Tootfinder

Complementary Time-Space Tradeoff for Self-Stabilizing Leader Election: Polynomial States Meet Sublinear Time
Yuichi Sudo
https://arxiv.org/abs/2505.23649 …

Complementary Time-Space Tradeoff for Self-Stabilizing Leader Election: Polynomial States Meet Sublinear Time
We study the self-stabilizing leader election (SS-LE) problem in the population protocol model, assuming exact knowledge of the population size $n$. Burman, Chen, Chen, Doty, Nowak, Severson, and Xu (PODC 2021) showed that this problem can be solved in $O(n)$ expected time with $O(n)$ states. Recently, Gąsieniec, Grodzicki, and Stachowiak (PODC 2025) proved that $n+O(\log n)$ states suffice to achieve $O(n \log n)$ time both in expectation and with high probability (w.h.p.). If substantially m…

Disentangling the growth rate of perturbations from the HI bias using only clustering data from galaxy surveys
Pankaj Chavan, Tapomoy Guha Sarkar, Anjan A Sen
https://arxiv.org/abs/2506.22064

Disentangling the growth rate of perturbations from the HI bias using only clustering data from galaxy surveys
This work serves two-fold purpose. Firstly, we provide an alternative to the traditional method of determining the growth rate of density perturbations $f(z)$. In usual practice, $f(z)$ can not be directly measured from tracer clustering at some redshift without knowledge of the bias. While the bayron acoustic oscillation (BAO) imprint allows the determination of $(D_A(z), H(z))$, redshift space anisotropy (RSD) allows the measurement of a quantity $f_8(z) = f(z) σ_{8,0} D_{+}(z)$. To extract …

Joint attitude estimation and 3D neural reconstruction of non-cooperative space objects
Cl\'ement Forray, Pauline Delporte, Nicolas Delaygue, Florence Genin, Dawa Derksen
https://arxiv.org/abs/2506.20638

Joint attitude estimation and 3D neural reconstruction of non-cooperative space objects
Obtaining a better knowledge of the current state and behavior of objects orbiting Earth has proven to be essential for a range of applications such as active debris removal, in-orbit maintenance, or anomaly detection. 3D models represent a valuable source of information in the field of Space Situational Awareness (SSA). In this work, we leveraged Neural Radiance Fields (NeRF) to perform 3D reconstruction of non-cooperative space objects from simulated images. This scenario is challenging for N…

This https://arxiv.org/abs/2403.13627 has been replaced.
initial toot: https://mastoxiv.page/@a…

A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints
Materials design aims to discover novel compounds with desired properties. However, prevailing strategies face critical trade-offs. Conventional element-substitution approaches readily and adaptively incorporate various domain knowledge but remain confined to a narrow search space. In contrast, deep generative models efficiently explore vast compositional landscapes, yet they struggle to flexibly integrate domain knowledge. To address these trade-offs, we propose a gradient-based material desig…

Covariant phase space and $L_\infty$ algebras
Vin\'icius Bernardes, Theodore Erler, Atakan Hilmi F{\i}rat
https://arxiv.org/abs/2506.20706 https://

Covariant phase space and $L_\infty$ algebras
We propose a symplectic structure for the phase space of a generic Lagrangian field theory expressed in the framework of $L_\infty$ algebras. The symplectic structure does not require explicit knowledge of the derivative content of the Lagrangian, and therefore is applicable to nonlocal models, such as string field theory, where traditional constructions are difficult to apply. We test our proposal in a number of examples ranging from general relativity to $p$-adic string theory.

