Tootfinder

Index Theory on Incomplete Cusp Edge Spaces
Jayson Liu
https://arxiv.org/abs/2508.01982 https://arxiv.org/pdf/2508.01982

Index Theory on Incomplete Cusp Edge Spaces
We study Dirac-type operators on incomplete cusp edge spaces with invertible boundary families. In particular, we construct the heat kernel for the associated Laplace-type operator and prove that the Dirac operators are essentially self-adjoint and Fredholm on their unique self adjoint domain. Using the asymptotics of the heat kernel and a generalisation of Getzler's rescaling argument we establish an index formula for these operators including a signature formula for the Hodge-de Rham operator…

Recovering Individual-Level Activity Sequences from Location-Based Service Data Using a Novel Transformer-Based Model
Weiyu Luo, Chenfeng Xiong
https://arxiv.org/abs/2508.02734 …

Recovering Individual-Level Activity Sequences from Location-Based Service Data Using a Novel Transformer-Based Model
Location-Based Service (LBS) data provides critical insights into human mobility, yet its sparsity often yields incomplete trip and activity sequences, making accurate inferences about trips and activities difficult. We raise a research problem: Can we use activity sequences derived from high-quality LBS data to recover incomplete activity sequences at the individual level? This study proposes a new solution, the Variable Selection Network-fused Insertion Transformer (VSNIT), integrating the In…

Hierarchical MoE: Continuous Multimodal Emotion Recognition with Incomplete and Asynchronous Inputs
Yitong Zhu, Lei Han, GuanXuan Jiang, PengYuan Zhou, Yuyang Wang
https://arxiv.org/abs/2508.02133

Hierarchical MoE: Continuous Multimodal Emotion Recognition with Incomplete and Asynchronous Inputs
Multimodal emotion recognition (MER) is crucial for human-computer interaction, yet real-world challenges like dynamic modality incompleteness and asynchrony severely limit its robustness. Existing methods often assume consistently complete data or lack dynamic adaptability. To address these limitations, we propose a novel Hi-MoE~(Hierarchical Mixture-of-Experts) framework for robust continuous emotion prediction. This framework employs a dual-layer expert structure. A Modality Expert Bank util…

Zero-Error Nash Equilibrium: Harnessing Nonlocal Correlation in Incomplete Information Games
Ambuj, Tushar, Siddharth R. Pandey, Ram Krishna Patra, Anandamay Das Bhowmik, Kuntal Som, Amit Mukherjee
https://arxiv.org/abs/2509.02947

Zero-Error Nash Equilibrium: Harnessing Nonlocal Correlation in Incomplete Information Games
Claude Shannon's zero-error communication paradigm reshaped our understanding of fault-tolerant information transfer. Here, we adapt this notion into game theory with incomplete information. We ask: can players with private information coordinate on a Nash equilibrium with zero probability of error? We identify Bayesian games in which such coordination is impossible classically, yet achievable by harnessing Bell nonlocal correlations. We formalize this requirement as zero-error Nash equilibrium…

Statistical modeling of SOFR term structure
Teemu Pennanen, Waleed Taoum
https://arxiv.org/abs/2508.02691 https://arxiv.org/pdf/2508.02691

Statistical modeling of SOFR term structure
SOFR derivatives market remains illiquid and incomplete so it is not amenable to classical risk-neutral term structure models which are based on the assumption of perfect liquidity and completeness. This paper develops a statistical SOFR term structure model that is well-suited for risk management and derivatives pricing within the incomplete markets paradigm. The model incorporates relevant macroeconomic factors that drive central bank policy rates which, in turn, cause jumps often observed in…

PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation
Jiajun He, Naoki Sawada, Koichi Miyazaki, Tomoki Toda
https://arxiv.org/abs/2509.04357 https…

PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation
Automatic speech recognition (ASR) systems struggle with domain-specific named entities, especially homophones. Contextual ASR improves recognition but often fails to capture fine-grained phoneme variations due to limited entity diversity. Moreover, prior methods treat entities as independent tokens, leading to incomplete multi-token biasing. To address these issues, we propose Phoneme-Augmented Robust Contextual ASR via COntrastive entity disambiguation (PARCO), which integrates phoneme-aware …

Remote Estimation for Markov Jump Linear Systems: A Distributionally Robust Approach
Ioannis Tzortzis, Themistoklis Charalambous, Charalambos D. Charalambous
https://arxiv.org/abs/2509.04116

Remote Estimation for Markov Jump Linear Systems: A Distributionally Robust Approach
This paper considers the problem of remote state estimation for Markov jump linear systems in the presence of uncertainty in the posterior mode probabilities. Such uncertainty may arise when the estimator receives noisy or incomplete measurements over an unreliable communication network. To address this challenge, the estimation problem is formulated within a distributionally robust framework, where the true posterior is assumed to lie within a total variation distance ball centered at the nomi…

On Pioneering Works of Albert Shiryaev on Markov Decision Processes and Some Later Developments
Eugene A. Feinberg
https://arxiv.org/abs/2506.04896 https:/…

On Pioneering Works of Albert Shiryaev on Markov Decision Processes and Some Later Developments
This article is dedicated to three fundamental papers on Markov Decision Processes and on control with incomplete observations published by Albert Shiryaev approximately sixty years ago. One of these papers was coauthored with O.V. Viskov. We discuss some of the results and some of many rich ideas presented in these papers and survey some later developments. At the end we mention some recent studies of Albert Shiryaev on Kolmogorov's equations for jump Markov processes and on control of continu…

From my LinkedIn post: “Telling your dev team to use AI coding tools is like telling your 2010 ops team to use AWS. They didn’t know how to code, they were ticket and click-it VMware people… developers who don’t have product management mindset or have never managed a dev team will fail by trying to micromanage the output of the tool rather than specifying the outcome of the product and managing the agent team to deliver that outcome.”

Back in 2010 when we first started using AWS to run Netflix, the AWS services were very incomplete, somewhat unreliable, had scaling issues, but were super fast to provision compared to filing… | Adrian Cockcroft
Back in 2010 when we first started using AWS to run Netflix, the AWS services were very incomplete, somewhat unreliable, had scaling issues, but were super fast to provision compared to filing tickets and waiting for data center ops teams to do what we need. We worked around the issues, built a new cloud native developer driven architecture and scaled Netflix at speed. At the time many people said we were crazy to run a large enterprise on cloud and would be back in datacenters when it failed. …

Meta-Imputation Balanced (MIB): An Ensemble Approach for Handling Missing Data in Biomedical Machine Learning
Fatemeh Azad, Zoran Bosni\'c, Matja\v{z} Kukar
https://arxiv.org/abs/2509.03316

Meta-Imputation Balanced (MIB): An Ensemble Approach for Handling Missing Data in Biomedical Machine Learning
Missing data represents a fundamental challenge in machine learning applications, often reducing model performance and reliability. This problem is particularly acute in fields like bioinformatics and clinical machine learning, where datasets are frequently incomplete due to the nature of both data generation and data collection. While numerous imputation methods exist, from simple statistical techniques to advanced deep learning models, no single method consistently performs well across divers…

Revisiting Adversarial Patch Defenses on Object Detectors: Unified Evaluation, Large-Scale Dataset, and New Insights
Junhao Zheng, Jiahao Sun, Chenhao Lin, Zhengyu Zhao, Chen Ma, Chong Zhang, Cong Wang, Qian Wang, Chao Shen
https://arxiv.org/abs/2508.00649

Revisiting Adversarial Patch Defenses on Object Detectors: Unified Evaluation, Large-Scale Dataset, and New Insights
Developing reliable defenses against patch attacks on object detectors has attracted increasing interest. However, we identify that existing defense evaluations lack a unified and comprehensive framework, resulting in inconsistent and incomplete assessments of current methods. To address this issue, we revisit 11 representative defenses and present the first patch defense benchmark, involving 2 attack goals, 13 patch attacks, 11 object detectors, and 4 diverse metrics. This leads to the large-s…

Are We on the Right Way for Assessing Document Retrieval-Augmented Generation?
Wenxuan Shen, Mingjia Wang, Yaochen Wang, Dongping Chen, Junjie Yang, Yao Wan, Weiwei Lin
https://arxiv.org/abs/2508.03644

