Computer Vision
AquaCubeAI-Powered Monitoring Turbidity on-board {\Phi}sat-2
Pietro Di Stasio, Francesca Razzano, Elisa Liparulo et al. (+6 more)
Timely monitoring of coastal water quality is critical for environmental protection, yet conventional satellite workflows rely on downlink and ground processing, introducing latency that can limit responsiveness to rapidly evolving turbidity events. To address this limitation, we propose AquaCubeAI, a lightweight machine-learning approach for onboard estimation of coastal water turbidity from {\Phi}sat-2 multispectral imagery. By shifting inference from the ground segment to the satellite, AquaCubeAI aims to enable lower-latency, more responsive, and more operationally useful turbidity monitoring under the strict compute and bandwidth constraints of spaceborne platforms. The model is trained on simulated {\Phi}sat-2 acquisitions spatially aligned with Copernicus Marine Service (CMEMS) High-Resolution Ocean Color (HR-OC) turbidity products over selected localized coastal sites spanning four European marine macro-regions. To provide a realistic evaluation of generalization in the presence of spatial correlation, we adopt a spatial block splitting protocol that mitigates data leakage between training and evaluation subsets. The main contributions of this work are: (i) a scalable
dataset generation pipeline pairing simulated {\Phi}sat-2 multispectral patches with CMEMS HR-OC turbidity labels across selected localized European coastal sites; (ii) a compact Multi-Layer Perceptron (MLP)-based turbidity regressor trained under a leakage-aware geospatial split and tailored to embedded constraints; and (iii) a reformulation for dense spatial prediction via parameter sharing, enabling turbidity mapping and simple threshold-based anomaly masks for onboard decision logic. Embedded deployment on an Intel Myriad
Vision Processing Unit (VPU) further confirms the feasibility of low-power hardware and supports low-latency inference from multispectral inputs.
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All authors Pietro Di Stasio, Francesca Razzano, Elisa Liparulo, Gabriele Meoni, Nicolas Longépé, Deodato Tapete, Paolo Gamba, Gilda Schirinzi, Silvia Liberata Ullo
Artificial Intelligence
Skill Issue: Lessons from Optimizing Repository SKILLs for Coding Agents
Mykhailo Kozyrev, Andrei Kozyrev and Anton Podkopaev
Coding
agents increasingly read repository knowledge from SKILLs — plain .md files versioned alongside the code. Recent work synthesizes these files automatically, by optimizing the document against a
benchmark. A bare repository comes with no
benchmark, and the synthetic tasks prior work builds are small enough that a capable
agent saturates them with no document at all. We mine harder tasks — merged pull requests of the repository, reverted at a single frozen base commit; and score a candidate document by whether the same
agent does better with it than without it. On three Kotlin repositories, the documents GEPA finds raise this score by $4.9$pp on average, and the ones SkillOpt finds leave it where it started, $0.1$pp above the seed. The GEPA gain matches what prior work reports with the same optimizer, and at the
dataset size a single repository supplies it cannot be separated from the
agent's run-to-run variance; settling that would take more tasks than one repository's history yields. The documents themselves read better than the score: a maintainer of one repository found in them knowledge one only gets by working in the project.
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Robotics
Control Architecture for Safe Grasping of Fragile Objects Using a Coarse Position-Controlled Gripper
Marko Pavlic, Moritz Geier, Timo Markert et al. (+1 more)
Robots are increasingly used in unstructured environments. The need for them to safely grasp unknown objects without damaging them becomes crucial. Humans achieve this by sensing and quickly responding by adjusting their grasping force. Similarly, effective grasp acquisition in
robots requires compliant interaction strategies that can adapt to uncertain object properties and adjust to any instabilities during manipulation. We present a geometry-aware force/torque-based contact estimation method for a coarse position-controlled gripper, combined with an adaptive admittance controller for safe grasp acquisition. The desired contact forces are estimated online to keep stable contact with objects of unknown properties. This enables compliant and stable grasps while avoiding excessive forces. Experiments with objects of different sizes, shapes, stiffnesses, and weights show that the proposed
algorithm not only prevents slippage but also applies minimal force to safely grasp an object without causing excessive deformation.
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All authors Marko Pavlic, Moritz Geier, Timo Markert, Darius Burschka
Machine Learning
Physics-Guided Synthetic High-Frequency Ultrasound Generation for Skin Layer Segmentation
Junkyung ju, Kyungho Yoon and Minwoo Shin
High-frequency ultrasound (HFUS) enables noninvasive visualization of superficial skin structures, but automated skin-layer analysis is limited by the scarcity of densely annotated data. Existing real HFUS
datasets commonly provide annotations for superficial targets such as the epidermis and subepidermal low-echogenic band (SLEB), while dense labels for deeper structures such as dermis, subcutaneous tissue, fascia, and muscle are rarely available. We propose a physics-guided synthetic HFUS generation framework for skin layer
segmentation. The framework constructs multilayer acoustic skin phantoms, assigns layer dependent acoustic properties, and uses k-Wave simulation to generate paired synthetic HFUS images, dense layer masks, and simulation metadata. To evaluate whether the generated data provide transferable supervision, we use it for downstream
segmentation pretraining and fine-tune the models on real Mendeley HFUS data. Synthetic pretraining followed by real fine-tuning achieved real-domain performance comparable to real-only training and improved mean Dice/IoU in three of four evaluated trainable architectures. These results suggest that physics-guided synthetic HFUS images contain transferable anatomical and textural cues for real-domain skin layer
segmentation, although further reduction of the synthetic-real appearance gap is needed to enable greater gains. The code and data are available at: https://github.com/Finn-02/synthetic-hfus-skin-layer-
segmentation.