Exploring the Design Space of 3D MLLMs for CT Report Generation
Mohammed Baharoon, Jun Ma, Congyu Fang, Augustin Toma, Bo Wang
https://arxiv.org/abs/2506.21535

Exploring the Design Space of 3D MLLMs for CT Report Generation
Multimodal Large Language Models (MLLMs) have emerged as a promising way to automate Radiology Report Generation (RRG). In this work, we systematically investigate the design space of 3D MLLMs, including visual input representation, projectors, Large Language Models (LLMs), and fine-tuning techniques for 3D CT report generation. We also introduce two knowledge-based report augmentation methods that improve performance on the GREEN score by up to 10\%, achieving the 2nd place on the MICCAI 2024 …

Conversations over Clicks: Impact of Chatbots on Information Search in Interdisciplinary Learning
Hannah Kim, Sergei L. Kosakovsky Pond, Stephen MacNeil
https://arxiv.org/abs/2507.21490

Conversations over Clicks: Impact of Chatbots on Information Search in Interdisciplinary Learning
This full research paper investigates the impact of generative AI (GenAI) on the learner experience, with a focus on how learners engage with and utilize the information it provides. In e-learning environments, learners often need to navigate a complex information space on their own. This challenge is further compounded in interdisciplinary fields like bioinformatics, due to the varied prior knowledge and backgrounds. In this paper, we studied how GenAI influences information search in bioinfor…

Quantifying Coupled Dynamics in Phase-Space from State Distribution Snapshots
Erez Aghion, Nava Leibovich
https://arxiv.org/abs/2507.18648 https://arxiv.or…

Quantifying Coupled Dynamics in Phase-Space from State Distribution Snapshots
We quantify nonlinear interactions between coupled complex processes, when the system is subject to noise and not all its components are measurable. Our method is applicable even when the system cannot be continuously monitored over time, but is rather observed only in snapshots. Having only partial information about the local topology of the network and observations of relevant interacting variables is sufficient to translate qualitative knowledge of interactions into a quantitative characteri…

This https://arxiv.org/abs/2504.00744 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Spatiotemporal Synchronization of Distributed Arrays using Particle-Based Loopy Belief Propagation
Sensing and imaging with distributed radio infrastructures (e.g., distributed MIMO, wireless sensor networks, multistatic radar) rely on knowledge of the positions, orientations, and clock parameters of distributed apertures. We extend a particle-based loopy belief propagation (BP) algorithm to cooperatively synchronize distributed agents to anchors in space and time. Substituting marginalization over nuisance parameters with approximate but closed-form concentration, we derive an efficient est…

Optimal decay of semi-uniformly stable operator semigroups with empty spectrum
Morgan Callewaert, Lenny Neyt, Jasson Vindas
https://arxiv.org/abs/2507.20376 https://

Optimal decay of semi-uniformly stable operator semigroups with empty spectrum
We show that it is impossible to quantify the decay rate of a semi-uniformly stable operator semigroup based on sole knowledge of its spectrum. More precisely, given an arbitrary positive function $r$ vanishing at $\infty$, we construct a Banach space $X$ and a bounded semigroup $ (T(t))_{t \geq 0}$ of operators on it whose infinitesimal generator $A$ has empty spectrum $σ(A)=\varnothing$, but for which, for some $x \in X$, $$\limsup_{t\to\infty} \frac{\|T(t)A^{-1}x\|_{X}}{r(t)}=\inf…

A Large Language Model-based Multi-Agent Framework for Analog Circuits' Sizing Relationships Extraction
Chengjie Liu, Weiyu Chen, Huiyao Xu, Yuan Du, Jun Yang, Li Du
https://arxiv.org/abs/2506.18424

A Large Language Model-based Multi-Agent Framework for Analog Circuits' Sizing Relationships Extraction
In the design process of the analog circuit pre-layout phase, device sizing is an important step in determining whether an analog circuit can meet the required performance metrics. Many existing techniques extract the circuit sizing task as a mathematical optimization problem to solve and continuously improve the optimization efficiency from a mathematical perspective. But they ignore the automatic introduction of prior knowledge, fail to achieve effective pruning of the search space, which the…

A Collaborative Framework Integrating Large Language Model and Chemical Fragment Space: Mutual Inspiration for Lead Design
Hao Tuo, Yan Li, Xuanning Hu, Haishi Zhao, Xueyan Liu, Bo Yang
https://arxiv.org/abs/2507.13580