Are We on the Right Way for Assessing Document Retrieval-Augmented Generation?
Retrieval-Augmented Generation (RAG) systems using Multimodal Large Language Models (MLLMs) show great promise for complex document understanding, yet their development is critically hampered by inadequate evaluation. Current benchmarks often focus on specific part of document RAG system and use synthetic data with incomplete ground truth and evidence labels, therefore failing to reflect real-world bottlenecks and challenges. To overcome these limitations, we introduce Double-Bench: a new large…

Multi-period Asset-liability Management with Reinforcement Learning in a Regime-Switching Market
Zhongqin Gao, Ping Chen, Xun Li, Yan Lv, Wenhao Zhang
https://arxiv.org/abs/2509.03251

Multi-period Asset-liability Management with Reinforcement Learning in a Regime-Switching Market
This paper explores the mean-variance portfolio selection problem in a multi-period financial market characterized by regime-switching dynamics and uncontrollable liabilities. To address the uncertainty in the decision-making process within the financial market, we incorporate reinforcement learning (RL) techniques. Specifically, the study examines an exploratory mean-variance (EMV) framework where investors aim to minimize risk while maximizing returns under incomplete market information, infl…

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- Adaptive greedy forward variable selection for linear regression models with incomplete data usin...
Yong-Shiuan Lee

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

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

Rigidity aspects of a cosmological singularity theorem
Eric Ling, Carl Rossdeutscher, Walter Simon, Roland Steinbauer
https://arxiv.org/abs/2508.00524 https://

Rigidity aspects of a cosmological singularity theorem
Inspired by the classical singularity theorems in General Relativity, Galloway and Ling have shown the following (cf. Theorem 0): If a globally hyperbolic spacetime satisfying the null energy condition contains a closed, spacelike Cauchy surface $(V,g,K)$ (with metric $g$ and extrinsic curvature ${K}$) which is strictly 2-convex (meaning that the sum of the lowest two eigenvalues of $K$ is positive) then $V$ is either a spherical space or past null geodesically incomplete \cite{Ling, Ling2}. In…

This https://arxiv.org/abs/2312.13658 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Sample-based nonlinear detectability for discrete-time systems
This paper introduces two sample-based formulations of incremental input/output-to-state stability (i-IOSS), a suitable detectability notion for general nonlinear systems. In this work we consider the case of limited output information, i.e., measurements are only infrequently and/or irregularly available. The output-dependent term of the sample-based i-IOSS bound is properly modified to yield a characterization for detectability in presence of incomplete output sequences. We provide both a non…

Fragmented eigenstate thermalization versus robust integrability in long-range models
Soumya Kanti Pal, Lea F Santos
https://arxiv.org/abs/2508.00077 https://

Fragmented eigenstate thermalization versus robust integrability in long-range models
Understanding the stability of integrability in many-body quantum systems is key to controlling their dynamics and predicting thermalization. While much is known about how integrability breaks down in short-range interacting systems, the corresponding picture for long-range couplings remains incomplete. Yet long-range interactions are both ubiquitous in nature and readily engineered in modern experimental platforms. Here, we show that integrability in fully connected models is either robust or …

Yo @…, I'm #ActuallyAutistic!

Effective resource allocation to combat invasions of the spotted lanternfly (Lycorma delicatula) and similar pests
Daniel Str\"ombom, Julianna Hoitt, Jinrong Hu, Swati Pandey, Elizabeth Batchelar
https://arxiv.org/abs/2508.01117

Effective resource allocation to combat invasions of the spotted lanternfly (Lycorma delicatula) and similar pests
The spotted lanternfly is rapidly establishing itself as a major insect pest with global implications. Despite significant management efforts, its spread continues in invaded regions, and refined management strategies are required. A recent study introduced a model that generalized the results of empirical control efficacy studies by incorporating population dynamics and incomplete delivery. In particular, a generalized population growth formula was derived, providing the minimum proportion of …

LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding
Yuchen Ma, Dennis Frauen, Jonas Schweisthal, Stefan Feuerriegel
https://arxiv.org/abs/2507.02843

LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding
Estimating treatment effects is crucial for personalized decision-making in medicine, but this task faces unique challenges in clinical practice. At training time, models for estimating treatment effects are typically trained on well-structured medical datasets that contain detailed patient information. However, at inference time, predictions are often made using textual descriptions (e.g., descriptions with self-reported symptoms), which are incomplete representations of the original patient i…

When Prompts Go Wrong: Evaluating Code Model Robustness to Ambiguous, Contradictory, and Incomplete Task Descriptions
Maya Larbi, Amal Akli, Mike Papadakis, Rihab Bouyousfi, Maxime Cordy, Federica Sarro, Yves Le Traon
https://arxiv.org/abs/2507.20439

When Prompts Go Wrong: Evaluating Code Model Robustness to Ambiguous, Contradictory, and Incomplete Task Descriptions
Large Language Models (LLMs) have demonstrated impressive performance in code generation tasks under idealized conditions, where task descriptions are clear and precise. However, in practice, task descriptions frequently exhibit ambiguity, incompleteness, or internal contradictions. In this paper, we present the first empirical study examining the robustness of state-of-the-art code generation models when faced with such unclear task descriptions. We extend the HumanEval and MBPP benchmarks by …

Learning Lifted Action Models From Traces of Incomplete Actions and States
Niklas Jansen, Jonas G\"osgens, Hector Geffner
https://arxiv.org/abs/2508.21449 https://

Learning Lifted Action Models From Traces of Incomplete Actions and States
Consider the problem of learning a lifted STRIPS model of the sliding-tile puzzle from random state-action traces where the states represent the location of the tiles only, and the actions are the labels up, down, left, and right, with no arguments. Two challenges are involved in this problem. First, the states are not full STRIPS states, as some predicates are missing, like the atoms representing the position of the ``blank''. Second, the actions are not full STRIPS either, as they do not reve…

Triangle Counting in Hypergraph Streams: A Complete and Practical Approach
Lingkai Meng, Long Yuan, Xuemin Lin, Wenjie Zhang, Ying Zhang
https://arxiv.org/abs/2509.00674 https:/…

Triangle Counting in Hypergraph Streams: A Complete and Practical Approach
Triangle counting in hypergraph streams, including both hyper-vertex and hyper-edge triangles, is a fundamental problem in hypergraph analytics, with broad applications. However, existing methods face two key limitations: (i) an incomplete classification of hyper-vertex triangle structures, typically considering only inner or outer triangles; and (ii) inflexible sampling schemes that predefine the number of sampled hyperedges, which is impractical under strict memory constraints due to highly v…

An elementary classification of the quaternionic reflection groups of rank two
Shayne Waldron
https://arxiv.org/abs/2509.01849 https://arxiv.org/pdf/2509.0…

An elementary classification of the quaternionic reflection groups of rank two
We give an elementary classification and presentation of the finite quaternionic reflection groups of rank two, based on the notion of a``reflection system''. This simplifies the existing classification, which is shown to be incomplete, e.g., there exist four imprimitive quaternionic reflection groups of order 192 with 22 reflections which are not isomorphic (one of which was previously unknown).