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Computer Vision
GRACE: Adaptive Concept Erasure with Geometry-Guided Retention in Diffusion Models
Qinghui Gong, Yihuai Liang, Jinshan Lai et al. (+3 more)
Text-to-image (T2I)
diffusion models inevitably internalize sensitive or non-compliant concepts from large-scale pretraining data, necessitating post-hoc concept erasure. However, existing erasure methods often lack explicit constraints on parameter updates, leading to over-intervention and unintended semantic drift. In addition, many methods rely on manually crafted counterfactual supervision, such as surrogate
prompts, which incurs substantial data construction costs that limit scalability to new concepts. To address these limitations, we propose GRACE, a structured concept erasure framework designed to enable localized and selective intervention. Specifically, we introduce a semantically weighted sensitive subspace estimation to precisely lock intervention directions, and employ lightweight subspace-constrained adapters to prevent global semantic disturbance. To eliminate the dependency on manual
prompt engineering, we design an automatically decoupled safe-anchor mechanism. To mitigate semantic drift induced by excessive intervention, we introduce an energy-driven dynamic gating mechanism that adaptively controls the timing and strength of intervention at inference. Extensive experiments demonstrate that our method achieves a superior balance between erasure effectiveness and generation fidelity. Compared with the average performance of five state-of-the-art (SOTA) concept erasure methods, our method improves the fine-grained NSFW reduction rate by $17.86\%$, while reducing the macro-averaged target CLIP Score and preservation-oriented Fréchet Inception Distance (FID) by $4.55\%$ and $52.14\%$, indicating stronger concept suppression with substantially improved preservation of the original model's generative utility.
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All authors Qinghui Gong, Yihuai Liang, Jinshan Lai, Deepak Kumar Jain, Vitomir Štruc, Zhengchun Zhou
Cryptography and Security
Fresh-Challenge VDF Attestations for Model-Relative Response Latency
Ansar Yesmukhanov and Aruzhan Tlessova
Can a finite verifier obtain public, model-relative evidence about response latency for sequential computation? Verifiable delay functions (VDFs) make this possible in principle: evaluation requires T sequential steps, whereas verification is efficient in the security parameter and polylogarithmic in the numerical value of T for standard constructions. Thus a delay can be astronomically large to evaluate yet succinctly represented and feasibly checked. A VDF proof for a chosen message alone is insufficient because it may be precomputed. We specify and analyze Fresh-Challenge VDF Attestations (FCLA), a protocol composition that binds a VDF to an unpredictable public challenge, a message, and independently auditable release and receipt records. Under explicit assumptions about VDF sequentiality, the challenge source, witness logs, and a calibrated upper bound on an adversary's sequential evaluation rate, an accepted FCLA transcript is inconsistent with post-challenge generation by an adversary in that bounded model. The result neither identifies a named claimant nor excludes relaying, outsourcing, or a faster unmodeled machine. A
benchmark of the public reference implementation confirms the expected empirical separation between evaluation and verification on one documented machine. Our contribution is a protocol/design analysis and benchmarked reference implementation layer, not a new VDF construction or cryptographic primitive.
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Machine Learning · Cross-listed from eess.SP
Prism-SQA: An Interpretable and Adaptable Neural Framework for Surface Electromyography Quality Assessment
Kuan-Chen Wang, Kai-Chun Liu, Ping-Cheng Yeh et al. (+2 more)
sEMG is vulnerable to various contaminants that distort signal morphology and spectral content. Accurate signal quality assessment (SQA) is essential for identifying such degradation and ensuring reliable clinical analyses and decisions. Recent neural network-based SQA methods achieve accurate quality estimation by learning complex contamination patterns, yet their black-box nature prevents clinicians from understanding or validating the reported scores and limits adaptability to application-specific quality definitions without retraining. To address these limitations, we propose Prism-SQA, an interpretable and adaptable neural framework that reformulates SQA as a physiology-aware source-separation and verification process. Prism-SQA decomposes each input signal into a clean sEMG component and five contaminant-specific components using a U-Net with bidirectional long short-term
memory. Each separated contaminant component is examined by a Contaminant Fingerprint Verifier, which enforces physiological plausibility by comparing its temporal and spectral structure with canonical contaminant signatures. This design allows clinicians to inspect how each contaminant affects signal quality, grounding the assessment in transparent, signal-level evidence rather than opaque latent representations. Quality indices computed from the verified components further enable customization of quality criteria across clinical contexts without retraining. We evaluate Prism-SQA on continuous quality-score estimation using synthesized noisy sEMG from public Ninapro
datasets and on binary quality classification using a clinical dysphagia
dataset. Results show that Prism-SQA achieves competitive or better performance than contemporary black-box neural methods while providing explicit interpretability and adaptability, advancing toward practical and clinically aligned sEMG SQA.