A Collaborative Framework Integrating Large Language Model and Chemical Fragment Space: Mutual Inspiration for Lead Design
Combinatorial optimization algorithm is essential in computer-aided drug design by progressively exploring chemical space to design lead compounds with high affinity to target protein. However current methods face inherent challenges in integrating domain knowledge, limiting their performance in identifying lead compounds with novel and valid binding mode. Here, we propose AutoLeadDesign, a lead compounds design framework that inspires extensive domain knowledge encoded in large language models…

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge
Chunhui Gu, Mohammad Sadegh Nasr, James P. Long, Kim-Anh Do, Ehsan Irajizad
https://arxiv.org/abs/2507.05540

Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge
Graph Neural Networks (GNNs) often struggle with noisy edges. We propose Latent Space Constrained Graph Neural Networks (LSC-GNN) to incorporate external "clean" links and guide embeddings of a noisy target graph. We train two encoders--one on the full graph (target plus external edges) and another on a regularization graph excluding the target's potentially noisy links--then penalize discrepancies between their latent representations. This constraint steers the model away from overfitting spur…

Knowledge Compression via Question Generation: Enhancing Multihop Document Retrieval without Fine-tuning
Anvi Alex Eponon, Moein Shahiki-Tash, Ildar Batyrshin, Christian E. Maldonado-Sifuentes, Grigori Sidorov, Alexander Gelbukh
https://arxiv.org/abs/2506.13778

Knowledge Compression via Question Generation: Enhancing Multihop Document Retrieval without Fine-tuning
This study presents a question-based knowledge encoding approach that improves retrieval-augmented generation (RAG) systems without requiring fine-tuning or traditional chunking. We encode textual content using generated questions that span the lexical and semantic space, creating targeted retrieval cues combined with a custom syntactic reranking method. In single-hop retrieval over 109 scientific papers, our approach achieves a Recall@3 of 0.84, outperforming traditional chunking methods by …

Finished reading "The Steerswoman" by Rosemary Kirstein. It's a great little fantasy novel. I'd call it "hard sci-fi" if it was set in space. Is there a term for medieval stories that don't get too magical?
The hero of this story is a member of a special guild that is all about preserving and acquiring knowledge. Sworn to speak the truth. Much of the story is her drawing conclusions about the world she inhabits. There are swords and scheming wizards. On t…

State Space Models Naturally Produce Traveling Waves, Time Cells, and Scale to Abstract Cognitive Functions
Sen Lu, Xiaoyu Zhang, Mingtao Hu, Eric Yeu-Jer Lee, Soohyeon Kim, Wei D. Lu
https://arxiv.org/abs/2507.13638

State Space Models Naturally Produce Traveling Waves, Time Cells, and Scale to Abstract Cognitive Functions
A grand challenge in modern neuroscience is to bridge the gap between the detailed mapping of microscale neural circuits and a mechanistic understanding of cognitive functions. While extensive knowledge exists about neuronal connectivity and biophysics, a significant gap remains in how these elements combine to produce flexible, learned behaviors. Here, we propose that a framework based on State-Space Models (SSMs), an emerging class of deep learning architectures, can bridge this gap. We argue…

Enhancing Cross-task Transfer of Large Language Models via Activation Steering
Xinyu Tang, Zhihao Lv, Xiaoxue Cheng, Junyi Li, Wayne Xin Zhao, Zujie Wen, Zhiqiang Zhang, Jun Zhou
https://arxiv.org/abs/2507.13236