Localized Supervised Learning for Cryo-ET Reconstruction
Vinith Kishore, Valentin Debarnot, AmirEhsan Khorashadizadeh, Ivan Dokmani\'c
https://arxiv.org/abs/2509.00968 https…

Localized Supervised Learning for Cryo-ET Reconstruction
Cryo-electron tomography (Cryo-ET) is a powerful tool in structural biology for 3D visualization of cells and biological systems at resolutions sufficient to identify individual proteins in situ. The measurements are collected by tilting the frozen specimen and exposing it to an electron beam of known dosage. As the biological samples are prone to electron damage, the samples can be exposed to only a limited dosage of electrons, leading to noisy and incomplete measurements. Thus, the reconstruc…

Basic scientists' disproportionate impact amid applied drift reveals structural asymmetry in science
Rikuei Kaku, Mikako Bito, Keita Nishimoto, Ichiro Sakata, Kimitaka Asatani
https://arxiv.org/abs/2509.01530

Basic scientists' disproportionate impact amid applied drift reveals structural asymmetry in science
Despite broad acclaim for basic research, science is undergoing an applied shift that marginalizes basic scientists. This gap reflects an incomplete understanding of their distinctive roles, which prevents translating philosophical appreciation into effective support. We introduce a scalable metric--the application score--to position research along the basic-applied spectrum and apply it to 62 million publications (1970-2023) to reveal the distinctive contributions of basic scientists. We find …

Emerging Activity Temporal Hypergraph (EATH), a model for generating realistic time-varying hypergraphs
Marco Mancastroppa, Giulia Cencetti, Alain Barrat
https://arxiv.org/abs/2507.01124

Emerging Activity Temporal Hypergraph (EATH), a model for generating realistic time-varying hypergraphs
Time-varying group interactions constitute the building blocks of many complex systems. The framework of temporal hypergraphs makes it possible to represent them by taking into account the higher-order and temporal nature of the interactions. However, the corresponding datasets are often incomplete and/or limited in size and duration, and surrogate time-varying hypergraphs able to reproduce their statistical features constitute interesting substitutions, especially to understand how dynamical p…

Atmospheric model-trained machine learning selection and classification of ultracool TY dwarfs
Ankit Biswas
https://arxiv.org/abs/2507.00957 https://

Atmospheric model-trained machine learning selection and classification of ultracool TY dwarfs
The T and Y spectral classes represent the coolest and lowest-mass population of brown dwarfs, yet their census remains incomplete due to limited statistics. Existing detection frameworks are often constrained to identifying M, L, and early T dwarfs, owing to the sparse observational sample of ultracool dwarfs (UCDs) at later types. This paper presents a novel machine learning framework capable of detecting and classifying late-T and Y dwarfs, trained entirely on synthetic photometry from atmos…

Predicting Qualification Thresholds in UEFA's incomplete round-robin tournaments via a Dixon and Coles Model
David Winkelmann, Rouven Michels, Christian Deutscher
https://arxiv.org/abs/2508.20075

Predicting Qualification Thresholds in UEFA's incomplete round-robin tournaments via a Dixon and Coles Model
In the 2024/25 season, UEFA introduced the incomplete round-robin format in the Champions League and Europa League, replacing the traditional group stage with a single league phase table of all 36 teams. Now, the top eight qualify directly for the round of 16, while teams ranked 9th-24th enter a play-off round. Existing simulation-based analyses, such as those by Opta, provide guidance on points presumably needed for qualification but show discrepancies when compared to actual outcomes in the f…

Combinatorial solutions to the Social Golfer Problem and Social Golfer Problem with Adjacent Group Sizes
Alice Miller, Ivaylo Valkov, R. Julian R. Abel
https://arxiv.org/abs/2507.23376

Combinatorial solutions to the Social Golfer Problem and Social Golfer Problem with Adjacent Group Sizes
Resolvable combinatorial designs including Resolvable Balanced Incomplete Block Designs, Resolvable Group Divisible Designs, Uniformly Resolvable Designs and Mutually Orthogonal Latin Squares and Rectangles are used to construct optimal solutions to the Social Golfer problem (SGP) and the Social Golfer problem with adjacent group sizes (SGA). An algorithm is presented to find an optimal solution in general, and a complete set of solutions is provided for up to 150 players.

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/4]:
- Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification
Chengliang Liu, Jie Wen, Yong Xu, Bob Zhang, Liqiang Nie, Min Zhang

Filling of incomplete sinograms from sparse PET detector configurations using a residual U-Net
Klara Leffler, Luigi Tommaso Luppino, Samuel Kuttner, Karin S\"oderkvist, Jan Axelsson
https://arxiv.org/abs/2506.19600

Filling of incomplete sinograms from sparse PET detector configurations using a residual U-Net
Long axial field-of-view PET scanners offer increased field-of-view and sensitivity compared to traditional PET scanners. However, a significant cost is associated with the densely packed photodetectors required for the extended-coverage systems, limiting clinical utilisation. To mitigate the cost limitations, alternative sparse system configurations have been proposed, allowing an extended field-of-view PET design with detector costs similar to a standard PET system, albeit at the expense of i…

i reminded myself of an acronym i learned from a book i bought over 30 years ago
there was an amazing amount of _pnambic_ AI going on in the years before the launch of ChatGPT
i wonder how much of it still exists
eg i know there are two people employed as remote-operator drivers per google “driverless” car

In the hands of someone like Trump,
deals are ways to evade, postpone or subvert the efficient work of markets.
Trump does not like markets,
precisely because they are impersonal and objective.
Their results – for corporations, entrepreneurs, investors and shareholders – are subject to clear measures of success and failure.
Because deals are personal, adversarial and incomplete,
they are perfect grist for Trump’s relentless publicity machine,
and a…

Temperature and Magnetic-Field Dependence of Energy Relaxation in a Fluxonium Qubit
Lamia Ateshian, Max Hays, David A. Rower, Helin Zhang, Kate Azar, R\'eouven Assouly, Leon Ding, Michael Gingras, Hannah Stickler, Bethany M. Niedzielski, Mollie E. Schwartz, Terry P. Orlando, Joel \^I-j. Wang, Simon Gustavsson, Jeffrey A. Grover, Kyle Serniak, William D. Oliver

Temperature and Magnetic-Field Dependence of Energy Relaxation in a Fluxonium Qubit
Noise from material defects at device interfaces is known to limit the coherence of superconducting circuits, yet our understanding of the defect origins and noise mechanisms remains incomplete. Here we investigate the temperature and in-plane magnetic-field dependence of energy relaxation in a low-frequency fluxonium qubit, where the sensitivity to flux noise and charge noise arising from dielectric loss can be tuned by applied flux. We observe an approximately linear scaling of flux noise wit…

Analysis of Semantic Communication for Logic-based Hypothesis Deduction
Ahmet Faruk Saz, Siheng Xiong, Faramarz Fekri
https://arxiv.org/abs/2508.21755 https://

Analysis of Semantic Communication for Logic-based Hypothesis Deduction
This work presents an analysis of semantic communication in the context of First-Order Logic (FOL)-based deduction. Specifically, the receiver holds a set of hypotheses about the State of the World (SotW), while the transmitter has incomplete evidence about the true SotW but lacks access to the ground truth. The transmitter aims to communicate limited information to help the receiver identify the hypothesis most consistent with true SotW. We formulate the objective as approximating the posterio…

Twice in the past week, I’ve received an edit notification about a post I’d previously boosted where the edit corrected bad or incomplete information. In both cases, I boosted the correction.
I’ve long wished for social media misinformation policy to be “If you saw the bad info, you see the correction.” Mastodon comes closer to that than anything I’ve previously encountered.
There’s been a lot of theoretical hand-wringing about allowing edits on social media. In practice, this aspect at least seems like a huge success.

We had the pleasure of presenting at FIRST.org 2025, showcasing the Vulnerability-Lookup and GCVE.eu initiatives.
Slides are now available.
#cybersecurity #vulnerability #cve

DMGIN: How Multimodal LLMs Enhance Large Recommendation Models for Lifelong User Post-click Behaviors
Zhuoxing Wei, Qingchen Xie, Qi Liu
https://arxiv.org/abs/2508.21801 https:/…

DMGIN: How Multimodal LLMs Enhance Large Recommendation Models for Lifelong User Post-click Behaviors
Modeling user interest based on lifelong user behavior sequences is crucial for enhancing Click-Through Rate (CTR) prediction. However, long post-click behavior sequences themselves pose severe performance issues: the sheer volume of data leads to high computational costs and inefficiencies in model training and inference. Traditional methods address this by introducing two-stage approaches, but this compromises model effectiveness due to incomplete utilization of the full sequence context. Mor…

Model Predictive Adversarial Imitation Learning for Planning from Observation
Tyler Han, Yanda Bao, Bhaumik Mehta, Gabriel Guo, Anubhav Vishwakarma, Emily Kang, Sanghun Jung, Rosario Scalise, Jason Zhou, Bryan Xu, Byron Boots
https://arxiv.org/abs/2507.21533