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All authors Kuan-Chen Wang, Kai-Chun Liu, Ping-Cheng Yeh, Sheng-Yu Peng, Yu Tsao
Computer Vision
Multimodal Floorplan Encoding: Learning Dense Modality-Invariant Representations
Xavier Anadón, Rémi Pautrat and Rui Wang
Floorplans arise in many forms, from vector CAD drawings to raster renderings and sensor-derived density maps. This heterogeneity makes it difficult to build learning systems that transfer across modalities and support geometry-centric tasks such as
alignment and
retrieval. We introduce the
Multimodal Floorplan Encoder (MMFE), which maps diverse 2D indoor representations into a shared dense latent grid. MMFE combines a frozen DINOv3 backbone with a trainable Dense Prediction
Transformer (DPT) head, and is trained with a per-
cell Information Noise-Contrastive Estimation (InfoNCE) objective that aligns spatially corresponding regions across modalities while using all other
cells as negatives. To improve robustness to
geometric distortions, we incorporate controlled similarity transformations and enforce
geometric consistency through feature-grid warping. On Structured3D, a held-out out-of-domain
dataset, MMFE improves cross-modal dense matching, enables robust similarity
alignment with RANSAC, and yields strong
retrieval when paired with learned aggregation.
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Robotics
Improving Imitation Learning Efficiency for Manipulation through Geometric Prior Pretraining
Shogo Iwakata, Tomohiro Motoda, Ryosuke Yamada et al. (+8 more)
Applying an imitation learning policy to a new manipulation task usually requires collecting new demonstrations and retraining the model, which makes sample efficiency a practical concern. Pretraining on large-scale
robot datasets is effective in this respect, but such
datasets are costly to collect and train on, while data augmentation techniques typically require a new round of data generation and retraining for each task. A complementary question is what useful prior can be provided to a policy at negligible cost before any task-specific data are collected. In this study, we construct a
geometric visual pretraining
dataset in which each scene contains only a plane, an object, and a hand, and trajectories are generated automatically. The scenes contain neither textures nor backgrounds; pretraining primarily exposes the policy to the
geometric relationship between the hand and the object. Furthermore, representing the hand as a cube avoids tailoring the
dataset to a specific
robot morphology. We evaluate this
geometric prior using ACT on three simulated
robots across five manipulation tasks each, as well as on three real-world
robot tasks. Across many of these
robot–task combinations, fine-tuning from the
geometric prior achieves higher success rates in the early stages of training than training from scratch while using only a small number of task demonstrations. These results suggest that even highly simplified
geometric scenes can provide a useful initialization that transfers across
robots and to real-world tasks when task data are limited.
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All authors Shogo Iwakata, Tomohiro Motoda, Ryosuke Yamada, Koshi Makihara, Ryoichi Nakajo, Keitaro Tanaka, Masaki Murooka, Roman Mykhailyshyn, Hirokatsu Kataoka, Shigeo Morishima, Yukiyasu Domae
Artificial Intelligence
When Rubrics Fail: Hallucinations Reveal Blind Spots in Medical AI Evaluation
Griffin Farrow, Lily Sijia Li, Jack Johnson et al. (+4 more)
Hallucinations can undermine clinician trust in LLMs, making it important that evaluation methods capture clinically relevant errors. Rubric-based evaluation has become the leading approach for assessing LLMs in medicine, but it is unclear whether rubric scores reflect such errors. We first study this in a controlled setting using MedHallu, finding that more specific rubrics better distinguish correct from hallucinated responses. To test this systematically, we develop a taxonomy of medical
hallucination types and a clinician-validated error-injection pipeline that creates matched correct and error-injected responses. Across HealthBench, HealthBench Professional, and LiveMedBench, our clinically relevant
hallucinations are missed by rubrics, often leaving scores unchanged. We find that rubrics are most effective when explicitly checking facts, and are less effective for additional or unexpected errors they do not anticipate. A preliminary
retrieval-based factuality check recovers some of the rubric-blind errors, suggesting a complementary approach. These findings reveal systematic blind spots in current medical evaluation of LLMs and suggest that rubric scores alone are insufficient to establish clinical reliability, potentially undermining clinician trust and confidence in clinical deployment.
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All authors Griffin Farrow, Lily Sijia Li, Jack Johnson, Tingyan Wang, Philip Torr, William Bolton, Fabio J. Fehr
Logic in Computer Science
Supermartingale Certificates for Parametric MDPs
Kaushik Mallik and {\DH}or{\dj}e Žikelić
We consider the problems of formal verification and synthesis in parametric Markov decision processes (MDPs) with general measurable state and action spaces. The heart of our approach is a parameter flattening transformation, which allows us to transform parametric MDPs into semantically equivalent non-parametric MDPs. Building on this transformation, we introduce the novel notion of parametric supermartingale certificates, which generalize the traditional supermartingale certificates—used for non-parametric MDPs—to the parametric setting. We use our parametric supermartingale certificates to design
algorithms for verification and approximate synthesis in
polynomial arithmetic parametric MDPs. This leads to the first verification and synthesis
algorithms for parametric MDPs with general state and action spaces. We implement our
algorithms and experimentally evaluate them on several continuous parametric random walk
benchmarks.
