruction intent. Predicted intents are integrated into large language models via in-context learning to disambiguate and …

When Bi-interpretability implies Synonymy
Harvey M. Friedman, Albert Visser
https://arxiv.org/abs/2506.01028 https://arxiv.org/pdf/25…

When Bi-interpretability implies Synonymy
Two salient notions of sameness of theories are synonymy, also known as definitional equivalence, and bi-interpretability. Of these two definitional equivalence is the strictest notion. In which cases can we infer synonymy from bi-interpretability? We study this question for the case of sequential theories. Our result is as follows. Suppose that two sequential theories are bi-interpretable and that the interpretations involved in the bi-interpretation are one-dimensional and identity preserving…

Reconstruction of the observable universe from the integrated Sachs-Wolfe effect
Julianne Chung, Yiran Wang
https://arxiv.org/abs/2507.01399 https://

Reconstruction of the observable universe from the integrated Sachs-Wolfe effect
The integrated Sachs-Wolfe (ISW) effect is a property of the Cosmic Microwave Background (CMB), in which photons from the CMB are gravitationally redshifted, causing the anisotropies in the CMB. An intriguing question is whether one can infer the gravitational perturbations from the ISW effect observed near the Earth. In this work, we address the question using a tomographic reconstruction approach, similar to X-ray CT reconstruction in medical imaging. We develop the mathematical analysis for …

Causal Prompting for Implicit Sentiment Analysis with Large Language Models
Jing Ren, Wenhao Zhou, Bowen Li, Mujie Liu, Nguyen Linh Dan Le, Jiade Cen, Liping Chen, Ziqi Xu, Xiwei Xu, Xiaodong Li
https://arxiv.org/abs/2507.00389

Causal Prompting for Implicit Sentiment Analysis with Large Language Models
Implicit Sentiment Analysis (ISA) aims to infer sentiment that is implied rather than explicitly stated, requiring models to perform deeper reasoning over subtle contextual cues. While recent prompting-based methods using Large Language Models (LLMs) have shown promise in ISA, they often rely on majority voting over chain-of-thought (CoT) reasoning paths without evaluating their causal validity, making them susceptible to internal biases and spurious correlations. To address this challenge, we …

Extraction of Physical Parameters of RRab Variables using Neural Network based Interpolator
Nitesh Kumar (Department of Physics, Applied Science Cluster, University of Petroleum and Energy Studies), Harinder P. Singh (Department of Physics and Astrophysics, University of Delhi, Delhi, India), Oleg Malkov (Institute of Astronomy of the Russian Academy of Sciences), Santosh Joshi (Aryabhatta Research Institute of Observational Sciences), Kefeng Tan (National Astronomical Observatories, C…

Extraction of Physical Parameters of RRab Variables using Neural Network based Interpolator
Determining the physical parameters of pulsating variable stars such as RR Lyrae is essential for understanding their internal structure, pulsation mechanisms, and evolutionary state. In this study, we present a machine learning framework that uses feedforward artificial neural networks (ANNs) to infer stellar parameters-mass ($M$), luminosity (log($L/L_\odot$)), effective temperature (log($T_{\rm eff}$)), and metallicity ($Z$)-directly from Transiting Exoplanet Survey Satellite (TESS) light cu…

A theoretical prediction for the dipole in nearby distances using cosmography
Hayley J. Macpherson, Asta Heinesen
https://arxiv.org/abs/2507.01095 https://…

A theoretical prediction for the dipole in nearby distances using cosmography
Cosmography is a widely applied method to infer kinematics of the Universe at small cosmological scales while remaining agnostic about the theory of gravity at play. Usually cosmologists invoke the Friedmann-Lemaitre-Robertson-Walker (FLRW) metric in cosmographic analyses, however generalised approaches allow for analyses outside of any assumed geometrical model. These methods have great promise to be able to model-independently map the cosmic neighborhood where the Universe has not yet converg…

This https://arxiv.org/abs/2311.03795 has been replaced.
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Periodicity of dynamical signatures of chaos in quantum kicked top
A host of dynamical measures of quantum correlations -- out-of-time ordered correlators (OTOC), Loschmidt echo, generalized entanglement and observational entropy -- are useful to infer the underlying classical chaotic dynamics in quantum regime. In this work, these measures are employed to analyse quantum kicked top with kick strength $k$. It is shown that, despite the differences in their definitions, these measures are periodic with $k$, and the periodicity depends on the number of spins rep…

Meteoroid stream identification with HDBSCAN unsupervised clustering algorithm
Eloy Pe\~na-Asensio, Fabio Ferrari
https://arxiv.org/abs/2507.01501 https://…

Meteoroid stream identification with HDBSCAN unsupervised clustering algorithm
Accurate identification of meteoroid streams is central to understanding their origins and evolution. However, overlapping clusters and background noise hinder classification, an issue amplified for missions such as ESA's LUMIO that rely on meteor shower observations to infer lunar meteoroid impact parameters. This study evaluates the performance of the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm for unsupervised meteoroid stream identification, …

