Tootfinder

interactome_pdz: PDZ-domain interactome (2005)
A network of PDZ-domain-mediated protein–protein binding interactions, extracted from the PDZBase database. Nodes represent proteins and an edge represents a binding interaction between two proteins.
This network has 212 nodes and 244 edges.
Tags: Biological, Protein interactions, Unweighted

Ah yes, I really dislike it when, as someone who is autistic, I don’t look before I accidentally block the entire Mastodon.social domain instead of just blocking a specific bot user.
Great, now over 100 lost me in their feeds. My day is ruined.
Domain unblocked, please boost for my visibility again!
#ActuallyAutistic

FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
Nan Mu, Ruiqi Song, Xiaoning Li, Zhihui Xu, Jingfeng Jiang, Chen Zhao
https://arxiv.org/abs/2506.11454

FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
Background: Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide. Precise segmentation of coronary arteries from invasive coronary angiography (ICA) is critical for effective clinical decision-making. Objective: This study aims to propose a novel deep learning model based on frequency-domain analysis to enhance the accuracy of coronary artery segmentation and stenosis detection in ICA, thereby offering robust support for the stenosis detection and treatment of …

The Strategic Imperative for Healthcare Organizations to Build Proprietary Foundation Models
Naresh Tiwari
https://arxiv.org/abs/2506.11412 https://…

The Strategic Imperative for Healthcare Organizations to Build Proprietary Foundation Models
This paper presents a comprehensive analysis of the strategic imperative for healthcare organizations to develop proprietary foundation models rather than relying exclusively on commercial alternatives. We examine four fundamental considerations driving this imperative: the domain-specific requirements of healthcare data representation, critical data sovereignty and governance considerations unique to healthcare, strategic competitive advantages afforded by proprietary AI infrastructure, and th…

from my link log —
Postgres extensions in Rust.
https://depth-first.com/articles/2021/08/25/postgres-extensions-in-rust/
saved 2021-08-29

Postgres Extensions in Rust
Opening a new path to high-performance, domain-specific database applications.

Harvard releases Institutional Books 1.0, a dataset for AI researchers with 242B tokens, from 394M scanned pages and 983K public domain books in 254 languages (Matt O'Brien/Associated Press)
https://apnews.com/article/ai-chatbot-

@… is/will there ever be a way to set a custom bluesky domain in wafrn?

@… is/will there ever be a way to set a custom bluesky domain in wafrn?

Inference post region selection
Dominique Bontemps (IMT), Fran\c{c}ois Bachoc (IMT, ANITI), Pierre Neuvial (IMT)
https://arxiv.org/abs/2506.11564 https://

Inference post region selection
Post-selection inference consists in providing statistical guarantees, based on a data set, that are robust to a prior model selection step on the same data set. In this paper, we address an instance of the post-selection-inference problem, where the model selection step consists in selecting a rectangular region in a spatial domain. The inference step then consists in constructing confidence intervals on the average signal of this region. This is motivated by applications such as genetics or b…

Heute vor 62 Jahren: Am 14. Juni 1963 zündeten die #USA im Rahmen von Operation Storax die Atombombe "Mataco". Storax war eine Serie von #Kernwaffentests bei der 62/63 insgesamt 47 Bomben im Testgebiet in #Nevada

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis
Zuzanna Skorniewska, Bartlomiej W. Papiez
https://arxiv.org/abs/2506.11753

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis
The adoption of neural network models in medical imaging has been constrained by strict privacy regulations, limited data availability, high acquisition costs, and demographic biases. Deep generative models offer a promising solution by generating synthetic data that bypasses privacy concerns and addresses fairness by producing samples for under-represented groups. However, unlike natural images, medical imaging requires validation not only for fidelity (e.g., Fréchet Inception Score) but also…

From Sharpness to Better Generalization for Speech Deepfake Detection
Wen Huang, Xuechen Liu, Xin Wang, Junichi Yamagishi, Yanmin Qian
https://arxiv.org/abs/2506.11532

From Sharpness to Better Generalization for Speech Deepfake Detection
Generalization remains a critical challenge in speech deepfake detection (SDD). While various approaches aim to improve robustness, generalization is typically assessed through performance metrics like equal error rate without a theoretical framework to explain model performance. This work investigates sharpness as a theoretical proxy for generalization in SDD. We analyze how sharpness responds to domain shifts and find it increases in unseen conditions, indicating higher model sensitivity. Bas…

Multimodal Modeling of CRISPR-Cas12 Activity Using Foundation Models and Chromatin Accessibility Data
Azim Dehghani Amirabad, Yanfei Zhang, Artem Moskalev, Sowmya Rajesh, Tommaso Mansi, Shuwei Li, Mangal Prakash, Rui Liao
https://arxiv.org/abs/2506.11182

Multimodal Modeling of CRISPR-Cas12 Activity Using Foundation Models and Chromatin Accessibility Data
Predicting guide RNA (gRNA) activity is critical for effective CRISPR-Cas12 genome editing but remains challenging due to limited data, variation across protospacer adjacent motifs (PAMs-short sequence requirements for Cas binding), and reliance on large-scale training. We investigate whether pre-trained biological foundation model originally trained on transcriptomic data can improve gRNA activity estimation even without domain-specific pre-training. Using embeddings from existing RNA foundati…

Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection
David Farr, Kevin Talty, Alexandra Farr, John Stockdale, Iain Cruickshank, Jevin West
https://arxiv.org/abs/2506.10104

Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection
As cyber threats become more sophisticated, rapid and accurate vulnerability detection is essential for maintaining secure systems. This study explores the use of Large Language Models (LLMs) in software vulnerability assessment by simulating the identification of Python code with known Common Weakness Enumerations (CWEs), comparing zero-shot, few-shot cross-domain, and few-shot in-domain prompting strategies. Our results indicate that while zero-shot prompting performs poorly, few-shot prompti…

Time-domain field correlation measurements enable tomography of highly multimode quantum states of light
Emanuel Hubenschmid, Guido Burkard
https://arxiv.org/abs/2506.10483

Time-domain field correlation measurements enable tomography of highly multimode quantum states of light
Recent progress in ultrafast optics facilitates the investigation of the dynamics of highly multimode quantum states of light, as demonstrated by the application of electro-optic sampling to quantum states of the electromagnetic field. Yet, the complete tomographic reconstruction of optical quantum states with prior unknown statistics and dynamics is still challenging, since state-of-the-art tomographic methods require the measurement of many orthogonal, distinguishable modes. Here, we propose …