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Machine Learning · Cross-listed from cond-mat.mtrl-sci
Inferring Dislocation Microstructures from X-ray Diffraction via Cross-Modal Contrastive Learning
Benjamin Udofia, Nicolas Bertin and Markus Stricker
Understanding and inferring dislocation microstructures from diffraction patterns remains an open challenge in materials characterization, as diffraction measurements provide only indirect information about the underlying dislocation structure. In this work, a cross-modal learning framework is developed to enable the prediction of 3D dislocation structures directly from diffraction data. Dislocation density fields generated from discrete dislocation dynamics simulations are paired with corresponding virtual X-ray diffraction patterns and embedded into a shared 2D latent space using contrastive learning. The
alignment between structural and diffraction representations of dislocation structures is evaluated directly in the learned latent space using correlations between corresponding latent features. To estimate the role of
dataset size for this approach, farthest point sampling is employed to construct representative and diverse training subsets of varying sizes. The results show strong cross-modal
alignment and that model performance improves rapidly with increasing
dataset size. Near-saturation is achieved with approximately 500 representative observations from a
dataset of 10,000 observations, enabling accurate prediction of dislocation density fields from previously unseen diffraction data of the same distribution. Qualitative comparisons confirm that the predicted structures capture the dominant spatial features of the underlying dislocation microstructures. These findings demonstrate an efficient approach for learning structure-diffraction relationships and highlight the potential for inferring structural characteristics of dislocation networks directly from diffraction patterns, providing a pathway toward diffraction-based structural analysis and future extension to experimental data.
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Machine Learning
InRTL: Effective Intra-Inter Interaction Learning for Relational Tables
Weichen Li, Ken Zhong, Zheng Wang et al. (+2 more)
Relational table learning has recently emerged as an important research direction for modeling multiple tables connected through primary key-foreign key (PK-FK) relationships. Despite recent advances, a principled modeling framework tailored to this task remains underexplored. In this paper, we propose Intra-Inter Relational Table Learning (InRTL), a unified framework that explicitly models dependencies both within and across relational tables. Specifically, InRTL formalizes two complementary interaction patterns: intra-table interactions, describing associations among rows within the same table, and inter-table interactions, describing dependencies between rows across PK-FK-linked tables. To model these dependencies, we develop a column-aware table encoder to generate initial row representations, followed by
Transformer-based self-
attention and cross-
attention modules for intra-table and inter-table learning, respectively. To further improve scalability, InRTL incorporates linearized
attention and heterogeneous graph neural networks to simplify the self-
attention and cross-
attention operations. Extensive experiments on ten
datasets covering 24 real-world tasks demonstrate the effectiveness of our approach. Code is available at https://github.com/W1nterFloW/InRTL.
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All authors Weichen Li, Ken Zhong, Zheng Wang, Li Pan, Jianhua Li
Systems and Control · Cross-listed from q-bio.QM
Perturbational Validity for Foundation Models of Brain Dynamics: A Controlled Proof-of-Principle Simulation
José C. Garcí Alanis, Sarah Alizadeh, Marco Rothermel et al. (+5 more)
Foundation models for human
brain recordings are usually evaluated by signal reconstruction, future-state prediction, and transfer to downstream tasks. However, these
benchmarks do not establish whether a transferred model remains valid when the system is actively perturbed. We define perturbational validity as the preservation, after limited system-specific adaptation, of the conditional distribution of future trajectories given the current state and a controlled input, and we evaluate it at three levels: time-series accuracy, dynamical-structure similarity, and responses to perturbations excluded from calibration. We demonstrate the framework in an oracle-drift simulation of
stochastic bistable systems. Two otherwise identical multilayer perceptrons were trained on drift evaluations from passive or input-driven trajectories. Next, for each held-out system the shared weights were frozen and only a three-dimensional
embedding was adapted, with a correctly specified cubic model fitted from scratch as comparator. With two to five system-specific evaluations, perturbational pretraining yielded lower errors in recovering controlled flow, landscape geometry, finite-run occupancy, response distributions, and dose-transition curves. The advantage was reproduced across five independent runs, persisted under full-network adaptation of the passive model, and was attributable to input excitation rather than transition-state coverage: excitation alone lowered controlled-flow error 1.94-fold relative to coverage alone, in five of five runs. The cubic model became competitive as calibration grew, showing that the benefit is specific to few-shot transfer. This controlled demonstration does not test recovery of dynamics from noisy or partially observed
brain recordings. It shows why passive prediction should be complemented by prospective evaluation under controlled inputs.