This https://arxiv.org/abs/2503.00303 has been replaced.
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Leveraging Complementary AI Explanations to Mitigate Misunderstanding in XAI
Artificial intelligence explanations can make complex predictive models more comprehensible. To be effective, however, they should anticipate and mitigate possible misinterpretations, e.g., arising when users infer incorrect information that is not explicitly conveyed. To this end, we propose complementary explanations -- a novel method that pairs explanations to compensate for their respective limitations. A complementary explanation adds insights that clarify potential misconceptions stemming…

Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning
Qingdong He, Xueqin Chen, Chaoyi Wang, Yanjie Pan, Xiaobin Hu, Zhenye Gan, Yabiao Wang, Chengjie Wang, Xiangtai Li, Jiangning Zhang
https://arxiv.org/abs/2507.01908

Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning
Instruction-based image editing (IIE) has advanced rapidly with the success of diffusion models. However, existing efforts primarily focus on simple and explicit instructions to execute editing operations such as adding, deleting, moving, or swapping objects. They struggle to handle more complex implicit hypothetical instructions that require deeper reasoning to infer plausible visual changes and user intent. Additionally, current datasets provide limited support for training and evaluating rea…

Deep Semiparametric Partial Differential Equation Models
Ziyuan Chen, Shunxing Yan, Fang Yao
https://arxiv.org/abs/2506.22779 https://

Deep Semiparametric Partial Differential Equation Models
In many scientific fields, the generation and evolution of data are governed by partial differential equations (PDEs) which are typically informed by established physical laws at the macroscopic level to describe general and predictable dynamics. However, some complex influences may not be fully captured by these laws at the microscopic level due to limited scientific understanding. This work proposes a unified framework to model, estimate, and infer the mechanisms underlying data dynamics. We …

ProxAnn: Use-Oriented Evaluations of Topic Models and Document Clustering
Alexander Hoyle, Lorena Calvo-Bartolom\'e, Jordan Boyd-Graber, Philip Resnik
https://arxiv.org/abs/2507.00828

ProxAnn: Use-Oriented Evaluations of Topic Models and Document Clustering
Topic model and document-clustering evaluations either use automated metrics that align poorly with human preferences or require expert labels that are intractable to scale. We design a scalable human evaluation protocol and a corresponding automated approximation that reflect practitioners' real-world usage of models. Annotators -- or an LLM-based proxy -- review text items assigned to a topic or cluster, infer a category for the group, then apply that category to other documents. Using this p…

This https://arxiv.org/abs/2505.23576 has been replaced.
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Cognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms
Small Uncrewed Aerial Systems (sUAS) are increasingly deployed as autonomous swarms in search-and-rescue and other disaster-response scenarios. In these settings, they use computer vision (CV) to detect objects of interest and autonomously adapt their missions. However, traditional CV systems often struggle to recognize unfamiliar objects in open-world environments or to infer their relevance for mission planning. To address this, we incorporate large language models (LLMs) to reason about dete…

Satellite and Mobile Phone Data Reveal How Violence Affects Seasonal Migration in Afghanistan
Xiao Hui Tai, Suraj R. Nair, Shikhar Mehra, Joshua E. Blumenstock
https://arxiv.org/abs/2507.00279

Satellite and Mobile Phone Data Reveal How Violence Affects Seasonal Migration in Afghanistan
Seasonal migration plays a critical role in stabilizing rural economies and sustaining the livelihoods of agricultural households. Violence and civil conflict have long been thought to disrupt these labor flows, but this hypothesis has historically been hard to test given the lack of reliable data on migration in conflict zones. Focusing on Afghanistan in the 8-year period prior to the Taliban's takeover in 2021, we first demonstrate how satellite imagery can be used to infer the timing of the …

This https://arxiv.org/abs/2505.00763 has been replaced.
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JFlow: Model-Independent Spherical Jeans Analysis using Equivariant Continuous Normalizing Flows
The kinematics of stars in dwarf spheroidal galaxies have been studied to understand the structure of dark matter halos. However, the kinematic information of these stars is often limited to celestial positions and line-of-sight velocities, making full phase space analysis challenging. Conventional methods rely on projected analytic phase space density models with several parameters and infer dark matter halo structures by solving the spherical Jeans equation. In this paper, we introduce an uns…

This https://arxiv.org/abs/2410.23394 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Auditing for Bias in Ad Delivery Using Inferred Demographic Attributes
Auditing social-media algorithms has become a focus of public-interest research and policymaking to ensure their fairness across demographic groups such as race, age, and gender in consequential domains such as the presentation of employment opportunities. However, such demographic attributes are often unavailable to auditors and platforms. When demographics data is unavailable, auditors commonly infer them from other available information. In this work, we study the effects of inference error …

Dark from light (DfL): Inferring halo properties from luminous tracers with machine learning trained on cosmological simulations. I. Method, proof of concept & preliminary testing
Asa F. L. Bluck, Joanna M. Piotrowska, Paul Goubert, Roberto Maiolino, Camilo Casimiro, Thomas Pinto Franco, Nicolas Cea
https://arxiv.org/abs/250…

Dark from light (DfL): Inferring halo properties from luminous tracers with machine learning trained on cosmological simulations. I. Method, proof of concept & preliminary testing
We present Dark from Light (DfL) - a novel method to infer the dark sector in wide-field galaxy surveys, leveraging a machine learning approach trained on contemporary cosmological simulations. The aim of this algorithm is to provide a fast, straightforward, and accurate route to estimating dark matter halo masses and group membership in wide-field spectroscopic galaxy surveys. This approach requires a highly limited number of input parameters and yields full probability distribution functions …