Complete fabrication. Just absolutely making shit up, and so confidently.
(The Apollo Archive is a thing, but it has nothing to do with archive.org, and that hostname has nothing to do with the Apollo Archive.)

crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023
Navodini Wijethilake, Reuben Dorent, Marina Ivory, Aaron Kujawa, Stefan Cornelissen, Patrick Langenhuizen, Mohamed Okasha, Anna Oviedova, Hexin Dong, Bogyeong Kang, Guillaume Sall\'e, Luyi Han, Ziyuan Zhao, Han Liu, Tao Yang, Shahad Hardan, Hussain Alasmawi, Santosh Sanjeev, Yuzhou Zhuang, Satoshi Kondo, Maria Baldeon Calisto, Shaikh Muh…

crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023
The cross-Modality Domain Adaptation (crossMoDA) challenge series, initiated in 2021 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), focuses on unsupervised cross-modality segmentation, learning from contrast-enhanced T1 (ceT1) and transferring to T2 MRI. The task is an extreme example of domain shift chosen to serve as a meaningful and illustrative benchmark. From a clinical application perspective, it aims to automate Ve…

Boundary output feedback stabilization of a cascade of N heat equations
Hugo Lhachemi (L2S), Christophe Prieur (GIPSA-INFINITY), Emmanuel Tr\'elat (LJLL)
https://arxiv.org/abs/2506.10500

Boundary output feedback stabilization of a cascade of N heat equations
This paper solves the problem of output feedback stabilization for a cascade of N heat equations that are coupled at the boundary, the input being a scalar boundary control applied to the first heat equation of the cascade, and the scalar output being either a distributed or a pointwise in-domain measurement done on the last equation of the cascade. Two different configurations are studied in details. The first one consists of a cascade of N heat equations with totally disconnected spectra. The…

Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments
Fatemeh Mohammadi Amin, Darwin G. Caldwell, Hans Wernher van de Venn
https://arxiv.org/abs/2506.09552

Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments
The robust interpretation of 3D environments is crucial for human-robot collaboration (HRC) applications, where safety and operational efficiency are paramount. Semantic segmentation plays a key role in this context by enabling a precise and detailed understanding of the environment. Considering the intense data hunger for real-world industrial annotated data essential for effective semantic segmentation, this paper introduces a pioneering approach in the Sim2Real domain adaptation for semantic…

Enhancing Taiji's Parameter Estimation under Non-Stationarity: a Time-Frequency Domain Framework for Galactic Binaries and Instrumental Noises
Minghui Du, Ziren Luo, Peng Xu
https://arxiv.org/abs/2506.10599

Enhancing Taiji's Parameter Estimation under Non-Stationarity: a Time-Frequency Domain Framework for Galactic Binaries and Instrumental Noises
The data analysis of space-based gravitational wave detectors like Taiji faces significant challenges from non-stationary noise, which compromises the efficacy of traditional frequency-domain analysis. This work proposes a unified framework based on short-time Fourier transform (STFT) to enhance parameter estimation of Galactic binary and characterization of instrumental noise under non-stationarity. Segmenting data into locally stationary intervals, we derive STFT-based models for signals and …

Topological defects as effective dynamical dark energy
Haipeng An, Chengcheng Han, Borui Zhang
https://arxiv.org/abs/2506.10075 https://

Topological defects as effective dynamical dark energy
In this work, we consider the possibility that the dynamical dark energy hinted at by recent DESI data may be mimicked by the effects of additional components in the universe, potentially arising from topological defects. We find that the data does not show a particular preference for the existence of cosmic strings. However, a domain wall contribution at the percent level can improve the fit, yielding a $Δχ^2= -1.72$ compared to the $Λ\rm{CDM}$ model. The improvement indicates that topologi…

Semi-Tensor-Product Based Convolutional Neural Networks
Daizhan Cheng
https://arxiv.org/abs/2506.10407 https://arxiv.org/pdf/2506.104…

Semi-Tensor-Product Based Convolutional Neural Networks
The semi-tensor product (STP) of vectors is a generalization of conventional inner product of vectors, which allows the factor vectors to of different dimensions. This paper proposes a domain-based convolutional product (CP). Combining domain-based CP with STP of vectors, a new CP is proposed. Since there is no zero or any other padding, it can avoid the junk information caused by padding. Using it, the STP-based convolutional neural network (CNN) is developed. Its application to image and thir…

This https://arxiv.org/abs/2502.00740 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Floating exercise boundaries for American options in time-inhomogeneous models
This paper examines a semi-analytical approach for pricing American options in time-inhomogeneous models characterized by negative interest rates (for equity/FX) or negative convenience yields (for commodities/cryptocurrencies). Under such conditions, exercise boundaries may exhibit a "floating" structure - dynamically appearing and disappearing. For example, a second exercise boundary could emerge within the computational domain and subsequently both could collapse, demanding specialized prici…

Description and Discussion on DCASE 2025 Challenge Task 2: First-shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Tomoya Nishida, Noboru Harada, Daisuke Niizumi, Davide Albertini, Roberto Sannino, Simone Pradolini, Filippo Augusti, Keisuke Imoto, Kota Dohi, Harsh Purohit, Takashi Endo, Yohei Kawaguchi
https://

Description and Discussion on DCASE 2025 Challenge Task 2: First-shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
This paper introduces the task description for the Detection and Classification of Acoustic Scenes and Events (DCASE) 2025 Challenge Task 2, titled "First-shot unsupervised anomalous sound detection (ASD) for machine condition monitoring." Building on the DCASE 2024 Challenge Task 2, this task is structured as a first-shot problem within a domain generalization framework. The primary objective of the first-shot approach is to facilitate the rapid deployment of ASD systems for new machine types …