Model Predictive Adversarial Imitation Learning for Planning from Observation
Human demonstration data is often ambiguous and incomplete, motivating imitation learning approaches that also exhibit reliable planning behavior. A common paradigm to perform planning-from-demonstration involves learning a reward function via Inverse Reinforcement Learning (IRL) then deploying this reward via Model Predictive Control (MPC). Towards unifying these methods, we derive a replacement of the policy in IRL with a planning-based agent. With connections to Adversarial Imitation Learnin…

The Incomplete Bridge: How AI Research (Mis)Engages with Psychology
Han Jiang, Pengda Wang, Xiaoyuan Yi, Xing Xie, Ziang Xiao
https://arxiv.org/abs/2507.22847 https://

The Incomplete Bridge: How AI Research (Mis)Engages with Psychology
Social sciences have accumulated a rich body of theories and methodologies for investigating the human mind and behaviors, while offering valuable insights into the design and understanding of Artificial Intelligence (AI) systems. Focusing on psychology as a prominent case, this study explores the interdisciplinary synergy between AI and the field by analyzing 1,006 LLM-related papers published in premier AI venues between 2023 and 2025, along with the 2,544 psychology publications they cite. T…

Bridging UI Design and chatbot Interactions: Applying Form-Based Principles to Conversational Agents
Sanjay Krishna Anbalagan, Xinrui Nie, Umesh Mohan, Vijay Kumar Kanamarlapudi, Anughna Kommalapati, Xiaodan Zhao
https://arxiv.org/abs/2507.01862

Bridging UI Design and chatbot Interactions: Applying Form-Based Principles to Conversational Agents
Domain specific chatbot applications often involve multi step interactions, such as refining search filters, selecting multiple items, or performing comparisons. Traditional graphical user interfaces (GUIs) handle these workflows by providing explicit "Submit" (commit data) and "Reset" (discard data) actions, allowing back-end systems to track user intent unambiguously. In contrast, conversational agents rely on subtle language cues, which can lead to confusion and incomplete context management…

Estimating Average Causal Effects with Incomplete Exposure and Confounders
Lan Wen, Glen McGee
https://arxiv.org/abs/2506.21786 https://

Estimating Average Causal Effects with Incomplete Exposure and Confounders
Standard methods for estimating average causal effects require complete observations of the exposure and confounders. In observational studies, however, missing data are ubiquitous. Motivated by a study on the effect of prescription opioids on mortality, we propose methods for estimating average causal effects when exposures and potential confounders may be missing. We consider missingness at random and additionally propose several specific missing not at random (MNAR) assumptions. Under our pr…

A new sparsity promoting residual transform operator for Lasso regression
Yao Xiao, Anne Gelb, Aditya Viswanathan
https://arxiv.org/abs/2506.22689 https://…

A new sparsity promoting residual transform operator for Lasso regression
Lasso regression is a widely employed approach within the $\ell_1$ regularization framework used to promote sparsity and recover piecewise smooth signals $f:[a,b) \rightarrow \mathbb{R}$ when the given observations are obtained from noisy, blurred, and/or incomplete data environments. In choosing the regularizing sparsity-promoting operator, it is assumed that the particular type of variability of the underlying signal, for example, piecewise constant or piecewise linear behavior across the ent…

A voice for minorities: diversity in approval-based committee elections under incomplete or inaccurate information
Feline Lindeboom, Martijn Brehm, Davide Grossi, Pradeep Murukannaiah
https://arxiv.org/abs/2506.10843

A voice for minorities: diversity in approval-based committee elections under incomplete or inaccurate information
We study diversity in approval-based committee elections with incomplete or inaccurate information. As standard in the literature on approval-based multi-winner voting, we define diversity according to the maximum coverage problem, which is known to be NP-complete, with a best attainable polynomial time approximation ratio of $1-1/\e$. In the incomplete information model, voters can vote on only a small portion of the candidates. We suggest a greedy algorithm and a local search algorithm that q…

Google releases a study saying a median Gemini text prompt uses 0.26mm of water and makes ~0.03g CO2; critics call it misleading for omitting indirect water use (Justine Calma/The Verge)
https://www.theverge.com/report/763080/google-ai-gemini-water-energy…

Google says a typical AI text prompt only uses 5 drops of water — experts say that’s misleading
Google estimates that a Gemini text prompt uses a minimal amount of water and energy, but experts say the study paints an incomplete picture of AI’s environmental toll.

Hybrid CNN-Mamba Enhancement Network for Robust Multimodal Sentiment Analysis
Xiang Li, Xianfu Cheng, Xiaoming Zhang, Zhoujun Li
https://arxiv.org/abs/2507.23444 https://…

Hybrid CNN-Mamba Enhancement Network for Robust Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) with missing modalities has recently attracted increasing attention. Although existing research mainly focuses on designing complex model architectures to handle incomplete data, it still faces significant challenges in effectively aligning and fusing multimodal information. In this paper, we propose a novel framework called the Hybrid CNN-Mamba Enhancement Network (HCMEN) for robust multimodal sentiment analysis under missing modality conditions. HCMEN is de…

Toddler moves towards an outreached hand in the first heat of the 100 meter race (FLRC runs from slow to fast so that the timers will know what they're doing when it counts)
#sports #photo #photography

Fun things to play on bass that aren't really basslines: _Clockwork_ from Castlevania 3. Had to get this written down so I could figure it out but it's friggin' blast...still working on speed though.
#bass #castlevania

Understanding the fill-factor limit of organic solar cells
Huotian Zhang, Jun Yuan, Tong Wang, Nurlan Tokmoldin, Rokas Jasiunas, Yiting Liu, Manasi Pranav, Yuxuan Li, Xiaolei Zhang, Vidmantas Gulbinas, Safa Shoaee, Yingping Zou, Veaceslav Coropceanu, Artem A. Bakulin, Dieter Neher, Thomas Kirchartz, Feng Gao
https://arxiv.org/abs/2507.2221…

Understanding the fill-factor limit of organic solar cells
Although the power conversion efficiencies of organic solar cells (OSCs) have surpassed 20%, they still lag behind commercial inorganic solar cells and emerging perovskite solar cells. To bridge this efficiency gap, improving the fill factor (FF) is critical, provided other photovoltaic parameters are not compromised. However, the fundamental understanding of the FF in OSCs remains incomplete. In this work, we systematically investigate a wide range of OSCs with the FF values spanning 0.27 to 0…

Through-the-Cycle PD Estimation Under Incomplete Data -- A Single Risk Factor Approach
Barbara D\"om\"ot\"or, Ferenc Ill\'es
https://arxiv.org/abs/2508.15651 …

Through-the-Cycle PD Estimation Under Incomplete Data -- A Single Risk Factor Approach
Banks are required to use long-term default probabilities (PDs) of their portfolios when calculating credit risk capital under internal ratings-based (IRB) models. However, the calibration models and historical data typically reflect prevailing market conditions. According to Basel recommendations, averaging annual PDs over a full economic cycle should yield the long-term PD. In practice, the available data are often temporally incomplete - even for high-risk portfolios. In this paper, we prese…

Resolving Indirect Calls in Binary Code via Cross-Reference Augmented Graph Neural Networks
Haotian Zhang, Kun Liu, Cristian Garces, Chenke Luo, Yu Lei, Jiang Ming
https://arxiv.org/abs/2507.18801

Resolving Indirect Calls in Binary Code via Cross-Reference Augmented Graph Neural Networks
Binary code analysis is essential in scenarios where source code is unavailable, with extensive applications across various security domains. However, accurately resolving indirect call targets remains a longstanding challenge in maintaining the integrity of static analysis in binary code. This difficulty arises because the operand of a call instruction (e.g., call rax) remains unknown until runtime, resulting in an incomplete inter-procedural control flow graph (CFG). Previous approaches have …

Fast Simulation of Damage Diffusion Distribution in Scanning Transmission Electron Microscopy
Amir Javadi Rad, Amirafshar Moshtaghpour, Dongdong Chen, Angus I. Kirkland
https://arxiv.org/abs/2507.00294