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All authors José C. Garcí Alanis, Sarah Alizadeh, Marco Rothermel, Bita Shariatpanahi, Mina Kheirkhah, Stefan G. Hofmann, Tim Hahn, Hamidreza Jamalabadi
Software Engineering
What is the Difference Between Me and You? Benchmarking the Quality Gap Between Human-Written and AI-Generated Code
Cristina Improta, Pietro Liguori and Domenico Cotroneo
AI coding assistants are becoming co-authors of production software, yet their evaluation centers on functional correctness, leaving open whether their code differs from human code in the quality dimensions dominating lifecycle cost. We compare human-written and AI-generated code at scale: 787,562 function pairs across Python, Java, and C, each human function mined from open-source repositories paired with implementations generated from its docstring by three AI assistants (OpenAI GPT models, DeepSeek-Coder, Qwen2.5-Coder). We characterize structural complexity and statistical naturalness, and map static-analysis findings onto Orthogonal Defect Classification for defects and the Common Weakness Enumeration for vulnerabilities, making authors and
languages directly comparable. AI-generated code is structurally compressed and stylistically templated: roughly half the size and branching of human code, clustering apart at the style level. Defect profiles differ in kind: human code concentrates issues of mature codebases, AI code repetitive boilerplate; security is
language-dependent, with LLMs producing more, and more severe, findings in Python and Java but fewer high-severity
memory-safety findings than humans in C. Once size is controlled for, complexity metrics carry little signal, while naturalness separates authors. Finally, we release CQBench, a
benchmark of 27,346 issue-prone tasks with baselines and an evaluation pipeline for quality assurance and security testing.
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Computer Vision
ExpertHTR: Unified Handwritten Text Recognition with Multi-Task Learning and Sparse Mixture-of-Experts
Dang Hoai Nam, Nguyen Duy Hieu, Quang Huu Hieu et al. (+1 more)
Handwritten text recognition resources are often small and distributed across collections that differ in
language, script, document structure, and annotation format, making joint page-level training difficult. We propose ExpertHTR, a unified
vision-
language framework that addresses this problem through complementary supervision and conditional model capacity. Structural annotations from heterogeneous
datasets are first organized through a common Page-Region-Line representation and used to construct four related training tasks for complete transcription, physical-line coverage, text localization, and localized recognition, without requiring additional manual labels. Building on a jointly trained dense model, ExpertHTR introduces a sparse Mixture-of-Experts architecture with an always-active shared branch and conditionally routed full-MLP experts. Sparsegen allows the number of active routed experts to vary with the hidden representation, while routing regularization reduces persistent concentration on a small subset of experts. Experiments on seven heterogeneous handwriting
benchmarks show that complementary supervision consistently improves training with page transcription alone, while joint multi-source training provides further gains on most
datasets. The proposed sparse expert model further improves the dense baseline on six of the seven sources. The final unified model also substantially outperforms the evaluated general-purpose OCR and
vision-
language systems on most
benchmarks and achieves state-of-the-art performance on the IAM paragraph-level
benchmark, while specialized HTR systems remain stronger on several challenging collections.
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All authors Dang Hoai Nam, Nguyen Duy Hieu, Quang Huu Hieu, Vo Nguyen Le Duy
Artificial Intelligence
Implicit Personality Representations in Humans and LLMs
Yilin Geng, Omri Abend, Eduard Hovy et al. (+1 more)
A century of psychology has found that the trait words people use to describe one another vary, but the relational structure among those traits, which ones go together and which oppose, is strikingly consistent across raters and cultures. We test whether the LLM (Qwen 2.5-7B-Instruct) reproduces this structure in its internal trait representations. From millions of crowd-sourced personality ratings of fictional characters, we build a human implicit-personality
matrix over hundreds of traits; from contrastive model activations, we build a matching
matrix over the same traits. The two relational structures align strongly (Mantel r = 0.77), and the agreement holds trait by trait as well as in aggregate. Two dominant axes of the model's trait representations recover the social and intellectual dimensions long known to organize human personality impressions, social warmth and intellectual competence. On held-out dialogue, projecting model activations onto these directions yields personality profiles that agree with human ratings. This work establishes a framework that enables comprehensive, human-grounded comparison between internal model trait geometry and the shared structure of human personality impressions.
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All authors Yilin Geng, Omri Abend, Eduard Hovy, Lea Frermann
Machine Learning
Write on Paper and Get the Online Digital Trace: A New Era for Handwriting
Florent Imbert, Yann Soullard, Eric Anquetil et al. (+5 more)
Capturing the digital trace of handwriting usually requires a specific stylus and a compatible substrate, be it a capacitive touchscreen, an ElectroMagnetic Resonance (EMR) tablet as used in Wacom systems or special paper. While writing on regular paper offers rich haptics, no latency and is well known for improving information retention, no low-cost and widely accepted, effective solution exists to digitize such a pen trace. The challenge is to accurately track the pen's trajectory without an external reference system while allowing unrestricted freedom of pen movement across a surface. We propose an innovative solution that combines a digital pen, advanced artificial intelligence
algorithms, and adaptive AI techniques to reconstruct the digital trace of handwriting. Our approach integrates hardware development, focusing on a sensor-equipped pen, with software innovations to optimize trajectory reconstruction and processing in real time using an embedded AI. This work aims to advance the state-of-the-art in automated trace reconstruction of handwriting, enabling a seamless connection between traditional handwriting on paper and capturing the trace digitally.