This https://arxiv.org/abs/2505.00533 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Test-time Correlation Alignment
Deep neural networks often degrade under distribution shifts. Although domain adaptation offers a solution, privacy constraints often prevent access to source data, making Test-Time Adaptation (TTA, which adapts using only unlabeled test data) increasingly attractive. However, current TTA methods still face practical challenges: (1) a primary focus on instance-wise alignment, overlooking CORrelation ALignment (CORAL) due to missing source correlations; (2) complex backpropagation operations for…

variability.dev: Towards an Online Toolbox for Feature Modeling
Tobias He{\ss}, Lukas Ostheimer, Tobias Betz, Simon Karrer, Tim Jannik Schmidt, Pierre Coquet, Sean Semmler, Thomas Th\"um
https://arxiv.org/abs/2506.09845

variability.dev: Towards an Online Toolbox for Feature Modeling
The emergence of feature models as the default to model the variability in configurable systems fosters a rich diversity in applications, application domains, and perspectives. Independent of their domain, modelers require to open, view, edit, transform, save, and configure models as well as to collaborate with others. However, at the time of writing, the top five results when googling ``Online Editor Feature Model'' point to editors that either have minimal functionality, are unmaintained or d…

This https://arxiv.org/abs/2505.16809 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalities
Existing methods for multimodal MRI segmentation with missing modalities typically assume that all MRI modalities are available during training. However, in clinical practice, some modalities may be missing due to the sequential nature of MRI acquisition, leading to performance degradation. Furthermore, retraining models to accommodate newly available modalities can be inefficient and may cause overfitting, potentially compromising previously learned knowledge. To address these challenges, we p…

Augmented Reality User Interfaces for First Responders: A Scoping Literature Review
Erin Argo, Tanim Ahmed, Sarah Gable, Callie Hampton, Jeronimo Grandi, Regis Kopper
https://arxiv.org/abs/2506.09236

Augmented Reality User Interfaces for First Responders: A Scoping Literature Review
During the past decade, there has been a significant increase in research focused on integrating AR User Interfaces into public safety applications, particularly for first responders in the domains of Emergency Medical Services, Firefighting, and Law Enforcement. This paper presents the results of a scoping review involving the application of AR user interfaces in the public safety domain and applies an established systematic review methodology to provide a comprehensive analysis of the current…

This https://arxiv.org/abs/2505.22240 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

BioHopR: A Benchmark for Multi-Hop, Multi-Answer Reasoning in Biomedical Domain
Biomedical reasoning often requires traversing interconnected relationships across entities such as drugs, diseases, and proteins. Despite the increasing prominence of large language models (LLMs), existing benchmarks lack the ability to evaluate multi-hop reasoning in the biomedical domain, particularly for queries involving one-to-many and many-to-many relationships. This gap leaves the critical challenges of biomedical multi-hop reasoning underexplored. To address this, we introduce BioHopR,…

Reduced-Order Time Splitting for Navier-Stokes with Open Boundaries
Mejdi Aza\"iez, Tom\'as Chac\'on Rebollo, Carlos N\'u\~nez Fern\'andez, Samuele Rubino
https://arxiv.org/abs/2506.10763

Reduced-Order Time Splitting for Navier-Stokes with Open Boundaries
In this work, we propose a Proper Orthogonal Decomposition-Reduced Order Model (POD-ROM) applied to time-splitting schemes for solving the Navier-Stokes equations with open boundary conditions. In this method, we combine three strategies to reduce the computing time to solve NSE: time splitting, reduction of the computational domain through non-standard treatment of open boundary conditions and reduced order modelling. To make the work self-contained, we first present the formulation of the tim…

New Jersey town uses zoning & eminent domain to stop a church from building a homeless shelter on an underused parking lot. https://apnews.com/article/new-jersey-episcopal-church-homeless-shelter-558f2bf594b3b6b0a587143933dae814…

A New Jersey church wanted to build a homeless shelter. Now the town might take its property
(RNS) — The mayor of Toms River, New Jersey, says he has a plan to revitalize the town’s riverfront and to create a new park for local families who live nearby.

Well--posedness for the biharmonic scattering problem for a penetrable obstacle
Rafael Ceja Ayala, Isaac Harris, Tonatiuh S\'anchez-Vizuet
https://arxiv.org/abs/2506.10176

Well--posedness for the biharmonic scattering problem for a penetrable obstacle
We address the direct scattering problem for a penetrable obstacle in an infinite elastic two--dimensional Kirchhoff--Love plate. Under the assumption that the plate's thickness is small relative to the wavelength of the incident wave, the propagation of perturbations on the plate is governed by the two-dimensional biharmonic wave equation, which we study in the frequency domain. With the help of an operator factorization, the scattering problem is analyzed from the perspective of a coupled bou…

Gradual Metaprogramming
Tianyu Chen, Darshal Shetty, Jeremy G. Siek, Chao-Hong Chen, Weixi Ma, Arnaud Venet, Rocky Liu
https://arxiv.org/abs/2506.09043 htt…

Gradual Metaprogramming
Data engineers increasingly use domain-specific languages (DSLs) to generate the code for data pipelines. Such DSLs are often embedded in Python. Unfortunately, there are challenges in debugging the generation of data pipelines: an error in a Python DSL script is often detected too late, after the execution of the script, and the source code location that triggers the error is hard to pinpoint. In this paper, we focus on the F3 DSL of Meta (Facebook), which is a DSL embedded in Python (so it …

Homeomorphic Sobolev extensions and integrability of hyperbolic metric
Xilin Zhou
https://arxiv.org/abs/2506.10216 https://arxiv.org/…

Homeomorphic Sobolev extensions and integrability of hyperbolic metric
Very recently, it was proved that if the hyperbolic metric of a planar Jordan domain is $L^q$-integrable for some $q\in (1,\infty)$, then every homeomorphic parametrization of the boundary Jordan curve via the unit circle can be extended to a Sobolev homeomorphism of the entire disk. This naturally raises the question of whether the extension holds under more general integrability conditions on the hyperbolic metric. In this work, we examine the case where the hyperbolic metric is $ϕ$-inte…

WDMIR: Wavelet-Driven Multimodal Intent Recognition
Weiyin Gong, Kai Zhang, Yanghai Zhang, Qi Liu, Xinjie Sun, Junyu Lu, Linbo Zhu
https://arxiv.org/abs/2506.10011

WDMIR: Wavelet-Driven Multimodal Intent Recognition
Multimodal intent recognition (MIR) seeks to accurately interpret user intentions by integrating verbal and non-verbal information across video, audio and text modalities. While existing approaches prioritize text analysis, they often overlook the rich semantic content embedded in non-verbal cues. This paper presents a novel Wavelet-Driven Multimodal Intent Recognition(WDMIR) framework that enhances intent understanding through frequency-domain analysis of non-verbal information. To be more spe…

TRAPs, Generalisations of MZVs, Locality and Resurgence for Quantum Field Theories
Pierre J. Clavier
https://arxiv.org/abs/2506.09493 https://

TRAPs, Generalisations of MZVs, Locality and Resurgence for Quantum Field Theories
This thesis presents some mathematical results related to quantum field theory. The first chapter is dedicated to TRAPs and how they could be used to rigorously define Feynman rules. The second introduces generalisations of MZVs and study their properties. The third gives the main results of the theory of locality structures. The fourth and last chapter presents a summability result within the framework of resurgence theory. Each chapter ends with open questions and conjectures on the domain.