Fast Simulation of Damage Diffusion Distribution in Scanning Transmission Electron Microscopy
Scanning Transmission Electron Microscopy (STEM) is a critical tool for imaging the properties of materials and biological specimens at atomic scale, yet our understanding of relevant electron beam damage mechanisms is incomplete. Recent studies suggest that certain types of damage can be modelled as a diffusion process. However, numerical simulation of such diffusion processes has remained computationally intensive. This work introduces a high-performance C++ framework for simulating damage di…

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…

Scaled Beta Models and Feature Dilution for Dynamic Ticket Pricing
Jonathan R. Landers
https://arxiv.org/abs/2507.23767 https://arxiv.org/pdf/2507.23767

Scaled Beta Models and Feature Dilution for Dynamic Ticket Pricing
A novel approach is presented for identifying distinct signatures of performing acts in the secondary ticket resale market by analyzing dynamic pricing distributions. Using a newly curated, time series dataset from the SeatGeek API, we model ticket pricing distributions as scaled Beta distributions. This enables accurate parameter estimation from incomplete statistical data using a hybrid of quantile matching and the method of moments. Incorporating the estimated $α$ and $β$ parameters into R…

Learning from the past in an irreversible investment problem
Topias Tolonen-Weckstr\"om
https://arxiv.org/abs/2508.21731 https://arxiv.org/pdf/2508.21…

Learning from the past in an irreversible investment problem
We consider an irreversible investment problem under incomplete information, where the investor is able to exercise multiple investment rights to a project. The investor does not observe the project value directly and instead only a noisy observation process is observed. Upon each investment, the investor acquires previously hidden information from the project's past (''learning from the past''), and so the learning rate of the problem is controlled by investing. The acquisition of additional i…

Aerosol dynamics on hot exoplanets: the role of radiation pressure
James E. Owen, Ruth A. Murray-Clay
https://arxiv.org/abs/2508.20175 https://arxiv.org/pd…

Aerosol dynamics on hot exoplanets: the role of radiation pressure
Aerosols appear to be ubiquitous in exoplanetary atmospheres. However because our understanding of the physical processes that govern aerosols is incomplete, their presence makes the measurement of atmospheric properties, such as molecular abundance ratios, difficult. We show that aerosol particles in highly-irradiated exoplanets experience an additional acceleration due to stellar radiation pressure. The strength of this radiative acceleration often exceeds the planet's gravity and can approac…

An incomplete list of people excluded from 'international' conferences that are in-person only and aren't streamed or recorded
- People with caring responsibilities
- People with disabilities
- People with health vulnerabilities
- People who don't fly in recognition of climate emergencies
- People who work for orgs with ethical no-fly policies
- People who have their travel disrupted due to climate chaos
- People who only engage with fully o…

Semiparametric model averaging for high-dimensional quantile regression with nonignorable nonresponse
Wei Xiong, Dianliang Deng, Dehui Wang
https://arxiv.org/abs/2509.00464 http…

Semiparametric model averaging for high-dimensional quantile regression with nonignorable nonresponse
Model averaging has demonstrated superior performance for ensemble forecasting in high-dimensional framework, its extension to incomplete datasets remains a critical but underexplored challenge. Moreover, identifying the parsimonious model through averaging procedure in quantile regression demands urgent methodological innovation. In this paper, we propose a novel model averaging method for high-dimensional quantile regression with nonignorable missingness. The idea is to relax the parametric c…

I know there’s lots of upsides and downsides to cloud hosting, but I think my biggest problem with it is that it is all built on a mountain of leaky, incomplete, and dysfunctional abstractions.
Even Kubernetes is full of them, which is why we keep getting new layers on top (see KRO, for example).
And the various cloud providers are much worse.
“Just one more layer of abstraction, bro, that’ll fix it!”
The issue with abstractions is that when one is faulty, every layer…

MoE-Health: A Mixture of Experts Framework for Robust Multimodal Healthcare Prediction
Xiaoyang Wang, Christopher C. Yang
https://arxiv.org/abs/2508.21793 https://

MoE-Health: A Mixture of Experts Framework for Robust Multimodal Healthcare Prediction
Healthcare systems generate diverse multimodal data, including Electronic Health Records (EHR), clinical notes, and medical images. Effectively leveraging this data for clinical prediction is challenging, particularly as real-world samples often present with varied or incomplete modalities. Existing approaches typically require complete modality data or rely on manual selection strategies, limiting their applicability in real-world clinical settings where data availability varies across patient…

The Impact of Non-Orographic Gravity Waves on Transport and Mixing: Effects of Oblique Propagation and Coupling to Turbulence
Tridib Banerjee, Sebastian Borchert, Young-Ha Kim, Alena Kosareva, Daniel Kunkel, G\"okce T. Masur, Zuzana Proch\'azkov\'a, Juerg Schmidli, Georg Sebastian Voelker, Ulrich Achatz
https://arxiv.org/abs/2…

The Impact of Non-Orographic Gravity Waves on Transport and Mixing: Effects of Oblique Propagation and Coupling to Turbulence
Gravity waves (GWs) are a fundamental driver of circulation, tracer transport, and mixing in the middle and upper atmosphere, but their treatment in global circulation models remains incomplete. In particular, standard parameterizations typically restrict propagation to the vertical and treat GW-turbulence interactions in only a rudimentary manner, potentially leading to systematic biases in simulated dynamics and transport. Here, we use the Multi-Scale Gravity-Wave Model (MS-GWaM) implemented …

Retrieve-and-Verify: A Table Context Selection Framework for Accurate Column Annotations
Zhihao Ding, Yongkang Sun, Jieming Shi
https://arxiv.org/abs/2508.17203 https://

Retrieve-and-Verify: A Table Context Selection Framework for Accurate Column Annotations
Tables are a prevalent format for structured data, yet their metadata, such as semantic types and column relationships, is often incomplete or ambiguous. Column annotation tasks, including Column Type Annotation (CTA) and Column Property Annotation (CPA), address this by leveraging table context, which are critical for data management. Existing methods typically serialize all columns in a table into pretrained language models to incorporate context, but this coarse-grained approach often degrad…

Matter Spectral Functions from Quantum Gravity
Varun Kher, Brandon King, Daniel F. Litim, Manuel Reichert
https://arxiv.org/abs/2507.17862 https://arxiv.or…

Matter Spectral Functions from Quantum Gravity
We investigate Lorentzian quantum gravity coupled to a template matter sector with gauge fields, scalars and fermions. In the absence of quantised gravity, the matter sector by itself is renormalisable, but UV-incomplete. Provided quantum gravity offers an asymptotically safe UV-completion, we determine the photon and scalar two-point functions in the presence of gravitational fluctuations, and show that both possess a Källén-Lehmann spectral representation. Our results are achieved using fun…

Disformal interactions in the Dark Sector: From driving Early Dark Energy to confronting cosmological tensions
Pulkit Bansal (IIT Bombay), Joseph P. Johnson (IISER Mohali), S. Shankaranarayanan (IIT Bombay)
https://arxiv.org/abs/2508.17003

Disformal interactions in the Dark Sector: From driving Early Dark Energy to confronting cosmological tensions
The $Λ$CDM model faces significant challenges, including an incomplete understanding of the dark sector and persistent tensions in the Hubble constant and the clustering amplitude. To address these issues, we propose a general disformal coupling between dark energy (DE) and dark matter from a field-theoretic action which can generate a rich variety of interactions including conformal and pure-momentum coupling scenarios. Our analysis reveals that a pure disformal coupling naturally produces a …

Multi-Agent Coordination under Poisson Observations: A Global Game Approach
Marcos M. Vasconcelos, Behrouz Touri
https://arxiv.org/abs/2507.00424 https://

Multi-Agent Coordination under Poisson Observations: A Global Game Approach
We study a model of strategic coordination based on a class of games with incomplete information known as Global Games. Under the assumption of Poisson-distributed signals and a Gamma prior distribution on state of the system, we demonstrate the existence of a Bayesian Nash equilibrium within the class of threshold policies for utility functions that are linear in the agents' actions. Although computing the exact threshold that constitutes an equilibrium in a system with finitely many agents is…

Alternative statistical inference for the first normalized incomplete moment
Jiannan Lu, Peng Ding, Anqi Zhao
https://arxiv.org/abs/2508.17145 https://arxi…