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All authors Florent Imbert, Yann Soullard, Eric Anquetil, Tanja Harbaum, Alexey Serdyuk, Fabian Kress, Tim Hamann, Peter Kampf
Artificial Intelligence
Enabling and Understanding Personalization in AI-Generated Advertising Imagery
Victor Kolominsky-Rabas, Leopold Müller, Claudius Budcke et al. (+2 more)
Personalized marketing traditionally matches static products to customers, while dynamic creative optimization focuses mainly on AI-driven text personalization or basic product image modifications. We address this gap by developing and implementing an AI-based framework that generates personalized advertising imagery directly from customer data. We evaluate this framework in a two-stage within-subject study with N=100 participants across four products and three levels of personalization, varied by the amount and specificity of customer data used. Participants rated each image on attitude toward the advertisement, attitude toward the product, and purchase intention. Results show that participants perceive differences across personalization levels and evaluate AI-generated advertising imagery most positively at a moderate level of personalization. High personalization increases perceived personalization, which is positively associated with all three outcome measures, but also increases perceived creepiness, which is negatively associated with the outcomes and dominates the total effect.
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All authors Victor Kolominsky-Rabas, Leopold Müller, Claudius Budcke, Claas Christian Germelmann, Niklas Kühl
Computers and Society · Cross-listed from physics.soc-ph
Global maps of travel time to emergency and tertiary hospitals
Johan Emilsson
Global estimates of access to health care treat every facility alike. Drive time was routed on the OpenStreetMap network from all 217,841 of its hospitals and 15-, 30- and 60-minute isochrones dissolved for nested capability tiers, weighted with GHS-POP 2025. 96.2% of the world's population lives within an hour's drive of a hospital, 78.5% of a hospital with a documented emergency department, and 40.3% of one with a helipad or university name marking tertiary care. The 1.45 billion people within an hour of a hospital but not of an emergency hospital live mainly in South and East Asia and Nigeria; the 312 million beyond an hour of any hospital lie in a belt from the Sahel to Madagascar. Defining the tertiary tier by helipad alone leaves its estimate unchanged, and the isochrone construction shifts the 60-minute figures by a few percent. Counting every facility as equivalent overstates access to time-critical care.
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Human-Computer Interaction
Understanding Game Coaching on Gig Platforms
Hwijoon Lee and Saiph Savage
Freelance game coaches monetize their gaming expertise by offering personalized instruction to players seeking to improve, working through gig platforms, yet little is known about how they operate. To address this gap, we conducted semi-structured interviews with 20 experienced freelance coaches across 17 competitive games on Fiverr. Despite lacking shared formal training, these coaches converged on similar practices centered on rapport-building, individualized diagnosis, and adaptive feedback. We identify two structural conditions shaping this work: dual precarity, in which coaches navigate both gig platform instability and the lifecycle
volatility of live-service games; and earned authority, in which coaches must continually establish legitimacy through visible competitive achievement within the same gaming spaces as their students. These coaches welcomed AI for administrative and analytic support but resisted its use in live interactions where trust, relational engagement, and situated judgment remained central. We discuss implications for Games HCI and the design of computational coaching systems.
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Artificial Intelligence
I Am AdMan: A Pipeline for Automatic Generation of Personalized Advertising Imagery
Victor Kolominsky-Rabas, Leopold Müller, Claudius Budcke et al. (+1 more)
Personalized marketing can increase customer engagement, satisfaction, and conversion. While existing personalization approaches have become effective at matching the right product to the right customer, the visual representation of advertisements remains generic and only weakly tailored to the individual. Prior research shows that generative artificial intelligence can improve the creation of personalized advertisements, particularly for text, and that image generation models can support scalable advertisement production. However, little research has examined how detailed customer information can be systematically translated into fully AI-generated, personalized advertising imagery at scale on a technical level. To address this gap, we propose AdMan, a multi-
agent pipeline that transforms customer data into personas, generates personalized advertisement images conditioned on product reference images, and applies an LLM-based judge
agent for automated quality control. We implement the pipeline with two different model configurations and evaluate it across four products, using six celebrity personas for qualitative inspection, and 100 real customer profiles, producing 1745 advertisements. The evaluation combines a qualitative expert focus group and a quantitative artifact-rate assessment. The results show that the pipeline can generate photorealistic and personalized advertisements. At the same time, performance varies substantially by product complexity and model configuration. Our findings extend the literature on AI-based personalized advertising by demonstrating the feasibility and current limitations of fully automated image generation for advertising.