Flexible and Efficient Drift Detection without Labels
Nelvin Tan, Yu-Ching Shih, Dong Yang, Amol Salunkhe
https://arxiv.org/abs/2506.08734 https://

Flexible and Efficient Drift Detection without Labels
Machine learning models are being increasingly used to automate decisions in almost every domain, and ensuring the performance of these models is crucial for ensuring high quality machine learning enabled services. Ensuring concept drift is detected early is thus of the highest importance. A lot of research on concept drift has focused on the supervised case that assumes the true labels of supervised tasks are available immediately after making predictions. Controlling for false positives while…

Analytic model for neutral penetration and plasma fueling
George J. Wilkie
https://arxiv.org/abs/2506.10906 https://arxiv.org/pdf/250…

Analytic model for neutral penetration and plasma fueling
Neutral atoms recycled from wall interaction interact with confined plasma, thereby refueling it, most strongly in the region closest to the wall. This occurs near the X-point in diverted configurations, or else near the wall itself in limited configurations. A progression of analytic models are developed for neutral density in the vicinity of a planar or linear source in an ionizing domain. First-principles neutral transport simulations with DEGAS2 are used throughout to test the validity and …

Deaths by domain of protest over 12 Bolivian presidencies.
Same data, same graphing technique, but sorting the categories (by which occurs across the most bins) brings visual order to this data visualization. #dataviz #rstats

Chiral non-Abelian domain walls in dense QCD
Sven Bjarke Gudnason, Muneto Nitta
https://arxiv.org/abs/2506.08819 https://arxiv.org/pd…

Chiral non-Abelian domain walls in dense QCD
In this paper, we study chiral non-Abelian domain walls in a phase of unconventional vacua of dense QCD. The phase is characterized by asymmetric vacuum-expectation values (VEVs), for example with the left scalar field, corresponding to the left quark-quark condensate, having a nonvanishing VEV and the right field having a vanishing one. The domain wall soliton interpolates between this vacuum and another where the left and right scalar fields switch roles. We contemplate that such vacua could …

Transaction Categorization with Relational Deep Learning in QuickBooks
Kaiwen Dong, Padmaja Jonnalagedda, Xiang Gao, Ayan Acharya, Maria Kissa, Mauricio Flores, Nitesh V. Chawla, Kamalika Das
https://arxiv.org/abs/2506.09234

Transaction Categorization with Relational Deep Learning in QuickBooks
Automatic transaction categorization is crucial for enhancing the customer experience in QuickBooks by providing accurate accounting and bookkeeping. The distinct challenges in this domain stem from the unique formatting of transaction descriptions, the wide variety of transaction categories, and the vast scale of the data involved. Furthermore, organizing transaction data in a relational database creates difficulties in developing a unified model that covers the entire database. In this work, …

A Spectral Confounder Adjustment for Spatial Regression with Multiple Exposures and Outcomes
Shih-Ni Prim, Yawen Guan, Shu Yang, Ana G Rappold, K. Lloyd Hill, Wei-Lun Tsai, Corinna Keeler, Brian J Reich
https://arxiv.org/abs/2506.09325

A Spectral Confounder Adjustment for Spatial Regression with Multiple Exposures and Outcomes
Unmeasured spatial confounding complicates exposure effect estimation in environmental health studies. This problem is exacerbated in studies with multiple health outcomes and environmental exposure variables, as the source and magnitude of confounding bias may differ across exposure/outcome pairs. We propose to mitigate the effects of spatial confounding in multivariate studies by projecting to the spectral domain to separate relationships by the spatial scale and assuming that the confounding…

This https://arxiv.org/abs/2410.20580 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Coherence-guided Preference Disentanglement for Cross-domain Recommendations
Discovering user preferences across different domains is pivotal in cross-domain recommendation systems, particularly when platforms lack comprehensive user-item interactive data. The limited presence of shared users often hampers the effective modeling of common preferences. While leveraging shared items' attributes, such as category and popularity, can enhance cross-domain recommendation performance, the scarcity of shared items between domains has limited research in this area. To address th…

Preorders on maximal chains: hyperplane arrangements, Cambrian lattices, and maximal green sequences
Mikhail Gorsky, Nicholas J. Williams
https://arxiv.org/abs/2506.08858

Preorders on maximal chains: hyperplane arrangements, Cambrian lattices, and maximal green sequences
We study preorders on (equivalence classes of) maximal chains in the general context of polygonal lattices endowed with suitably nice edge labellings. We show that, given a quotient of polygonal lattices, such edge labellings descend to the quotient, and that there is an induced order-preserving surjective map on the preordered sets of equivalence classes of maximal chains. Under a natural condition ensuring that the domain is a poset, the map is a contraction of preordered sets. We apply this …

Crossover in growth law in the vapor-liquid phase separation inside complex porous medium
Preethi M, Bhaskar Sen Gupta
https://arxiv.org/abs/2506.09646 htt…

Crossover in growth law in the vapor-liquid phase separation inside complex porous medium
We employ molecular dynamics simulations to investigate the domain morphology and growth kinetics of a vapor-liquid system embedded within a complex porous medium. By systematically varying the pore structure, we analyze the scaling behavior of correlation functions, structure factors, and domain growth exponents. The structure factor confirms the breakdown of Porod law and the emergence of fractal-like domain boundaries. Our key finding is the clear crossover in the domain growth law, from the…

This https://arxiv.org/abs/2504.18202 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