Attack Smarter: Attention-Driven Fine-Grained Webpage Fingerprinting Attacks
Yali Yuan, Weiyi Zou, Guang Cheng
https://arxiv.org/abs/2506.20082 https://

Attack Smarter: Attention-Driven Fine-Grained Webpage Fingerprinting Attacks
Website Fingerprinting (WF) attacks aim to infer which websites a user is visiting by analyzing traffic patterns, thereby compromising user anonymity. Although this technique has been demonstrated to be effective in controlled experimental environments, it remains largely limited to small-scale scenarios, typically restricted to recognizing website homepages. In practical settings, however, users frequently access multiple subpages in rapid succession, often before previous content fully loads.…

Feel the Force: Contact-Driven Learning from Humans
Ademi Adeniji, Zhuoran Chen, Vincent Liu, Venkatesh Pattabiraman, Raunaq Bhirangi, Siddhant Haldar, Pieter Abbeel, Lerrel Pinto
https://arxiv.org/abs/2506.01944

Feel the Force: Contact-Driven Learning from Humans
Controlling fine-grained forces during manipulation remains a core challenge in robotics. While robot policies learned from robot-collected data or simulation show promise, they struggle to generalize across the diverse range of real-world interactions. Learning directly from humans offers a scalable solution, enabling demonstrators to perform skills in their natural embodiment and in everyday environments. However, visual demonstrations alone lack the information needed to infer precise contac…

I don't recall at what time my train departs, but I do recall what time I set the wake-up alarm for, so I can approximately infer the former. Brains are a crazy thing.

Asymptotically Optimal Secure Aggregation for Wireless Federated Learning with Multiple Servers
Zhenhao Huang, Kai Liang, Yuanming Shi, Songze Li, Youlong Wu
https://arxiv.org/abs/2506.23680

Asymptotically Optimal Secure Aggregation for Wireless Federated Learning with Multiple Servers
In this paper, we investigate the transmission latency of the secure aggregation problem in a \emph{wireless} federated learning system with multiple curious servers. We propose a privacy-preserving coded aggregation scheme where the servers can not infer any information about the distributed users' local gradients, nor the aggregation value. In our scheme, each user encodes its local gradient into $\sK$ confidential messages intended exclusively for different servers using a multi-secret shari…

Network classification through random walks
Gonzalo Travieso, Joao Merenda, Odemir M. Bruno
https://arxiv.org/abs/2505.21706 https://…

Network classification through random walks
Network models have been widely used to study diverse systems and analyze their dynamic behaviors. Given the structural variability of networks, an intriguing question arises: Can we infer the type of system represented by a network based on its structure? This classification problem involves extracting relevant features from the network. Existing literature has proposed various methods that combine structural measurements and dynamical processes for feature extraction. In this study, we introd…

Anchoring Stellar Age Indicators: A Cross-Calibration of [C/N] and Gyrochronology Ages via the Age-Velocity-Dispersion Relation
Yuxi Lu, Marc H. Pinsonneault, Yuan-Sen Ting, Phil R. Van-Lane, John D Roberts, Jamie Tayar, Alexander Stone-Martinez
https://arxiv.org/abs/2506.24010

Anchoring Stellar Age Indicators: A Cross-Calibration of [C/N] and Gyrochronology Ages via the Age-Velocity-Dispersion Relation
Determining stellar ages is challenging, as it depends on other stellar parameters in a non-linear way and often relies on stellar evolution models to infer the underlying relation between these parameters and age. This complexity increases when comparing different age-dating methods, as they rely on distinct indicators and are often applicable to non-overlapping regions of the color-magnitude diagram. Moreover, many empirical calibration methods rely on pre-determined ages, often from open clu…

This https://arxiv.org/abs/2505.13062 has been replaced.
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Hearing from Silence: Reasoning Audio Descriptions from Silent Videos via Vision-Language Model
Humans can intuitively infer sounds from silent videos, but whether multimodal large language models can perform modal-mismatch reasoning without accessing target modalities remains relatively unexplored. Current text-assisted-video-to-audio (VT2A) methods excel in video foley tasks but struggle to acquire audio descriptions during inference. We introduce the task of Reasoning Audio Descriptions from Silent Videos (SVAD) to address this challenge and investigate vision-language models' (VLMs) c…

Posterior Cram\'er-Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM
Benjamin J. B. Deutschmann, Xuhong Li, Florian Meyer, Erik Leitinger
https://arxiv.org/abs/2506.19957

Posterior Cramér-Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM
Radio-frequency simultaneous localization and mapping (RF-SLAM) methods jointly infer the position of mobile transmitters and receivers in wireless networks, together with a geometric map of the propagation environment. An inferred map of specular surfaces can be used to exploit non-line-of-sight components of the multipath channel to increase robustness, bypass obstructions, and improve overall communication and positioning performance. While performance bounds for user location are well estab…

Simulation-based digital twinning of activation and repolarisation sequences from the ECG across healthy and diseased hearts
James A Coleman, Julia Camps, Abdallah I Hasaballa, Alfonso Bueno-Orovio
https://arxiv.org/abs/2506.22243