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All authors Victor Kolominsky-Rabas, Leopold Müller, Claudius Budcke, Niklas Kühl
Computer Vision
Semantically Aligned Gradient-Driven Context-Preserving Image Editing
Chiranjeev Chiranjeev, Muskan Dosi, Mayank Vatsa et al. (+1 more)
Instruction-guided image editing has a training-time blind spot. Generative editors are never required to semantically verify whether their outputs actually satisfy the instruction. Supervision stops at reconstruction and input textual-level conditioning. This produces incomplete edits, spatial spillover, and poor localization. We present IABEdit, a model-agnostic framework that embeds differentiable semantic verification into training. A frozen
vision-
language model extracts spatially-aware descriptors from the ground-truth edit. A trainable aligner then reproduces them from the generated output. The residual between the two becomes a gradient that teaches the generator both what to edit and where, with no inference-time VLM cost. IABEdit is compatible with diverse backbones, including U-Net (Stable
Diffusion) and MMDiT (FLUX), without altering their inference pipelines. On MagicBrush, it improves structural fidelity by +3.49 DINO-I over the best
diffusion baseline and +1.26 over the best overall baseline, while remaining competitive on instruction
alignment. It also achieves state-of-the-art instruction adherence performance on RealEdit and EMU Edit
benchmarks based on
embedding-based metrics. Most consequentially, on the D-LORD surveillance
benchmark, it surpasses the proprietary Gemini
agent by +5.13 DINO-P under heavy occlusion, where preserving identity is hardest. This shows that gradient-aligned VLM
distillation holds up under real-world-like surveillance and occlusion conditions. Human and GPT-4o evaluations confirm perceptually precise, well-localized edits.
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All authors Chiranjeev Chiranjeev, Muskan Dosi, Mayank Vatsa, Richa Singh
Machine Learning
SIFPBPNet: A Dual-Path Network for Wearable and Cuffless Blood Pressure Estimation via Individualized Steady-state Representation
Shuailong Tang, Xiaoyu Li, Donglin Xie et al. (+4 more)
Continuous and cuffless blood pressure (BP) monitoring using photoplethysmography (PPG) is of great interest for low-cost and personalized cardiovascular health management. However, significant population heterogeneity and the "one-to-many mapping" problem, where similar waveforms across individuals correspond to different BP levels, limit the accuracy of conventional population-based models. To address this challenge, we propose a dual-path architecture termed SIFPBPNet, which separately represents steady-state and instantaneous features, through a Steady-state Feature Path (SFP) and an Instantaneous Feature Path (IFP). The SFP employs a Graph
Attention Network (GAT) to extract individual-specific and long-term characteristics from multi-day historical PPG trajectories. In parallel, the IFP captures short-term dynamics from current PPG segments and incorporates the steady-state prior via a cross-
attention mechanism. Experiments on a large-scale wearable
dataset demonstrate that SIFPBPNet achieves a Mean Absolute Error (MAE) of 8.57 and 5.97 mmHg for systolic and diastolic BP, respectively, outperforming state-of-the-art models. Furthermore, the SFP module consistently improves performance when integrated into various backbone architectures, yielding 2.8-13.1% relative MAE reductions for systolic BP. These results highlight the strong generalizability and plug-and-play transferability of the SFP module, underscoring its great potential for accurate cuffless BP monitoring.
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All authors Shuailong Tang, Xiaoyu Li, Donglin Xie, Wei Chen, Guangpu Zhu, Yelei Li, Yali Zheng
Artificial Intelligence
Residual Vector-based Reconstruction as Long-Context Recall Regardless of Context Window Size
MyungHoon Ryu, XinYu Piao and Jong-Kook Kim
Large
language models (LLMs) process long contexts, including long documents and lengthy conversations, but face
token-level
memory usage that increases proportionally to input length. Although model optimization and lossy
prompt compression are widely used, these methods still fail to solve the long-context recall problem beyond pretrained and size-constrained context windows. This paper proposes a long-context recall method that maintains near-constant GPU
memory usage as context length increases, without additional training. The main idea is to reconstruct facts using parameter activations in the LLM's feed-forward layers, which store residual vectors representing facts from the source document. Utilizing residual vectors allows the LLM to deterministically reconstruct query relevant facts without referencing the original document, preserving high fidelity and reducing
memory usage without fine-tuning weights. Experimental results show that the proposed method enables answering single-fact questions in two-million-
token story contexts where previous methods fail.
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Artificial Intelligence
Generative AI Use Cases In Real Estate Marketing: Adoption and Constraints in Germany
Victor Kolominsky-Rabas, Leopold Müller, Felicia Perpina et al. (+1 more)
Generative artificial intelligence (GenAI) is changing how work is organized and performed. Real estate marketing is a
prime example of this, yet evidence of GenAI in real estate
agents' day-to-day practice remains scarce. In this work, we report on our insights from a German-based empirical study with eleven semi-structured interviews. GenAI is already utilized across different activities, with marketing communication being the most prominent. Concrete use cases are emergent and unevenly adopted, with writing exposé texts being the only widely established one. Interaction is predominantly human-in-the-loop: GenAI drafts, structures, and retrieves, while real estate
agents curate, verify, and decide. Constraints stem less from model capability than from integration with listings and documents, data availability, and compliance in sensitive tasks. The study contributes a grounded map of existing and potential use cases and identifies tentative practical implications for adoption.