A post-common-envelope binary with double-peaked Balmer emission lines from TMTS
The dynamical method provides an efficient way to discover post-common-envelope binaries (PCEB) with faint white dwarfs (WDs), thanks to the development of time-domain survey projects. We perform a comprehensive analysis of the PCEB system TMTS J15530469+4457458 (J1553), discovered by the Tsinghua University-Ma Huateng Telescopes for Survey, to explore its physical origin and evolutionary fate. This system is characterized by double-peaked Balmer emission lines, and a cross-correlation function…

From IOCs to Group Profiles: On the Specificity of Threat Group Behaviors in CTI Knowledge Bases
Aakanksha Saha, Martina Lindorfer, Juan Caballero
https://arxiv.org/abs/2506.10645

From IOCs to Group Profiles: On the Specificity of Threat Group Behaviors in CTI Knowledge Bases
Indicators of Compromise (IOCs) such as IP addresses, file hashes, and domain names are commonly used for threat detection and attribution. However, IOCs tend to be short-lived as they are easy to change. As a result, the cybersecurity community is shifting focus towards more persistent behavioral profiles, such as the Tactics, Techniques, and Procedures (TTPs) and the software used by a threat group. However, the distinctiveness and completeness of such behavioral profiles remain largely unexp…

A collection of results relating the geometry of plane domains and the exit time of planar Brownian motion, II
Greg Markowsky, Clayton McDonald
https://arxiv.org/abs/2506.09364

A collection of results relating the geometry of plane domains and the exit time of planar Brownian motion, II
This paper is the sequel to another with the same name (Buttigieg et al., Comput. Methods Funct. Theory, 2023), and is concerned with results of the same type. We deduce a result on the moments of the exit time of Brownian motion from domains whose boundary curve is replaced by a dashed line, and from domains arising from a periodic tiling of the plane. We also give a construction of a type of domain which is similar to a wedge domain, but the behaviour of whose exit time moments answer several…

Heute vor 63 Jahren: Am 13.06.1962 zündeten die #USA im Rahmen von Operation Nougat die 36. Atombombe "Des Moines". Nougat war eine Serie von #Kernwaffentests bei der 61/62 insgesamt 45 Bomben im Testgebiet in

This https://arxiv.org/abs/2410.08476 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

JingZhao: A Framework for Rapid NIC Prototyping in the Domain-Specific-Network Era
The network is becoming domain-specific, which requires on-demand design of the network protocols, as well as the microarchitecture of the NIC. However, to develop such a NIC is not that easy. Since the scissor gap between network speed and the growth of CPU frequency is expanding, most of the protocols need to be offloaded to hardware. The process of designing, verifying and optimizing a domain-specific NIC usually takes great effort, which hinders the rapid iteration of new protocols and algo…

This https://arxiv.org/abs/2506.04327 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Fishing for the Optimal Roman High Latitude Time Domain Survey: Cosmological Constraints for 1,000 Possible Surveys
The upcoming Nancy Grace Roman Space Telescope is set to conduct a generation-defining SN Ia cosmology measurement with its High Latitude Time Domain Survey (HLTDS). However, between optical elements, exposure times, cadences, and survey areas, there are many survey parameters to consider. This work was part of a Roman Project Infrastructure Team effort to help the Core Community Survey (CCS) Committee finalize the HLTDS recommendation to the Roman Observations Time Allocation Committee. We sim…

The logic of hyperlinking in the era of social media:
- sure, you can link outside of our platform, but we'll proxy your link through our shortener so that you aren't exactly sure where you're going,
- everybody is confused where the hell they are going (and browsers amplify that nonsense by making address bars even dumber, showing barely anything except the top-level domain)
- oh, feeling too confused? Gotcha, we'll serve you additional screen showing "you're leaving our site, this is the exact link, are you sure?" so that you never run of mundane things to click at.
Reinventing the wheel for the millionth time, hell yeah.
(I have the same concern about those transition screens on Mastodon, but there's at least a tiny bit of logic behind that, especially with multiple different servers involved and stuff)

Design, development and commissioning of a new towing tank at the University of Southampton: A decade of endeavour
B Malas (LHEEA)
https://arxiv.org/abs/2506.09057

Design, development and commissioning of a new towing tank at the University of Southampton: A decade of endeavour
The University of Southampton in the United Kingdom is worldly renowned in the naval architecture domain. For several decades, the researchers in hydrodynamics have been discussing the need of a middle-sized towing tank to accommodate the teaching, research and commercial experiments that previously had to be outsourced. In the early 2010s, the renovation of the Boldrewood campus was decided and opened the way for the construction of this 138 m long, 6 m wide and 3.5 m deep facility. Commission…

Prospects for Time-Domain and Multi-Messenger Science with eXTP
Shu-Xu Yi, Wen Zhao, Ren-Xin Xu, Xue-Feng Wu, Giulia Stratta, Simone Dall'Osso, Yan-Jun Xu, Andrea Santangelo, Silvia Zane, Shuang-Nan Zhang, Hua Feng, Huan Yang, Junjie Mao, Junqiang Ge, Lijing Shao, Mi-Xiang Lan, He Gao, Lin Lin, Ning Jiang, Qingwen Wu, Tong Liu, Yun-Wei Yu, Xiang-Yu Wang, Jin Zhang, Dafne Guetta, Jin-Jun Geng, Di Xiao, Yong-Feng Huang, Yacheng Kang, Tian-Yong Cao, Zhen Zhang, Zhenwei Lyu, Zhen Pan, …

Prospects for Time-Domain and Multi-Messenger Science with eXTP
In this new era of time-domain and multi-messenger astronomy, various new transients and new phenomena are constantly being discovered thanks to the rapid advances in observations, which provide the excellent opportunity to study the physics in the extreme environments. The enhanced X-ray Timing and Polarimetry mission (eXTP), planned to be launched in 2030, has several key advantages, including advanced polarimetry, high sensitivity & large effective area, and wide energy range coverage, which…

Correcting Errors Through Partitioning and Burst-Deletion Correction
Yubo Sun, Gennian Ge
https://arxiv.org/abs/2506.07609 https://ar…

Correcting Errors Through Partitioning and Burst-Deletion Correction
In this paper, we propose a partitioning technique that decomposes a pair of sequences with overlapping $t$-deletion $s$-substitution balls into sub-pairs, where the $^{\leq}t$-burst-deletion balls of each sub-pair intersect. This decomposition facilitates the development of $t$-deletion $s$-substitution correcting codes that leverage approaches from $^{\leq}t$-burst-deletion correction. Building upon established approaches in the $^{\leq}t$-burst-deletion correction domain, we construct $t$-de…