Simulation-based digital twinning of activation and repolarisation sequences from the ECG across healthy and diseased hearts
Abnormal patterns of ventricular repolarisation contribute to lethal arrhythmias in various cardiac conditions, including inherited and acquired channelopathies, cardiomyopathies, and ischaemic heart disease. However, methods to detect these repolarisation abnormalities are limited. In this study, we introduce and assess a novel simulation-based method to infer ventricular activation and repolarisation times from the 12-lead electrocardiogram (ECG) and magnetic resonance-derived ventricular a…

Infrared observations reveal the reprocessing envelope in the tidal disruption event AT 2019azh
Thomas M. Reynolds, Lars Thomsen, Seppo Mattila, Takashi Nagao, Joseph P. Anderson, Franz E. Bauer, Panos Charalampopoulos, Lixin Dai, Sara Faris, Mariusz Gromadzki, Claudia P. Guti\'errez, Hanin Kuncarayakti, Cosimo Inserra, Erkki Kankare, Timo Kravtsov, Shane Moran, Phil Wiseman

Infrared observations reveal the reprocessing envelope in the tidal disruption event AT 2019azh
Tidal disruption events (TDEs) are expected to release much of their energy in the far-ultraviolet (UV), which we do not observe directly. However, infrared (IR) observations can observe re-radiation of the optical/UV emission from dust, and if this dust is observed in the process of sublimation, we can infer the un-observed UV radiated energy. TDEs have also been predicted to show spectra shallower than a blackbody in the IR, but this has not yet been observed. We present near/mid-IR observati…

Predicting protein folding dynamics using sequence information
Ezequiel A. Galpern, Federico Caama\~no, Diego U. Ferreiro
https://arxiv.org/abs/2505.17237 …

Predicting protein folding dynamics using sequence information
Natural protein sequences somehow encode the structural forms that these molecules adopt. Recent developments in structure-prediction are agnostic to the mechanisms by which proteins fold and represent them as static objects. However, the amino acid sequences also encode information about how the folding process can happen, and how variations in the sequences impact on the populations of the distinct structural forms that proteins acquire. Here we present a method to infer protein folding dynam…

Learning Individual Reproductive Behavior from Aggregate Fertility Rates via Neural Posterior Estimation
Daniel Ciganda, Ignacio Camp\'on, I\~naki Permanyer, Jakob H Macke
https://arxiv.org/abs/2506.22607

Learning Individual Reproductive Behavior from Aggregate Fertility Rates via Neural Posterior Estimation
While age-specific fertility rates (ASFRs) provide the most extensive record of reproductive change, their aggregate nature masks the underlying behavioral mechanisms that ultimately drive fertility trends. To recover these mechanisms, we develop a likelihood-free Bayesian framework that couples an individual-level model of the reproductive process with Sequential Neural Posterior Estimation (SNPE). This allows us to infer eight behavioral and biological parameters from just two aggregate serie…

Models for cyclic infinity operads
Brandon Doherty, Philip Hackney
https://arxiv.org/abs/2506.15622 https://arxiv.org/pdf/2506.15622

Models for cyclic infinity operads
We construct model structures on cyclic dendroidal sets and cyclic dendroidal spaces for cyclic quasi-operads and complete cyclic dendroidal Segal spaces, respectively. We show these models are Quillen equivalent to the model structure for simplicial cyclic operads. This answers in the affirmative a question of the second author and Drummond-Cole concerning model structures for cyclic $\infty$-operads. We infer similar statements for planar cyclic $\infty$-operads, providing the model-categoric…

Influence and information in a collective of self-propelled particles
Jiahuan Pang, Wendong Wang
https://arxiv.org/abs/2506.20888 https://

Influence and information in a collective of self-propelled particles
While information-theoretic quantities, such as transfer entropy, have been widely adopted to infer causal relationships in collective systems, a critical gap exists: the absence of quantitative evidence directly linking information-theoretic quantities to a physically defined influence. This letter addresses this gap by proposing a modified Vicsek model that enables the calculation of a physically interpretable influence grounded in the angular interactions between particles. Averaged pairwise…

Network Structures as an Attack Surface: Topology-Based Privacy Leakage in Federated Learning
Murtaza Rangwala, Richard O. Sinnott, Rajkumar Buyya
https://arxiv.org/abs/2506.19260

Network Structures as an Attack Surface: Topology-Based Privacy Leakage in Federated Learning
Federated learning systems increasingly rely on diverse network topologies to address scalability and organizational constraints. While existing privacy research focuses on gradient-based attacks, the privacy implications of network topology knowledge remain critically understudied. We conduct the first comprehensive analysis of topology-based privacy leakage across realistic adversarial knowledge scenarios, demonstrating that adversaries with varying degrees of structural knowledge can infer s…

GSDNet: Revisiting Incomplete Multimodal-Diffusion from Graph Spectrum Perspective for Conversation Emotion Recognition
Yuntao Shou, Jun Yao, Tao Meng, Wei Ai, Cen Chen, Keqin Li
https://arxiv.org/abs/2506.12325