Enhancing Cross-task Transfer of Large Language Models via Activation Steering
Large language models (LLMs) have shown impressive abilities in leveraging pretrained knowledge through prompting, but they often struggle with unseen tasks, particularly in data-scarce scenarios. While cross-task in-context learning offers a direct solution for transferring knowledge across tasks, it still faces critical challenges in terms of robustness, scalability, and efficiency. In this paper, we investigate whether cross-task transfer can be achieved via latent space steering without par…

zkSDK: Streamlining zero-knowledge proof development through automated trace-driven ZK-backend selection
William Law
https://arxiv.org/abs/2507.05294 https…

zkSDK: Streamlining zero-knowledge proof development through automated trace-driven ZK-backend selection
The rapid advancement of creating Zero-Knowledge (ZK) programs has led to the development of numerous tools designed to support developers. Popular options include being able to write in general-purpose programming languages like Rust from Risc Zero. Other languages exist like Circom, Lib-snark, and Cairo. However, developers entering the ZK space are faced with many different ZK backends to choose from, leading to a steep learning curve and a fragmented developer experience across different pl…

Limb-Darkening Coefficients for the 4-Term and Power-2 Laws for the JWST Space Mission, Adopting PHOENIX Spherical Models at High Resolution
A. Claret (IAA), P. H. Hauschildt (Hamburger Sternwarte), G. Torres (CfA)
https://arxiv.org/abs/2506.07265

Limb-Darkening Coefficients for the 4-Term and Power-2 Laws for the JWST Space Mission, Adopting PHOENIX Spherical Models at High Resolution
Modeling observations of transiting exoplanets or close binary systems by comparing the observations with theoretical light curves requires precise knowledge of the distribution of specific intensities across the stellar disk. We aim to facilitate this type of research by providing extensive tabulations of limb-darkening coefficients for 11 frequently used near- and mid-infrared passbands on the NIRCam, NIRISS, and NIRSpec instruments installed on board the James Webb Space Telescope. The calcu…

KINDLE: Knowledge-Guided Distillation for Prior-Free Gene Regulatory Network Inference
Rui Peng, Yuchen Lu, Qichen Sun, Yuxing Lu, Chi Zhang, Ziru Liu, Jinzhuo Wang
https://arxiv.org/abs/2505.09664

KINDLE: Knowledge-Guided Distillation for Prior-Free Gene Regulatory Network Inference
Gene regulatory network (GRN) inference serves as a cornerstone for deciphering cellular decision-making processes. Early approaches rely exclusively on gene expression data, thus their predictive power remain fundamentally constrained by the vast combinatorial space of potential gene-gene interactions. Subsequent methods integrate prior knowledge to mitigate this challenge by restricting the solution space to biologically plausible interactions. However, we argue that the effectiveness of thes…

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making
Yucen Wang, Rui Yu, Shenghua Wan, Le Gan, De-Chuan Zhan
https://arxiv.org/abs/2507.12496

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making
Foundation Models (FMs) and World Models (WMs) offer complementary strengths in task generalization at different levels. In this work, we propose FOUNDER, a framework that integrates the generalizable knowledge embedded in FMs with the dynamic modeling capabilities of WMs to enable open-ended task solving in embodied environments in a reward-free manner. We learn a mapping function that grounds FM representations in the WM state space, effectively inferring the agent's physical states in the wo…

Stability of Gel'fand's inverse interior spectral problem for Schr\"odinger operators
Jinpeng Lu
https://arxiv.org/abs/2507.15560 https://

Stability of Gel'fand's inverse interior spectral problem for Schrödinger operators
We study Gel'fand's inverse interior spectral problem of determining a closed Riemannian manifold $(M,g)$ and a potential function $q$ from the knowledge of the eigenvalues $λ_j$ of the Schrödinger operator $-Δ_g + q$ and the restriction of the eigenfunctions $ϕ_j|_U$ on a given open subset $U\subset M$, where $Δ_g$ is the Laplace-Beltrami operator on $(M,g)$. We prove that an approximation of finitely many spectral data on $U$ determines a finite metric space that is close to $(M,g)$…

Blind Source Separation in Biomedical Signals Using Variational Methods
Yasaman Torabi, Shahram Shirani, James P. Reilly
https://arxiv.org/abs/2506.18281 h…