This https://arxiv.org/abs/2501.03542 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Turbulence modeling over riblets via domain transformation
Numerical and experimental studies have demonstrated the drag-reducing potential of carefully designed streamwise-elongated riblets in lowering skin-friction drag. To support the systematic design of such surface corrugations, recent efforts have integrated simplified versions of the governing equations with innovative methods for representing the effects of rough boundaries on flow dynamics. Notably, the statistical response of the eddy-viscosity-enhanced linearized Navier-Stokes equations has…

The chat program is in public domain. This is not the GNU public license.
If it breaks then you get to keep both pieces.
-- Copyright notice for the chat program

AI research nonprofit EleutherAI releases the Common Pile v0.1, an 8TB dataset of licensed and open-domain text for AI models that it says is one of the largest (Kyle Wiggers/TechCrunch)
https://techcrunch.com/2025/06/06/eleu

EleutherAI releases massive AI training dataset of licensed and open domain text | TechCrunch
EleutherAI, an AI research organization, has released what it's claiming is one of the largest collections of licensed and open-domain text for training AI models.

Quasi-normal modes and echoes of generalized black hole bounces and their correspondence with shadows
Albert Duran-Cabac\'es, Diego Rubiera-Garcia, Diego S\'aez-Chill\'on G\'omez
https://arxiv.org/abs/2506.10814

Quasi-normal modes and echoes of generalized black hole bounces and their correspondence with shadows
We study the quasi-normal modes (QNMs) of a family of generalized black bounces interpolating between regular black holes and traversable wormhole solutions according to a single extra parameter $a$. Firstly, working with a generic spherically symmetric space-time with arbitrary radial function and an anisotropic fluid matter source, the general equations for the gravitational waves are obtained. Then, we focus on such particular space-time metric and use the time-domain method to find the evol…

This https://arxiv.org/abs/2505.21904 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation
Instance segmentation demands costly per-pixel annotations and large models. We introduce CAST, a semi-supervised knowledge distillation (SSKD) framework that compresses pretrained vision foundation models (VFM) into compact experts using limited labeled and abundant unlabeled data. CAST unfolds in three stages: (1) domain adaptation of the VFM teacher(s) via self-training with contrastive pixel calibration, (2) distillation into a compact student via a unified multi-objective loss that couples…

Convergence of adaptive boundary element methods driven by functional a posteriori error estimates
Alexander Freiszlinger, Dirk Pauly, Dirk Praetorius
https://arxiv.org/abs/2506.10499

Convergence of adaptive boundary element methods driven by functional a posteriori error estimates
The recent work [Kurz et al., Numer. Math., 147 (2021)] proposed functional a posteriori error estimates for boundary element methods (BEMs) together with a related adaptive mesh-refinement strategy. Unlike most a posteriori BEM error estimators, the proposed functional error estimators cover Galerkin as well as collocation BEM and, more importantly, do not control the error in the integral density on the boundary, but the error of the potential approximation in the domain, which is of greater …

This https://arxiv.org/abs/2506.04929 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT
Neural Machine Translation (NMT) has improved translation by using Transformer-based models, but it still struggles with word ambiguity and context. This problem is especially important in domain-specific applications, which often have problems with unclear sentences or poor data quality. Our research explores how adding information to models can improve translations in the context of e-commerce data. To this end we create ConECT -- a new Czech-to-Polish e-commerce product translation dataset c…

This https://arxiv.org/abs/2312.17294 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub
Large Language Models (LLMs) excel in traditional natural language processing tasks but struggle with problems that require complex domain-specific calculations or simulations. While equipping LLMs with external tools to build LLM-based agents can enhance their capabilities, existing approaches lack the flexibility to address diverse and ever-evolving user queries in open domains. Currently, there is also no existing dataset that evaluates LLMs on open-domain knowledge that requires tools to so…

On the sharp critical mass threshold for the 3D Patlak-Keller-Segel-Navier-Stokes system via Couette flow
Shikun Cui, Lili Wang, Wendong Wang, Juncheng Wei
https://arxiv.org/abs/2506.10578

On the sharp critical mass threshold for the 3D Patlak-Keller-Segel-Navier-Stokes system via Couette flow
As is well-known, the solution of the Patlak-Keller-Segel system in 3D may blow up in finite time regardless of any initial cell mass. In this paper, we are interested in the suppression of blow-up and the critical mass threshold for the 3D Patlak-Keller-Segel-Navier-Stokes system via the Couette flow $(Ay, 0, 0)$. It is proved that if the Couette flow is sufficiently strong ($A$ is large enough), then the solutions for the system are global in time in the periodic domain $(x,y,z)\in\mathbb{T}^…

Sobolev regularity for the $\bar{\partial}$--Neumann operator and transverse vector fields
Qianyun Wang, Yuan Yuan, Xu Zhang
https://arxiv.org/abs/2506.10372

Sobolev regularity for the $\bar{\partial}$--Neumann operator and transverse vector fields
On a smooth, bounded, pseudoconvex domain in $\mathbb{C}^n$ with $n >2$, inspired by the compactness conditions introduced by Yue Zhang, we present new sufficient conditions for the exact regularity of the $\overline{\partial}$--Neumann operator.

Attention-based Learning for 3D Informative Path Planning
Rui Zhao, Xingjian Zhang, Yuhong Cao, Yizhuo Wang, Guillaume Sartoretti
https://arxiv.org/abs/2506.08434

Attention-based Learning for 3D Informative Path Planning
In this work, we propose an attention-based deep reinforcement learning approach to address the adaptive informative path planning (IPP) problem in 3D space, where an aerial robot equipped with a downward-facing sensor must dynamically adjust its 3D position to balance sensing footprint and accuracy, and finally obtain a high-quality belief of an underlying field of interest over a given domain (e.g., presence of specific plants, hazardous gas, geological structures, etc.). In adaptive IPP task…

Heute vor 23 Jahren: Am 13. Juni 2002 enthüllte Vadim Logachev (Krasnoyarsk SB RAS), dass am Testgelände #Semipalatinsk radiologische Waffen erprobt wurden. Die Tests, bei denen radioaktiver Abfall abgeworfen oder gesprengt wurde, endeten 1958, da sie als unpraktikabel galten.