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All authors Victor Kolominsky-Rabas, Leopold Müller, Felicia Perpina, Niklas Kühl
Computer Vision
NOVA-GS: Noise-Aware View-Consistent Gaussian Splatting for Low-Light Novel View Synthesis
Shaurya Pavan A, Vemunuri Divya Madhuri, Yash Pradeep Gawande et al. (+1 more)
Reconstructing 3D scenes under real-world low-light conditions remains challenging due to severe sensor noise, low signal-to-noise ratios, and degraded photometric consistency, which destabilize geometry estimation and novel view synthesis. Existing approaches often rely on well-lit reference data for reliable Structure-from-Motion (SfM) initialization under degraded inputs or apply per-view enhancement methods that introduce cross-view inconsistencies. To address these limitations, we propose NOVA-GS, a unified noise-aware framework for low-light 3D Gaussian Splatting that subsumes enhancement, denoising, and geometry optimization within a single process. Our method leverages VGGT-based feed-forward estimation to obtain robust camera poses and geometry directly from degraded inputs, eliminating the need for SfM. Building on this initialization, NOVA-GS integrates three coupled components: a structure-aware enhancement module for exposure correction, a self-supervised denoising module with blind-spot masking for pseudo-supervision, and a consistency-driven Gaussian Splatting optimization enforcing cross-view
geometric coherence. We further introduce a noise-guided spherical harmonic regularization to suppress view-dependent artifacts in noisy regions. Extensive experiments on diverse real-world low-light
datasets demonstrate improved
geometric fidelity, color consistency, and robustness without requiring paired supervision or well-lit references. https://shaurya2524.github.io/nova-gs/
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All authors Shaurya Pavan A, Vemunuri Divya Madhuri, Yash Pradeep Gawande, Kaushik Mitra
Cryptography and Security
Bridging the First-Hour Gap: Evaluating AI Reliability and Benchmarking Deficiencies in Cyber Incident Response for Law Enforcement
Roshin Sleeba C and Hiran V Nath
The actions of frontline law enforcement officers in the initial hour of a cyber incident play a vital role in determining the ultimate success of an investigation. The minor mistakes they commit might result in irreversible critical impacts. The integrity of the investigation can be compromised, and the prosecution of cyber criminals can be hindered due to minor mistakes that happen in the initial hour. These are mainly because of the volatile nature of digital artifacts that might lead to procedural errors and evidence attrition. This paper provides a systematic survey of decision-support architectures designed to assist first responders of a cybercrime, categorizing them into playbooks, Large
Language Models (LLMs),
Retrieval-Augmented Generation (RAG) frameworks, and Agentic AI systems. The survey critically considers the constraints of limited technical proficiency and inconsistent forensic infrastructure in a practical scenario. Our analysis identifies RAG-based systems as a relatively viable intermediate solution due to their natural
language adaptability. However, significant risk factors like
prompt sensitivity and the potential for confident
hallucinations in legal contexts pose a major challenge. Furthermore, we review current
benchmarks in cybersecurity and demonstrate that they are not sufficient to capture the specific safety and legal requirements of law enforcement, focusing on the initial hour of the cybercrime. We conclude by arguing for the necessity of a new evaluation
benchmark focused on naive query robustness and evidence preservation, so as to ensure that AI-driven guidance aligns with the mandatory demands of judicial proceedings.
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Information Retrieval
Personalized and Trust-Aware Health Recommendation Policies for a Construction Workplace
Atefeh Mollabagher, Yogesh Gautam, Houtan Jebelli et al. (+1 more)
Construction workers face workplace risks such as fatigue, heat stress, and other physically demanding conditions that can negatively affect their health and safety. Although monitoring these risks is important, timely and personalized health interventions are also needed to help prevent negative impacts on workers' well-being and productivity. To this end, in this paper, we propose a model to capture the interactions between a trust-aware health recommender system and workers who differ in health and trust sensitivity. Specifically, in our proposed dynamic model, worker health evolves over time, worker trust is affected by both health and recommendation dynamics, and trust in turn affects compliance with future recommendations. Given this model, we characterize the recommender policy, including a health-based recommendation triggering threshold and the recommendation frequency. We do so using both model-based short-horizon control and model-free reinforcement learning. We then investigate how recommendation frequencies are adjusted for different workers to balance their health, productivity, and trust. Our findings provide insight into the design of personalized health recommendation policies in construction workplaces and beyond.
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All authors Atefeh Mollabagher, Yogesh Gautam, Houtan Jebelli, Parinaz Naghizadeh
Computer Vision
Detecting and Explaining Fake News Short Videos with Multimodal Content and Real-World Evidence
Yifeng Luo, Yupeng Li, Ming Tang et al. (+2 more)
Short-
video platforms have become a primary news source for the public, which has also enabled the widespread dissemination of fake news
videos. We study the task of fake news
video detection and explanation (FNVDE). Existing methods face two critical limitations. First, commonly used frame selection strategies may omit veracity-relevant cues or provide insufficient temporal context for understanding news
videos. Second, prior methods neglect either
multimodal understanding or evidence
retrieval. To address these limitations, we propose NVKE-CEI, a unified system that integrates a news
video keyframes extraction method (NVKE) and an FNVDE framework leveraging both content and evidence information (CEI). NVKE selects keyframes based on chronological changes in combined visual and OCR-text similarity. CEI employs two specialized LLM-based fact checkers (content-based and evidence-based) whose outputs are fused by a lightweight judge model. Extensive experiments show that NVKE-CEI outperforms state-of-the-art baselines while generating high-quality content-grounded explanations.
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All authors Yifeng Luo, Yupeng Li, Ming Tang, Jianxiong Guo, Liang Lan