Alternative statistical inference for the first normalized incomplete moment
This paper re-examines the first normalized incomplete moment, a well-established measure of inequality with wide applications in economic and social sciences. Despite the popularity of the measure itself, existing statistical inference appears to lag behind the needs of modern-age analytics. To fill this gap, we propose an alternative solution that is intuitive, computationally efficient, mathematically equivalent to the existing solutions for "standard" cases, and easily adaptable to "non-sta…

Latent Factorization of Tensors with Threshold Distance Weighted Loss for Traffic Data Estimation
Lei Yang
https://arxiv.org/abs/2506.22441 https://…

Latent Factorization of Tensors with Threshold Distance Weighted Loss for Traffic Data Estimation
Intelligent transportation systems (ITS) rely heavily on complete and high-quality spatiotemporal traffic data to achieve optimal performance. Nevertheless, in real-word traffic data collection processes, issues such as communication failures and sensor malfunctions often lead to incomplete or corrupted datasets, thereby posing significant challenges to the advancement of ITS. Among various methods for imputing missing spatiotemporal traffic data, the latent factorization of tensors (LFT) model…

SLICEMATE: Accurate and Scalable Static Program Slicing via LLM-Powered Agents
Jianming Chang, Jieke Shi, Yunbo Lyu, Xin Zhou, Lulu Wang, Zhou Yang, Bixin Li, David Lo
https://arxiv.org/abs/2507.18957 …

SLICEMATE: Accurate and Scalable Static Program Slicing via LLM-Powered Agents
Static program slicing, which extracts the executable portions of a program that affect the values at a specific location, supports many software analysis tasks such as debugging and security auditing. However, traditional slicing tools rely on computationally expensive reachability analysis over dependency graphs, which struggle to scale to large programs and often fail to handle code with incomplete syntax. Recently emerged learning-based methods, while more robust to such cases, still fall s…

Generating Satisfiable Benchmark Instances for Stable Roommates Problems with Optimization
Baturay Y{\i}lmaz, Esra Erdem
https://arxiv.org/abs/2507.20013 https://

Generating Satisfiable Benchmark Instances for Stable Roommates Problems with Optimization
While the existence of a stable matching for the stable roommates problem possibly with incomplete preference lists (SRI) can be decided in polynomial time, SRI problems with some fairness criteria are intractable. Egalitarian SRI that tries to maximize the total satisfaction of agents if a stable matching exists, is such a hard variant of SRI. For experimental evaluations of methods to solve these hard variants of SRI, several well-known algorithms have been used to randomly generate benchmark…

Analysis of User Experience Evaluation Methods for Deaf users: A Case Study on a mobile App
A. E. Fuentes-Cort\'azar, A. Rivera-Hern\'andez, J. R. Rojano-C\'aceres
https://arxiv.org/abs/2507.22455

Analysis of User Experience Evaluation Methods for Deaf users: A Case Study on a mobile App
User Experience (UX) evaluation methods that are commonly used with hearing users may not be functional or effective for Deaf users. This is because these methods are primarily designed for users with hearing abilities, which can create limitations in the interaction, perception, and understanding of the methods for Deaf individuals. Furthermore, traditional UX evaluation approaches often fail to address the unique accessibility needs of Deaf users, resulting in an incomplete or biased assessme…

I$^3$-MRec: Invariant Learning with Information Bottleneck for Incomplete Modality Recommendation
Huilin Chen, Miaomiao Cai, Fan Liu, Zhiyong Cheng, Richang Hong, Meng Wang
https://arxiv.org/abs/2508.04247

I$^3$-MRec: Invariant Learning with Information Bottleneck for Incomplete Modality Recommendation
Multimodal recommender systems (MRS) improve recommendation performance by integrating diverse semantic information from multiple modalities. However, the assumption of the availability of all modalities rarely holds in practice due to missing images, incomplete descriptions, or inconsistent user content. These challenges significantly degrade the robustness and generalization capabilities of current models. To address these challenges, we introduce a novel method called \textbf{I$^3$-MRec}, wh…

Robust Incomplete-Modality Alignment for Ophthalmic Disease Grading and Diagnosis via Labeled Optimal Transport
Qinkai Yu, Jianyang Xie, Yitian Zhao, Cheng Chen, Lijun Zhang, Liming Chen, Jun Cheng, Lu Liu, Yalin Zheng, Yanda Meng
https://arxiv.org/abs/2507.04999

Robust Incomplete-Modality Alignment for Ophthalmic Disease Grading and Diagnosis via Labeled Optimal Transport
Multimodal ophthalmic imaging-based diagnosis integrates color fundus image with optical coherence tomography (OCT) to provide a comprehensive view of ocular pathologies. However, the uneven global distribution of healthcare resources often results in real-world clinical scenarios encountering incomplete multimodal data, which significantly compromises diagnostic accuracy. Existing commonly used pipelines, such as modality imputation and distillation methods, face notable limitations: 1)Imputat…

WebGuard :Interpretable Malicious URL Detection via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT
Ye Tian, Zhang Yumin, Yifan Jia, Jianguo Sun, Yanbin Wang
https://arxiv.org/abs/2506.19356

WebGuard++:Interpretable Malicious URL Detection via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT
URL+HTML feature fusion shows promise for robust malicious URL detection, since attacker artifacts persist in DOM structures. However, prior work suffers from four critical shortcomings: (1) incomplete URL modeling, failing to jointly capture lexical patterns and semantic context; (2) HTML graph sparsity, where threat-indicative nodes (e.g., obfuscated scripts) are isolated amid benign content, causing signal dilution during graph aggregation; (3) unidirectional analysis, ignoring URL-HTML feat…

Dark Side of Modalities: Reinforced Multimodal Distillation for Multimodal Knowledge Graph Reasoning
Yu Zhao, Ying Zhang, Xuhui Sui, Baohang Zhou, Haoze Zhu, Jeff Z. Pan, Xiaojie Yuan
https://arxiv.org/abs/2507.20738

Dark Side of Modalities: Reinforced Multimodal Distillation for Multimodal Knowledge Graph Reasoning
The multimodal knowledge graph reasoning (MKGR) task aims to predict the missing facts in the incomplete MKGs by leveraging auxiliary images and descriptions of entities. Existing approaches are trained with single-target objectives, which neglect the probabilistic correlations of entity labels, especially in non-target entities. Moreover, previous studies incorporate all modalities statically or adaptively, overlooking the negative impacts of irrelevant or misleading information in the incompe…

Systematic bias in LISA ringdown analysis due to waveform inaccuracy
Lodovico Capuano, Massimo Vaglio, Rohit S. Chandramouli, Chantal L Pitte, Adrien Kuntz, Enrico Barausse
https://arxiv.org/abs/2506.21181

Systematic bias in LISA ringdown analysis due to waveform inaccuracy
Inaccurate modeling of gravitational-wave signals can introduce systematic biases in the inferred source parameters. As detector sensitivities improve and signals become louder, mitigating such waveform-induced systematics becomes increasingly important. In this work, we assess the systematic biases introduced by an incomplete description of the ringdown signal from massive black hole binaries in the LISA band. Specifically, we investigate the impact of mode truncation in the ringdown template.…

CS-SHRED: Enhancing SHRED for Robust Recovery of Spatiotemporal Dynamics
Romulo B. da Silva, C\'assio M. Oishi, Diego Passos, J. Nathan Kutz
https://arxiv.org/abs/2507.22303

CS-SHRED: Enhancing SHRED for Robust Recovery of Spatiotemporal Dynamics
We present $\textbf{CS-SHRED}$, a novel deep learning architecture that integrates Compressed Sensing (CS) into a Shallow Recurrent Decoder ($\textbf{SHRED}$) to reconstruct spatiotemporal dynamics from incomplete, compressed, or corrupted data. Our approach introduces two key innovations. First, by incorporating CS techniques into the $\textbf{SHRED}$ architecture, our method leverages a batch-based forward framework with $\ell_1$ regularization to robustly recover signals even in scenarios wi…

Penalized FCI for Causal Structure Learning in a Sparse DAG for Biomarker Discovery in Parkinson's Disease
Samhita Pal, Dhrubajyoti Ghosh, Shu Yang
https://arxiv.org/abs/2507.00173