Bridging Continuous-time LQR and Reinforcement Learning via Gradient Flow of the Bellman Error
Armin Gie{\ss}ler, Albertus Johannes Malan, S\"oren Hohmann
https://arxiv.org/abs/2506.09685

Bridging Continuous-time LQR and Reinforcement Learning via Gradient Flow of the Bellman Error
In this paper, we present a novel method for computing the optimal feedback gain of the infinite-horizon Linear Quadratic Regulator (LQR) problem via an ordinary differential equation. We introduce a novel continuous-time Bellman error, derived from the Hamilton-Jacobi-Bellman (HJB) equation, which quantifies the suboptimality of stabilizing policies and is parametrized in terms of the feedback gain. We analyze its properties, including its effective domain, smoothness, coerciveness and show th…

This https://arxiv.org/abs/2502.02661 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

The Conformal Primon Gas at the End of Time
The Belinksy-Khalatnikov-Lifshitz dynamics of gravity close to a spacelike singularity can be mapped, at each point in space separately, onto the motion of a particle bouncing within half the fundamental domain of the modular group. We show that the semiclassical quantisation of this motion is a conformal quantum mechanics where the states are constrained to be modular invariant. Each such state defines an odd automorphic $L$-function. In particular, in a basis of dilatation eigenstates the wav…

This https://arxiv.org/abs/2504.21838 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Learning Universal User Representations Leveraging Cross-domain User Intent at Snapchat
The development of powerful user representations is a key factor in the success of recommender systems (RecSys). Online platforms employ a range of RecSys techniques to personalize user experience across diverse in-app surfaces. User representations are often learned individually through user's historical interactions within each surface and user representations across different surfaces can be shared post-hoc as auxiliary features or additional retrieval sources. While effective, such schemes …

Do-PFN: In-Context Learning for Causal Effect Estimation
Jake Robertson, Arik Reuter, Siyuan Guo, Noah Hollmann, Frank Hutter, Bernhard Sch\"olkopf
https://arxiv.org/abs/2506.06039

Do-PFN: In-Context Learning for Causal Effect Estimation
Estimation of causal effects is critical to a range of scientific disciplines. Existing methods for this task either require interventional data, knowledge about the ground truth causal graph, or rely on assumptions such as unconfoundedness, restricting their applicability in real-world settings. In the domain of tabular machine learning, Prior-data fitted networks (PFNs) have achieved state-of-the-art predictive performance, having been pre-trained on synthetic data to solve tabular prediction…

Empirical Quantification of Spurious Correlations in Malware Detection
Bianca Perasso, Ludovico Lozza, Andrea Ponte, Luca Demetrio, Luca Oneto, Fabio Roli
https://arxiv.org/abs/2506.09662

Empirical Quantification of Spurious Correlations in Malware Detection
End-to-end deep learning exhibits unmatched performance for detecting malware, but such an achievement is reached by exploiting spurious correlations -- features with high relevance at inference time, but known to be useless through domain knowledge. While previous work highlighted that deep networks mainly focus on metadata, none investigated the phenomenon further, without quantifying their impact on the decision. In this work, we deepen our understanding of how spurious correlation affects d…

Dynamic Sub-region Search in Homogeneous Collections Using CLIP
Bastian J\"ackl, Vojt\v{e}ch Kloda, Daniel A. Keim, Jakub Loko\v{c}
https://arxiv.org/abs/2506.09506

Dynamic Sub-region Search in Homogeneous Collections Using CLIP
Querying with text-image-based search engines in highly homogeneous domain-specific image collections is challenging for users, as they often struggle to provide descriptive text queries. For example, in an underwater domain, users can usually characterize entities only with abstract labels, such as corals and fish, which leads to low recall rates. Our work investigates whether recall can be improved by supplementing text queries with position information. Specifically, we explore dynamic image…

This https://arxiv.org/abs/2411.07651 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem
The Poisson compound decision problem is a long-standing problem in statistics, where empirical Bayes methodologies are commonly used to estimate Poisson's means in static or batch domains. In this paper, we study the Poisson compound decision problem in a streaming or online domain. Adopting a quasi-Bayesian approach, referred to as Newton's algorithm, we obtain a sequential estimate that is easy to evaluate, computationally efficient, and maintain a constant per-observation computational cost…

ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation
Xi Chen, Zhiqiang Shen, Peng Cao, Jinzhu Yang, Osmar R. Zaiane
https://arxiv.org/abs/2506.10675

ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation
Medical images are usually collected from multiple domains, leading to domain shifts that impair the performance of medical image segmentation models. Domain Generalization (DG) aims to address this issue by training a robust model with strong generalizability. Recently, numerous domain randomization-based DG methods have been proposed. However, these methods suffer from the following limitations: 1) constrained efficiency of domain randomization due to their exclusive dependence on image style…

This https://arxiv.org/abs/2408.08968 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Online SLA Decomposition: Enabling Real-Time Adaptation to Evolving Network Systems
When a network slice spans multiple technology domains, it is crucial for each domain to uphold the End-to-End (E2E) Service Level Agreement (SLA) associated with the slice. Consequently, the E2E SLA must be properly decomposed into partial SLAs that are assigned to each domain involved. In a network slice management system with a two-level architecture, comprising an E2E service orchestrator and local domain controllers, we consider that the orchestrator has access only to historical data rega…

Disentangling Dual-Encoder Masked Autoencoder for Respiratory Sound Classification
Peidong Wei Shiyu Miao Lin Li
https://arxiv.org/abs/2506.10698 https://

Disentangling Dual-Encoder Masked Autoencoder for Respiratory Sound Classification
Deep neural networks have been applied to audio spectrograms for respiratory sound classification, but it remains challenging to achieve satisfactory performance due to the scarcity of available data. Moreover, domain mismatch may be introduced into the trained models as a result of the respiratory sound samples being collected from various electronic stethoscopes, patient demographics, and recording environments. To tackle this issue, we proposed a modified MaskedAutoencoder(MAE) model, named …