GSDNet: Revisiting Incomplete Multimodal-Diffusion from Graph Spectrum Perspective for Conversation Emotion Recognition
Multimodal emotion recognition in conversations (MERC) aims to infer the speaker's emotional state by analyzing utterance information from multiple sources (i.e., video, audio, and text). Compared with unimodality, a more robust utterance representation can be obtained by fusing complementary semantic information from different modalities. However, the modality missing problem severely limits the performance of MERC in practical scenarios. Recent work has achieved impressive performance on moda…

This https://arxiv.org/abs/2505.05420 has been replaced.
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Robustly optimal dynamics for active matter reservoir computing
Information processing abilities of active matter are studied in the reservoir computing (RC) paradigm to infer the future state of a chaotic signal. We uncover an exceptional regime of agent dynamics that has been overlooked previously. It appears robustly optimal for performance under many conditions, thus providing valuable insights into computation with physical systems more generally. The key to forming effective mechanisms for information processing appears in the system's intrinsic relax…

Wide-field Polarization Imaging and Numerical Modeling of the Coma and Tail of Comet C/2023 A3 (Tsuchinshan-ATLAS)
Mirza Arnaut, Christian W\"ohler, Pritish Halder, Goldy Ahuja, Shashikiran Ganesh, Megha Bhatt
https://arxiv.org/abs/2506.21284

Wide-field Polarization Imaging and Numerical Modeling of the Coma and Tail of Comet C/2023 A3 (Tsuchinshan-ATLAS)
Imaging polarimetry is a well-known method for analysing the structure and composition of cometary dust. Accordingly, comets are classified based on the phase angle dependent degree of linear polarization of their comae. The goal of this study is to examine the size and structure of the dust grains in the coma and in particular in the tail of the bright comet C/2023 A3 (Tsuchinshan-ATLAS) and to infer possible variations. For this purpose, we rely on the method of telescopic wide-field polarime…

Using random perturbations to infer the structure of feedback control in gene expression
Seshu Iyengar, Andreas Hilfinger
https://arxiv.org/abs/2505.04912 …

Using random perturbations to infer the structure of feedback control in gene expression
Feedback in cellular processes is typically inferred through cellular responses to experimental perturbations. Modular response analysis provides a theoretical framework for translating specific perturbations into feedback sensitivities between cellular modules. However, in large-scale drug perturbation studies the effect of any given drug may not be known and may not only affect one module at a time. Here, we analyze the response of gene expression models to random perturbations that affect mu…

Modeling and Inferring Metacommunity Dynamics with Maximum Caliber
Zachary Jackson, Mathew A. Leibold, Robert D. Holt, BingKan Xue
https://arxiv.org/abs/2506.17495

Modeling and Inferring Metacommunity Dynamics with Maximum Caliber
A major challenge for community ecology is to use distribution patterns to infer basic parameters of dynamical models without conducting laborious experimental manipulations. We present a novel framework drawn from statistical physics -- Maximum Caliber -- for characterizing the temporal dynamics of complex ecological systems in spatially extended landscapes and inferring parameters from temporal data. As an extension of Maximum Entropy modeling, Maximum Caliber models the probability of possib…

EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Bruno Aristimunha, Dung Truong, Pierre Guetschel, Seyed Yahya Shirazi, Isabelle Guyon, Alexandre R. Franco, Michael P. Milham, Aviv Dotan, Scott Makeig, Alexandre Gramfort, Jean-Remi King, Marie-Constance Corsi, Pedro A. Vald\'es-Sosa, Amit Majumdar, Alan Evans, Terrence J Sejnowski, Oren Shriki, Sylvain Chevallier, Arnaud Delorme

EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-based competition comprising two challenges. First, the Transfer Challenge asks participants to build and test a model that can zero-shot decode new tasks and new subjects from their EEG data. Second, the Psychopathology factor prediction Challenge asks participants to infer subject measures of mental health from EEG da…

Deep learning inference with the Event Horizon Telescope II. The Zingularity framework for Bayesian artificial neural networks
M. Janssen, C. -k. Chan, J. Davelaar, M. Wielgus
https://arxiv.org/abs/2506.13875

Deep learning inference with the Event Horizon Telescope II. The Zingularity framework for Bayesian artificial neural networks
(abridged) In this second paper in our publication series, we present the open-source Zingularity framework for parameter inference with deep Bayesian artificial neural networks. We carried out out supervised learning with synthetic millimeter very long baseline interferometry observations of the EHT. Our ground-truth models are based on GRMHD simulations of Sgr A* and M87* on horizon scales. We investigated how well Zingularity neural networks are able to infer key model parameters from EHT ob…

Bayesian Inference for Left-Truncated Log-Logistic Distributions for Time-to-event Data Analysis
Fahad Mostafa, Md Rejuan Haque, Md Mostafijur Rahman, Farzana Nasrin
https://arxiv.org/abs/2506.17852

Bayesian Inference for Left-Truncated Log-Logistic Distributions for Time-to-event Data Analysis
Parameter estimation is a foundational step in statistical modeling, enabling us to extract knowledge from data and apply it effectively. Bayesian estimation of parameters incorporates prior beliefs with observed data to infer distribution parameters probabilistically and robustly. Moreover, it provides full posterior distributions, allowing uncertainty quantification and regularization, especially useful in small or truncated samples. Utilizing the left-truncated log-logistic (LTLL) distributi…