Blind Source Separation in Biomedical Signals Using Variational Methods
This study introduces a novel unsupervised approach for separating overlapping heart and lung sounds using variational autoencoders (VAEs). In clinical settings, these sounds often interfere with each other, making manual separation difficult and error-prone. The proposed model learns to encode mixed signals into a structured latent space and reconstructs the individual components using a probabilistic decoder, all without requiring labeled data or prior knowledge of source characteristics. We …

In this moment when equity & inclusion efforts are being hindered & erased, the DLF Committee on Equity & Inclusion is creating a Zotero library of related resources for the GLAM community. Let us know what topics would be most helpful to you: https://docs.google.com/f…

This https://arxiv.org/abs/2410.16860 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Typical Quantum States of the Universe are Observationally Indistinguishable
We establish three new impossibility results regarding our knowledge of the quantum state of the universe -- a central object in quantum theory. We show that, if the universal quantum state is a typical unit vector from a high-dimensional subspace H_0 of Hilbert space H (such as the one defined by a low-entropy macro-state as prescribed by the Past Hypothesis), then no observation can determine or just significantly narrow down which vector it is. In other words, the overwhelming majority of po…

Replaced article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new/
[1/1]:
Milgram's experiment in the knowledge space: Individual navigation strategies

Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering
Linhao Ye, Lang Yu, Zhikai Lei, Qin Chen, Jie Zhou, Liang He
https://arxiv.org/abs/2506.00491

Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering
Retrieval-augmented generation (RAG) is usually integrated into large language models (LLMs) to mitigate hallucinations and knowledge obsolescence. Whereas,conventional one-step retrieve-and-read methods are insufficient for multi-hop question answering, facing challenges of retrieval semantic mismatching and the high cost in handling interdependent subquestions. In this paper, we propose Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering (Q-DREAM). …

Bridging Logic and Learning: Decoding Temporal Logic Embeddings via Transformers
Sara Candussio, Gaia Saveri, Gabriele Sarti, Luca Bortolussi
https://arxiv.org/abs/2507.07808

Bridging Logic and Learning: Decoding Temporal Logic Embeddings via Transformers
Continuous representations of logic formulae allow us to integrate symbolic knowledge into data-driven learning algorithms. If such embeddings are semantically consistent, i.e. if similar specifications are mapped into nearby vectors, they enable continuous learning and optimization directly in the semantic space of formulae. However, to translate the optimal continuous representation into a concrete requirement, such embeddings must be invertible. We tackle this issue by training a Transformer…

Schatten-von Neumann classes of tensors of invariant operators
Julio Delgado, Liliana Posada, Michael Ruzhansky
https://arxiv.org/abs/2507.15055 https://…

Schatten-von Neumann classes of tensors of invariant operators
In this work we study Schatten-von Neumann classes of tensor products of invariant operators on Hilbert spaces. In the first part we first deduce some spectral properties for tensors of anharmonic oscillators thanks to the knowledge on corresponding Schatten-von Neumann properties. In the second part we specialised on tensors of invariant operators. In the special case where a suitable Fourier analysis associated to a fixed partition of a Hilbert space into finite dimensional subspaces is avail…

This https://arxiv.org/abs/2502.19236 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Universal Topology of Exceptional Points in Nonlinear Non-Hermitian Systems
Exceptional points (EPs) are non-Hermitian degeneracies where eigenvalues and eigenvectors coalesce, giving rise to unusual physical effects across scientific disciplines. The concept of EPs has recently been extended to nonlinear physical systems. We theoretically demonstrate a universal topology in the nonlinear parameter space for a large class of physical systems that support $2^\mathrm{nd}$ order EPs in the linear regime. Knowledge of this topology (called elliptic umbilic singularity in b…

This https://arxiv.org/abs/2209.01847 has been replaced.
link: https://scholar.google.com/scholar?q=a