Penalized FCI for Causal Structure Learning in a Sparse DAG for Biomarker Discovery in Parkinson's Disease
Parkinson's disease (PD) is a progressive neurodegenerative disorder that lacks reliable early-stage biomarkers for diagnosis, prognosis, and therapeutic monitoring. While cerebrospinal fluid (CSF) biomarkers, such as alpha-synuclein seed amplification assays (alphaSyn-SAA), offer diagnostic potential, their clinical utility is limited by invasiveness and incomplete specificity. Plasma biomarkers provide a minimally invasive alternative, but their mechanistic role in PD remains unclear. A major…

DiagR1: A Vision-Language Model Trained via Reinforcement Learning for Digestive Pathology Diagnosis
Minxi Ouyang, Lianghui Zhu, Yaqing Bao, Qiang Huang, Jingli Ouyang, Tian Guan, Xitong Ling, Jiawen Li, Song Duan, Wenbin Dai, Li Zheng, Xuemei Zhang, Yonghong He
https://arxiv.org/abs/2507.18433

DiagR1: A Vision-Language Model Trained via Reinforcement Learning for Digestive Pathology Diagnosis
Multimodal large models have shown great potential in automating pathology image analysis. However, current multimodal models for gastrointestinal pathology are constrained by both data quality and reasoning transparency: pervasive noise and incomplete annotations in public datasets predispose vision language models to factual hallucinations when generating diagnostic text, while the absence of explicit intermediate reasoning chains renders the outputs difficult to audit and thus less trustwort…

Hardware Implementation of a Zero-Prior-Knowledge Approach to Lifelong Learning in Kinematic Control of Tendon-Driven Quadrupeds
Hesam Azadjou, Suraj Chakravarthi Raja, Ali Marjaninejad, Francisco J. Valero-Cuevas
https://arxiv.org/abs/2508.15160

Hardware Implementation of a Zero-Prior-Knowledge Approach to Lifelong Learning in Kinematic Control of Tendon-Driven Quadrupeds
Like mammals, robots must rapidly learn to control their bodies and interact with their environment despite incomplete knowledge of their body structure and surroundings. They must also adapt to continuous changes in both. This work presents a bio-inspired learning algorithm, General-to-Particular (G2P), applied to a tendon-driven quadruped robotic system developed and fabricated in-house. Our quadruped robot undergoes an initial five-minute phase of generalized motor babbling, followed by 15 r…

Portfolio optimization in incomplete markets and price constraints determined by maximum entropy in the mean
Argimiro Arratia, Henryk Gzyl
https://arxiv.org/abs/2507.07053

Portfolio optimization in incomplete markets and price constraints determined by maximum entropy in the mean
A solution to a portfolio optimization problem is always conditioned by constraints on the initial capital and the price of the available market assets. If a risk neutral measure is known, then the price of each asset is the discounted expected value of the asset's price under this measure. But if the market is incomplete, the risk neutral measure is not unique, and there is a range of possible prices for each asset, which can be identified with bid-ask ranges. We present in this paper an effec…

TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understanding
Junwen Zhang, Pu Chen, Yin Zhang
https://arxiv.org/abs/2506.21393

TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understanding
Multimodal understanding of tables in real-world contexts is challenging due to the complexity of structure, symbolic density, and visual degradation (blur, skew, watermarking, incomplete structures or fonts, multi-span or hierarchically nested layouts). Existing multimodal large language models (MLLMs) struggle with such WildStruct conditions, resulting in limited performance and poor generalization. To address these challenges, we propose TableMoE, a neuro-symbolic Mixture-of-Connector-Expert…

DeepMEL: A Multi-Agent Collaboration Framework for Multimodal Entity Linking
Fang Wang, Tianwei Yan, Zonghao Yang, Minghao Hu, Jun Zhang, Zhunchen Luo, Xiaoying Bai
https://arxiv.org/abs/2508.15876

DeepMEL: A Multi-Agent Collaboration Framework for Multimodal Entity Linking
Multimodal Entity Linking (MEL) aims to associate textual and visual mentions with entities in a multimodal knowledge graph. Despite its importance, current methods face challenges such as incomplete contextual information, coarse cross-modal fusion, and the difficulty of jointly large language models (LLMs) and large visual models (LVMs). To address these issues, we propose DeepMEL, a novel framework based on multi-agent collaborative reasoning, which achieves efficient alignment and disambigu…

Joint space-time wind field data extrapolation and uncertainty quantification using nonparametric Bayesian dictionary learning
George D. Pasparakis, Ioannis A. Kougioumtzoglou, Michael D. Shields
https://arxiv.org/abs/2507.11385

Joint space-time wind field data extrapolation and uncertainty quantification using nonparametric Bayesian dictionary learning
A methodology is developed, based on nonparametric Bayesian dictionary learning, for joint space-time wind field data extrapolation and estimation of related statistics by relying on limited/incomplete measurements. Specifically, utilizing sparse/incomplete measured data, a time-dependent optimization problem is formulated for determining the expansion coefficients of an associated low-dimensional representation of the stochastic wind field. Compared to an alternative, standard, compressive sam…

ArgusCogito: Chain-of-Thought for Cross-Modal Synergy and Omnidirectional Reasoning in Camouflaged Object Segmentation
Jianwen Tan, Huiyao Zhang, Rui Xiong, Han Zhou, Hongfei Wang, Ye Li
https://arxiv.org/abs/2508.18050

ArgusCogito: Chain-of-Thought for Cross-Modal Synergy and Omnidirectional Reasoning in Camouflaged Object Segmentation
Camouflaged Object Segmentation (COS) poses a significant challenge due to the intrinsic high similarity between targets and backgrounds, demanding models capable of profound holistic understanding beyond superficial cues. Prevailing methods, often limited by shallow feature representation, inadequate reasoning mechanisms, and weak cross-modal integration, struggle to achieve this depth of cognition, resulting in prevalent issues like incomplete target separation and imprecise segmentation. Ins…

Time and covariance smoothing for restoration of bivariate signals
Yusuf Yigit Pilavci (CRIStAL), Pierre Palud (LERMA), Julien Flamant (CRAN), Pierre-Antoine Thouvenin (CRIStAL), J\'er\'emie Boulanger (CRIStAL), Pierre Chainais (CRIStAL)
https://arxiv.org/abs/2506.20237

Time and covariance smoothing for restoration of bivariate signals
In many applications and physical phenomena, bivariate signals are polarized, i.e. they trace an elliptical trajectory over time when viewed in the 2D planes of their two components. The smooth evolution of this elliptical trajectory, called polarization ellipse, is highly informative to solve ill-posed inverse problems involving bivariate signals where the signal is collected through indirect, noisy or incomplete measurements. This work proposes a novel formulation and an efficient algorithm f…

Quantum Dark Magic: Efficiency of Intermediate Non-Stabiliserness
Tom Kr\"uger, Wolfgang Mauerer
https://arxiv.org/abs/2507.16543 https://

Quantum Dark Magic: Efficiency of Intermediate Non-Stabiliserness
While superiority of quantum over classical computation has been established, the repertoire of primitives with proven or conjectured quantum advantage remains limited. Despite considerable progress in delineating the quantumclassical divide, the systematic construction of algorithms with quantum advantage remains challenging, which can be attributed to a still incomplete understanding of the sources of quantum computational power. While intermediate non-stabiliserness (i.e., traversal of state…

FaultLine: Automated Proof-of-Vulnerability Generation Using LLM Agents
Vikram Nitin, Baishakhi Ray, Roshanak Zilouchian Moghaddam
https://arxiv.org/abs/2507.15241

FaultLine: Automated Proof-of-Vulnerability Generation Using LLM Agents
Despite the critical threat posed by software security vulnerabilities, reports are often incomplete, lacking the proof-of-vulnerability (PoV) tests needed to validate fixes and prevent regressions. These tests are crucial not only for ensuring patches work, but also for helping developers understand how vulnerabilities can be exploited. Generating PoV tests is a challenging problem, requiring reasoning about the flow of control and data through deeply nested levels of a program. We present F…