This https://arxiv.org/abs/2505.20111 has been replaced.
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Preference Disaggregation Analysis with Criteria Selection in a Regularization Framework
Limited by cognitive abilities, decision-makers (DMs) may struggle to evaluate decision alternatives based on all criteria in multiple criteria decision-making problems. This paper proposes an embedded criteria selection method derived from preference disaggregation technique and regularization theory. The method aims to infer the criteria and value functions used by the DM to evaluate decision alternatives. It measures the quality of criteria subsets by investigating both the empirical error (…

Multi-omic Causal Discovery using Genotypes and Gene Expression
Stephen Asiedu, David Watson
https://arxiv.org/abs/2505.15866 https://

Multi-omic Causal Discovery using Genotypes and Gene Expression
Causal discovery in multi-omic datasets is crucial for understanding the bigger picture of gene regulatory mechanisms, but remains challenging due to high dimensionality, differentiation of direct from indirect relationships, and hidden confounders. We introduce GENESIS (GEne Network inference from Expression SIgnals and SNPs), a constraint-based algorithm that leverages the natural causal precedence of genotypes to infer ancestral relationships in transcriptomic data. Unlike traditional causal…

The space of multifurcating ranked tree shapes: enumeration, lattice structure, and Markov chains
Julie Zhang, Noah A. Rosenberg, Julia A. Palacios
https://arxiv.org/abs/2506.10856

The space of multifurcating ranked tree shapes: enumeration, lattice structure, and Markov chains
Coalescent models of bifurcating genealogies are used to infer evolutionary parameters from molecular data. However, there are many situations where bifurcating genealogies do not accurately reflect the true underlying ancestral history of samples, and a multifurcating genealogy is required. The space of multifurcating genealogical trees, where nodes can have more than two descendants, is largely underexplored in the setting of coalescent inference. In this paper, we examine the space of rooted…

Latent geometry emerging from network-driven processes
Andrea Filippo Beretta, Davide Zanchetta, Sebastiano Bontorin, Manlio De Domenico
https://arxiv.org/abs/2506.09616

Latent geometry emerging from network-driven processes
Understanding network functionality requires integrating structure and dynamics, and emergent latent geometry induced by network-driven processes captures the low-dimensional spaces governing this interplay. Here, we focus on generative-model-based approaches, distinguishing two reconstruction classes: fixed-time methods, which infer geometry at specific temporal scales (e.g., equilibrium), and multi-scale methods, which integrate dynamics across near- and far-from-equilibrium scales. Over the …

Sudoku: Decomposing DRAM Address Mapping into Component Functions
Minbok Wi, Seungmin Baek, Seonyong Park, Mattan Erez, Jung Ho Ahn
https://arxiv.org/abs/2506.15918

Sudoku: Decomposing DRAM Address Mapping into Component Functions
Decomposing DRAM address mappings into component-level functions is critical for understanding memory behavior and enabling precise RowHammer attacks, yet existing reverse-engineering methods fall short. We introduce novel timing-based techniques leveraging DRAM refresh intervals and consecutive access latencies to infer component-specific functions. Based on this, we present Sudoku, the first software-based tool to automatically decompose full DRAM address mappings into channel, rank, bank gro…

Bayesian Posteriors with Stellar Population Synthesis on GPUs
Georgios Zacharegkas, Andrew Hearin, Andrew Benson
https://arxiv.org/abs/2506.19919 https://

Bayesian Posteriors with Stellar Population Synthesis on GPUs
Models of Stellar Population Synthesis (SPS) provide a predictive framework for the spectral energy distribution (SED) of a galaxy. SPS predictions can be computationally intensive, creating a bottleneck for attempts to infer the physical properties of large populations of individual galaxies from their SEDs and photometry; these computational challenges are especially daunting for near-future cosmology surveys that will measure the photometry of billions of galaxies. In this paper, we explore …

Nearest neighbor permutation entropy detects phase transitions in complex high-pressure systems
Arthur A. B. Pessa, Leonardo G. J. M. Voltarelli, Lucio Cardozo-Filho, Andres G. M. Tamara, Claudio Dariva, Papa M. Ndiaye, Matjaz Perc, Haroldo V. Ribeiro
https://arxiv.org/abs/2506.06516

Nearest neighbor permutation entropy detects phase transitions in complex high-pressure systems
Understanding the high-pressure phase behavior of carbon dioxide-hydrocarbon mixtures is of considerable interest owing to their wide range of applications. Under certain conditions, these systems are not amenable to direct visual monitoring, and experimentalists often rely on spectrophotometric data to infer phase behavior. Consequently, developing computationally efficient and robust methods to leverage such data is crucial. Here, we combine nearest neighbor permutation entropy, computed dire…

Reconstructing Heterogeneous Biomolecules via Hierarchical Gaussian Mixtures and Part Discovery
Shayan Shekarforoush, David B. Lindell, Marcus A. Brubaker, David J. Fleet
https://arxiv.org/abs/2506.09063