Conflict-Aware Pseudo Labeling via Optimal Transport for Entity Alignment
Entity alignment aims to discover unique equivalent entity pairs with the same meaning across different knowledge graphs (KGs). Existing models have focused on projecting KGs into a latent embedding space so that inherent semantics between entities can be captured for entity alignment. However, the adverse impacts of alignment conflicts have been largely overlooked during training, thereby limiting the entity alignment performance. To address this issue, we propose a novel Conflict-aware Pseudo…

Constraining decaying dark matter models with gravitational lensing and cosmic voids
Earl Lester, Krzysztof Bolejko
https://arxiv.org/abs/2507.08275 https:…

Constraining decaying dark matter models with gravitational lensing and cosmic voids
Despite overwhelming observational evidence for dark matter, we still have no evidence of direct detection. Consequently, our knowledge about dark matter is limited, for example, we do not know if dark matter is a stable particle or if it decays. Without a theoretical particle model, the parameter space of possible decay models is highly variable, and astrophysical or cosmological means of indirectly constraining the phenomenological models are required. This paper investigates a scenario in wh…

Towards generating more interpretable counterfactuals via concept vectors: a preliminary study on chest X-rays
Bulat Maksudov, Kathleen Curran, Alessandra Mileo
https://arxiv.org/abs/2506.04058

Towards generating more interpretable counterfactuals via concept vectors: a preliminary study on chest X-rays
An essential step in deploying medical imaging models is ensuring alignment with clinical knowledge and interpretability. We focus on mapping clinical concepts into the latent space of generative models to identify Concept Activation Vectors (CAVs). Using a simple reconstruction autoencoder, we link user-defined concepts to image-level features without explicit label training. The extracted concepts are stable across datasets, enabling visual explanations that highlight clinically relevant feat…

Unique and Stable Recovery of Space-Variable Order in Multidimensional Subdiffusion
Jiho Hong, Bangti Jin, Yavar Kian
https://arxiv.org/abs/2507.06524 https://arxiv.org/pdf/2507.06524 https://arxiv.org/html/2507.06524
arXiv:2507.06524v1 Announce Type: new
Abstract: In this work we investigate the unique identifiability and stable recovery of a spatially dependent variable-order in the subdiffusion model from the boundary flux measurement. We establish several new unique identifiability results from the observation at one point on the boundary without / with the knowledge of medium properties, and a conditional Lipschitz stability when the observation is available on the whole boundary. The analysis crucially employs resolvent estimates in $L^r(\Omega)$, $r>2$, spaces, solution representation in the Laplace domain and novel asymptotic expansions of the Laplace transform of the boundary flux at $p= 0$ and $p=1$.
toXiv_bot_toot

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

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

Beyond Retrieval: Ensembling Cross-Encoders and GPT Rerankers with LLMs for Biomedical QA
Shashank Verma, Fengyi Jiang, Xiangning Xue
https://arxiv.org/abs/2507.05577

Beyond Retrieval: Ensembling Cross-Encoders and GPT Rerankers with LLMs for Biomedical QA
Biomedical semantic question answering rooted in information retrieval can play a crucial role in keeping up to date with vast, rapidly evolving and ever-growing biomedical literature. A robust system can help researchers, healthcare professionals and even layman users access relevant knowledge grounded in evidence. The BioASQ 2025 Task13b Challenge serves as an important benchmark, offering a competitive platform for advancement of this space. This paper presents the methodologies and results …

Impact of redshift distribution uncertainties on Lyman-break galaxy cosmological parameter inference
Francesco Petri, Boris Leistedt, Daniel J. Mortlock, Joel Leja, Stephen Thorp, Justin Alsing, Hiranya V. Peiris, Sinan Deger
https://arxiv.org/abs/2506.06475

Impact of redshift distribution uncertainties on Lyman-break galaxy cosmological parameter inference
A significant number of Lyman-break galaxies (LBGs) with redshifts 3 < z < 5 are expected to be observed by the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). This will enable us to probe the universe at higher redshifts than is currently possible with cosmological galaxy clustering and weak lensing surveys. However, accurate inference of cosmological parameters requires precise knowledge of the redshift distributions of selected galaxies, where the number of faint o…