When Diffusion Models Memorize: Inductive Biases in Probability Flow of Minimum-Norm Shallow Neural Nets
Chen Zeno, Hila Manor, Greg Ongie, Nir Weinberger, Tomer Michaeli, Daniel Soudry
https://arxiv.org/abs/2506.19031

When Diffusion Models Memorize: Inductive Biases in Probability Flow of Minimum-Norm Shallow Neural Nets
While diffusion models generate high-quality images via probability flow, the theoretical understanding of this process remains incomplete. A key question is when probability flow converges to training samples or more general points on the data manifold. We analyze this by studying the probability flow of shallow ReLU neural network denoisers trained with minimal $\ell^2$ norm. For intuition, we introduce a simpler score flow and show that for orthogonal datasets, both flows follow similar traj…

What Breaks Knowledge Graph based RAG? Empirical Insights into Reasoning under Incomplete Knowledge
Dongzhuoran Zhou, Yuqicheng Zhu, Xiaxia Wang, Hongkuan Zhou, Yuan He, Jiaoyan Chen, Evgeny Kharlamov, Steffen Staab
https://arxiv.org/abs/2508.08344

What Breaks Knowledge Graph based RAG? Empirical Insights into Reasoning under Incomplete Knowledge
Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) is an increasingly explored approach for combining the reasoning capabilities of large language models with the structured evidence of knowledge graphs. However, current evaluation practices fall short: existing benchmarks often include questions that can be directly answered using existing triples in KG, making it unclear whether models perform reasoning or simply retrieve answers directly. Moreover, inconsistent evaluation metrics …

ASLSL: Adaptive shared latent structure learning with incomplete multi-modal physiological data for multi-dimensional emotional feature selection
Xueyuan Xu, Tianze Yu, Wenjia Dong, Fulin Wei, Li Zhuo
https://arxiv.org/abs/2508.05934

ASLSL: Adaptive shared latent structure learning with incomplete multi-modal physiological data for multi-dimensional emotional feature selection
Recently, multi-modal physiological signals based emotion recognition has garnered increasing attention in the field of brain-computer interfaces. Nevertheness, the associated multi-modal physiological features are often high-dimensional and inevitably include irrelevant, redundant, and noisy representation, which can easily lead to overfitting, poor performance, and high computational complexity in emotion classifiers. Feature selection has been widely applied to address these challenges. Howe…

A new algorithm for sampling parameters in a structured correlation matrix with application to estimating optimal combinations of muscles to quantify progression in Duchenne muscular dystrophy
Michael K. Kim, Michael J. Daniels, William D. Rooney, Rebecca J. Willcocks, Glenn A. Walter, Krista H. Vandenborne
https://arxiv.org/abs…

A new algorithm for sampling parameters in a structured correlation matrix with application to estimating optimal combinations of muscles to quantify progression in Duchenne muscular dystrophy
The goal of this paper is to estimate an optimal combination of biomarkers for individuals with Duchenne muscular dystrophy (DMD), which provides the most sensitive combinations of biomarkers to assess disease progression (in this case, optimal with respect to standardized response mean (SRM) for 4 muscle biomarkers). The biomarker data is an incomplete (missing and irregular) multivariate longitudinal data. We propose a normal model with structured covariance designed for our setting. To sampl…

4D Virtual Imaging Platform for Dynamic Joint Assessment via Uni-Plane X-ray and 2D-3D Registration
Hao Tang, Rongxi Yi, Lei Li, Kaiyi Cao, Jiapeng Zhao, Yihan Xiao, Minghai Shi, Peng Yuan, Yan Xi, Hui Tang, Wei Li, Zhan Wu, Yixin Zhou
https://arxiv.org/abs/2508.16138

4D Virtual Imaging Platform for Dynamic Joint Assessment via Uni-Plane X-ray and 2D-3D Registration
Conventional computed tomography (CT) lacks the ability to capture dynamic, weight-bearing joint motion. Functional evaluation, particularly after surgical intervention, requires four-dimensional (4D) imaging, but current methods are limited by excessive radiation exposure or incomplete spatial information from 2D techniques. We propose an integrated 4D joint analysis platform that combines: (1) a dual robotic arm cone-beam CT (CBCT) system with a programmable, gantry-free trajectory optimized …

KG-EDAS: A Meta-Metric Framework for Evaluating Knowledge Graph Completion Models
Haji Gul, Abul Ghani Naim, Ajaz Ahmad Bhat
https://arxiv.org/abs/2508.15357 https://

KG-EDAS: A Meta-Metric Framework for Evaluating Knowledge Graph Completion Models
Knowledge Graphs (KGs) enable applications in various domains such as semantic search, recommendation systems, and natural language processing. KGs are often incomplete, missing entities and relations, an issue addressed by Knowledge Graph Completion (KGC) methods that predict missing elements. Different evaluation metrics, such as Mean Reciprocal Rank (MRR), Mean Rank (MR), and Hit@k, are commonly used to assess the performance of such KGC models. A major challenge in evaluating KGC models, ho…

Representation Learning of Auxiliary Concepts for Improved Student Modeling and Exercise Recommendation
Yahya Badran, Christine Preisach
https://arxiv.org/abs/2508.16269 https:/…

Representation Learning of Auxiliary Concepts for Improved Student Modeling and Exercise Recommendation
Personalized recommendation is a key feature of intelligent tutoring systems, typically relying on accurate models of student knowledge. Knowledge Tracing (KT) models enable this by estimating a student's mastery based on their historical interactions. Many KT models rely on human-annotated knowledge concepts (KCs), which tag each exercise with one or more skills or concepts believed to be necessary for solving it. However, these KCs can be incomplete, error-prone, or overly general. In this …

ADSEL: Adaptive dual self-expression learning for EEG feature selection via incomplete multi-dimensional emotional tagging
Tianze Yu, Junming Zhang, Wenjia Dong, Xueyuan Xu, Li Zhuo
https://arxiv.org/abs/2508.05229

ADSEL: Adaptive dual self-expression learning for EEG feature selection via incomplete multi-dimensional emotional tagging
EEG based multi-dimension emotion recognition has attracted substantial research interest in human computer interfaces. However, the high dimensionality of EEG features, coupled with limited sample sizes, frequently leads to classifier overfitting and high computational complexity. Feature selection constitutes a critical strategy for mitigating these challenges. Most existing EEG feature selection methods assume complete multi-dimensional emotion labels. In practice, open acquisition environme…

Clustering data with values missing at random using scale mixtures of multivariate skew-normal distributions
Jason Pillay, Cristina Tortora, Antonio Punzo, Andriette Bekker
https://arxiv.org/abs/2507.20329

Clustering data with values missing at random using scale mixtures of multivariate skew-normal distributions
Handling missing data is a major challenge in model-based clustering, especially when the data exhibit skewness and heavy tails. We address this by extending the finite mixture of scale mixtures of multivariate skew-normal (FMSMSN) family to accommodate incomplete data under a missing at random (MAR) mechanism. Unlike previous work that is limited to one of the special cases of the FMSMSN family, our method offers a cluster analysis methodology for the entire family that accounts for skewness a…

Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints
Daniel Daza, Alberto Bernardi, Luca Costabello, Christophe Gueret, Masoud Mansoury, Michael Cochez, Martijn Schut
https://arxiv.org/abs/2508.13663

Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints
Methods for query answering over incomplete knowledge graphs retrieve entities that are likely to be answers, which is particularly useful when such answers cannot be reached by direct graph traversal due to missing edges. However, existing approaches have focused on queries formalized using first-order-logic. In practice, many real-world queries involve constraints that are inherently vague or context-dependent, such as preferences for attributes or related categories. Addressing this gap, we …

MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model
Yu Li, Zulong Chen, Wenjian Xu, Hong Wen, Yipeng Yu, Man Lung Yiu, Yuyu Yin
https://arxiv.org/abs/2508.13676

MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model
To maintain the company's talent pool, recruiters need to continuously search for resumes from third-party websites (e.g., LinkedIn, Indeed). However, fetched resumes are often incomplete and inaccurate. To improve the quality of third-party resumes and enrich the company's talent pool, it is essential to conduct duplication detection between the fetched resumes and those already in the company's talent pool. Such duplication detection is challenging due to the semantic complexity, structural h…