Reconstructing Heterogeneous Biomolecules via Hierarchical Gaussian Mixtures and Part Discovery
Cryo-EM is a transformational paradigm in molecular biology where computational methods are used to infer 3D molecular structure at atomic resolution from extremely noisy 2D electron microscope images. At the forefront of research is how to model the structure when the imaged particles exhibit non-rigid conformational flexibility and compositional variation where parts are sometimes missing. We introduce a novel 3D reconstruction framework with a hierarchical Gaussian mixture model, inspired in…

Inference for microbe--metabolite association networks using a latent graph model
Jing Ma
https://arxiv.org/abs/2506.12275 https://ar…

Inference for microbe--metabolite association networks using a latent graph model
Correlation networks are commonly used to infer associations between microbes and metabolites. The resulting p-values are then corrected for multiple comparisons using existing methods such as the Benjamini and Hochberg procedure to control the false discovery rate (FDR). However, most existing methods for FDR control assume the p-values are weakly dependent. Consequently, they can have low power in recovering microbe-metabolite association networks that exhibit important topological features, …

Exploration of features in the black hole mass spectrum inspired by non-parametric analyses of gravitational wave observations
Stefano Rinaldi, Yajie Liang, Gabriele Demasi, Michela Mapelli, Walter Del Pozzo
https://arxiv.org/abs/2506.05929

Exploration of features in the black hole mass spectrum inspired by non-parametric analyses of gravitational wave observations
Current gravitational-wave data reveal structures in the mass function of binary compact objects. Properly modelling and deciphering such structures is the ultimate goal of gravitational-wave population analysis: in this context, non-parametric models are a powerful tool to infer the distribution of black holes from gravitational waves without committing to any specific functional form. Here, we aim to quantitatively corroborate the findings of non-parametric methods with parametrised models in…

Mass models of galaxy clusters from a non-parametric weak-lensing reconstruction
Tobias Mistele, Federico Lelli, Stacy McGaugh, James Schombert, Benoit Famaey
https://arxiv.org/abs/2506.13716

Mass models of galaxy clusters from a non-parametric weak-lensing reconstruction
We study the CLASH sample of galaxy clusters using a new deprojection method for weak gravitational lensing observations. This method is non-parametric, allowing us to infer mass profiles, or equivalently circular velocities, without having to assume a specific halo profile. While this method assumes spherical symmetry, we show that, on average, triaxiality is unlikely to significantly affect our results. We use this method to study the total mass profiles of the CLASH clusters, as well as the …

SceneCompleter: Dense 3D Scene Completion for Generative Novel View Synthesis
Weiliang Chen, Jiayi Bi, Yuanhui Huang, Wenzhao Zheng, Yueqi Duan
https://arxiv.org/abs/2506.10981

SceneCompleter: Dense 3D Scene Completion for Generative Novel View Synthesis
Generative models have gained significant attention in novel view synthesis (NVS) by alleviating the reliance on dense multi-view captures. However, existing methods typically fall into a conventional paradigm, where generative models first complete missing areas in 2D, followed by 3D recovery techniques to reconstruct the scene, which often results in overly smooth surfaces and distorted geometry, as generative models struggle to infer 3D structure solely from RGB data. In this paper, we propo…

This https://arxiv.org/abs/2410.21295 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

TRENDY: Gene Regulatory Network Inference Enhanced by Transformer
Gene regulatory networks (GRNs) play a crucial role in the control of cellular functions. Numerous methods have been developed to infer GRNs from gene expression data, including mechanism-based approaches, information-based approaches, and more recent deep learning techniques, the last of which often overlook the underlying gene expression mechanisms. In this work, we introduce TRENDY, a novel GRN inference method that integrates transformer models to enhance the mechanism-based WENDY approach.…

Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models
Huihan Liu, Rutav Shah, Shuijing Liu, Jack Pittenger, Mingyo Seo, Yuchen Cui, Yonatan Bisk, Roberto Mart\'in-Mart\'in, Yuke Zhu
https://arxiv.org/abs/2506.14727

Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models
Assistive teleoperation, where control is shared between a human and a robot, enables efficient and intuitive human-robot collaboration in diverse and unstructured environments. A central challenge in real-world assistive teleoperation is for the robot to infer a wide range of human intentions from user control inputs and to assist users with correct actions. Existing methods are either confined to simple, predefined scenarios or restricted to task-specific data distributions at training, limit…

ABYSS III: Observing accretion activity in young stars through empirical veiling measurements
Serat Saad, Marina Kounkel, Keivan G. Stassun, A. Roman-Lopes, Carlos G. Rom\'an-Z\'u\~niga, Jinyoung Serena Kim, Jonathan C. Tan, R. Lopez-Valdivia
https://arxiv.org/abs/2506.09826

ABYSS III: Observing accretion activity in young stars through empirical veiling measurements
Stellar accretion plays an important role in the early stages of stellar evolution, particularly in Classical T Tauri Stars (CTTSs). Accretion of a CTTS can be related to different physical parameters such as effective temperature (T$_{\text{eff}}$), age, abundance of hydrogen, etc. We can infer how accretion works by examining it across different wavelength regions. Accretion can be traced using veiling, a parameter that measures how excess emission from accretion affects the photospheric spec…

Modeling Gravitational Wave Bias from 3D Power Spectra of Spectroscopic Surveys
Dorsa Sadat Hosseini, Amir Dehghani, J. Leo Kim, Alex Krolewski, Suvodip Mukherjee, Ghazal Geshnizjani
https://arxiv.org/abs/2506.11201

Modeling Gravitational Wave Bias from 3D Power Spectra of Spectroscopic Surveys
We present a framework for relating gravitational wave (GW) sources to the astrophysical properties of spectroscopic galaxy samples. We show how this can enable using clustering measurements of gravitational wave (GW) sources to infer the relationship between the GW sources and the astrophysical properties of their host galaxies. We accomplish this by creating mock GW catalogs from the spectroscopic Sloan Digital Sky Survey (SDSS) DR7 galaxy survey. We populate the GWs using a joint host-galaxy…

Anomalously fast core and envelope rotation in red giants
Siddharth Dhanpal, Othman Benomar, Shravan Hanasoge, Jim Fuller
https://arxiv.org/abs/2506.06415 …

Anomalously fast core and envelope rotation in red giants
Red giants undergo dramatic and complex structural transformations as they evolve. Angular momentum is transported between the core and envelope during this epoch, a poorly understood process. Here, we infer envelope and core rotation rates from Kepler observations of $\sim$1517 red giants. While many measurements are consistent with the existing studies, our investigation reveals systematic changes in the envelope-to-core rotation ratio and we report the discovery of anomalies such as clump st…

This https://arxiv.org/abs/2503.09605 has been replaced.
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QWENDY: Gene Regulatory Network Inference Enhanced by Large Language Model and Transformer
Knowing gene regulatory networks (GRNs) is important for understanding various biological mechanisms. In this paper, we present a method, QWENDY, that uses single-cell gene expression data measured at four time points to infer GRNs. Based on a linear gene expression model, it solves the transformation for the covariance matrices. Unlike its predecessor WENDY, QWENDY avoids solving a non-convex optimization problem and produces a unique solution. Then we enhance QWENDY by the transformer neural …

Computing Nonlinear Power Spectra Across Dynamical Dark Energy Model Space with Neural ODEs
Peter L. Taylor
https://arxiv.org/abs/2506.09128 https://

Computing Nonlinear Power Spectra Across Dynamical Dark Energy Model Space with Neural ODEs
I show how to compute the nonlinear power spectrum across the entire $w(z)$ dynamical dark energy model space. Using synthetic $Λ$CDM data, I train a neural ordinary differential equation (ODE) to infer the evolution of the nonlinear matter power spectrum as a function of the background expansion and mean matter density across $\sim$$9 {\rm \ Gyr}$ of cosmic evolution. After training, the model generalises to {\it any} dynamical dark energy model parameterised by $w(z)$. With little optimisati…

Chemo-dynamics of the stellar component of the Sculptor dwarf galaxy II: dynamical properties and dark matter halo density
Jos\'e Mar\'ia Arroyo Polonio, Raffaele Pascale, Giuseppina Battaglia, Guillaume F. Thomas, Carlo Nipoti, Eugene Vasiliev, Eline Tolstoy
https://arxiv.org/abs/2506.11845

Chemo-dynamics of the stellar component of the Sculptor dwarf galaxy II: dynamical properties and dark matter halo density
Dwarf galaxies satellite of the Milky Way are excellent laboratories for testing dark matter (DM) models and baryonic feedback implementation in simulations. The Sculptor 'classical' dwarf spheroidal galaxy, a system with two distinct stellar populations and high-quality data, offers a remarkable opportunity to study DM distributions in these galaxies. In this work, we infer the DM halo density distribution of Sculptor, applying a method based on spherically symmetric distribution functions dep…

A Scalable Exponential Random Graph Model: Amortised Hierarchical Sequential Neural Posterior Estimation with Applications in Neuroscience
Yefeng Fan, Simon Richard White
https://arxiv.org/abs/2506.04558

A Scalable Exponential Random Graph Model: Amortised Hierarchical Sequential Neural Posterior Estimation with Applications in Neuroscience
Exponential Random Graph Models (ERGMs) are an inferential model for analysing statistical networks. Recent development in ERGMs uses hierarchical Bayesian setup to jointly model a group of networks, which is called a multiple-network Exponential Random Graph Model (MN-ERGMs). MN-ERGM has been successfully applied on real-world resting-state fMRI data from the Cam-CAN project to infer the brain connectivity on aging. However, conventional Bayesian ERGM estimation approach is computationally int…

Bayesian luminosity function estimation in multidepth datasets with selection effects: a case study for $3<z<5$ Ly$\alpha$ emitters
Davide Tornotti, Matteo Fossati, Michele Fumagalli, Davide Gerosa, Lorenzo Pizzuti, Fabrizio Arrigoni Battaia
https://arxiv.org/abs/2506.10083

Bayesian luminosity function estimation in multidepth datasets with selection effects: a case study for $3
We present a hierarchical Bayesian framework designed to infer the luminosity function of any class of objects by jointly modeling data from multiple surveys with varying depth, completeness, and sky coverage. Our method explicitly accounts for selection effects and measurement uncertainties (e.g., in luminosity) and could be generalized to any extensive quantity, such as mass. We validate the model using mock catalogs, recovering how deep data reaching $\gtrsim 1.5$ dex below a characteristic …