This https://arxiv.org/abs/2505.19914 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles
Large Language Models (LLMs), such as OpenAI's o1 and DeepSeek's R1, excel at advanced reasoning tasks like math and coding via Reinforcement Learning with Verifiable Rewards (RLVR), but still struggle with puzzles solvable by humans without domain knowledge. We introduce Enigmata, the first comprehensive suite tailored for improving LLMs with puzzle reasoning skills. It includes 36 tasks across seven categories, each with 1) a generator that produces unlimited examples with controllable diffic…

This https://arxiv.org/abs/2410.08911 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Test-driven Software Experimentation with LASSO: an LLM Prompt Benchmarking Example
Empirical software engineering faces a critical gap: the lack of standardized tools for rapid development and execution of Test-Driven Software Experiments (TDSEs) -- that is, experiments that involve the execution of software subjects and the observation and analysis of their "de facto" run-time behavior. In this paper we present a general-purpose analysis platform called LASSO that provides a minimal set of domain-specific languages and data structures to conduct TDSEs. By empowering users wi…

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective
Minye Shao, Zeyu Wang, Haoran Duan, Yawen Huang, Bing Zhai, Shizheng Wang, Yang Long, Yefeng Zheng
https://arxiv.org/abs/2506.10142

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective
Precise segmentation of brain tumors, particularly contrast-enhancing regions visible in post-contrast MRI (areas highlighted by contrast agent injection), is crucial for accurate clinical diagnosis and treatment planning but remains challenging. However, current methods exhibit notable performance degradation in segmenting these enhancing brain tumor areas, largely due to insufficient consideration of MRI-specific tumor features such as complex textures and directional variations. To address t…

An optimal class of domains permitting inner boundaries in a divergence theorem for rough integrands
Thomas Ruf
https://arxiv.org/abs/2506.09978 https://…

An optimal class of domains permitting inner boundaries in a divergence theorem for rough integrands
The divergence theorem on an open domain $U \subset \mathbb{R}^m$ of finite measure is investigated for the rough class $\mathcal{DM}_\infty(U)$ of bounded vector fields $u \, \colon \, U \to \mathbb{R}^m$ with a finite Radon measure divergence. Let $\mathcal{DB}(U)$ denote the closure of $\mathcal{DM}_\infty(U)$ in the Lebesgue space $L_\infty(U)$. Equivalent conditions are proved for the domain $U$ to admit the divergence theorem \begin{equation} \exists ! \ell \in \mathcal{DB}'(U) \, \colon …

Challenging Vision-Language Models with Surgical Data: A New Dataset and Broad Benchmarking Study
Leon Mayer, Tim R\"adsch, Dominik Michael, Lucas Luttner, Amine Yamlahi, Evangelia Christodoulou, Patrick Godau, Marcel Knopp, Annika Reinke, Fiona Kolbinger, Lena Maier-Hein
https://arxiv.org/abs/2506.06232

Challenging Vision-Language Models with Surgical Data: A New Dataset and Broad Benchmarking Study
While traditional computer vision models have historically struggled to generalize to endoscopic domains, the emergence of foundation models has shown promising cross-domain performance. In this work, we present the first large-scale study assessing the capabilities of Vision Language Models (VLMs) for endoscopic tasks with a specific focus on laparoscopic surgery. Using a diverse set of state-of-the-art models, multiple surgical datasets, and extensive human reference annotations, we address t…

MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization
Ken Yagel, Eyal German, Aviel Ben Siman Tov
https://arxiv.org/abs/2506.07563

MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization
Personalized recommendation systems must adapt to user interactions across different domains. Traditional approaches like MLoRA apply a single adaptation per domain but lack flexibility in handling diverse user behaviors. To address this, we propose MoE-MLoRA, a mixture-of-experts framework where each expert is first trained independently to specialize in its domain before a gating network is trained to weight their contributions dynamically. We evaluate MoE-MLoRA across eight CTR models on Mov…

On the Ethics of Using LLMs for Offensive Security
Andreas Happe, J\"urgen Cito
https://arxiv.org/abs/2506.08693 https://arxiv.o…

On the Ethics of Using LLMs for Offensive Security
Large Language Models (LLMs) have rapidly evolved over the past few years and are currently evaluated for their efficacy within the domain of offensive cyber-security. While initial forays showcase the potential of LLMs to enhance security research, they also raise critical ethical concerns regarding the dual-use of offensive security tooling. This paper analyzes a set of papers that leverage LLMs for offensive security, focusing on how ethical considerations are expressed and justified in th…

Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gases
Axel TahmasebiMoradi, Vincent Ren, Benjamin Le-Creurer, Chetra Mang
https://arxiv.org/abs/2506.08033

Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gases
Aiming to reduce the computational cost of numerical simulations, a convolutional neural network (CNN) and a multi-layer perceptron (MLP) are introduced to build a surrogate model to approximate radiative heat transfer solutions in a 2-D walled domain with participative gases. The originality of this work lays in the adaptation of the inputs of the problem (gas and wall properties) in order to fit with the CNN architecture, more commonly used for image processing. Two precision datasets have be…

This https://arxiv.org/abs/2506.01644 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A Budgeted Multi-Level Monte Carlo Method for Full Field Estimates of Multi-PDE Problems
We present a high-performance budgeted multi-level Monte Carlo method for estimates on the entire spatial domain of multi-PDE problems with random input data. The method is designed to operate optimally within memory and CPU-time constraints and eliminates the need for a priori knowledge of the problem's regularity and the algorithm's potential memory demand. To achieve this, we build on the budgeted multi-level Monte Carlo framework and enhance it with a sparse multi-index update algorithm ope…

Heute vor 60 Jahren: Am 11.06.1965 zündeten die #USA im Rahmen von Operation Whetstone die Atombombe "Petrel". Whetstone war eine Serie von #Kernwaffentests bei der 1964/65 insgesamt 46 Bomben größtenteils im Testgebiet in

Existence of variational solutions to doubly nonlinear systems in general noncylindrical domains
Leah Sch\"atzler, Christoph Scheven, Jarkko Siltakoski, Calvin Stanko
https://arxiv.org/abs/2506.09617

Existence of variational solutions to doubly nonlinear systems in general noncylindrical domains
We consider the Cauchy-Dirichlet problem to doubly nonlinear systems of the form \begin{align*} \partial_t \big( |u|^{q-1}u \big) - \operatorname{div} \big( D_ξf(x,u,Du) \big) = - D_u f(x,u,Du) \end{align*} with $q \in (0, \infty)$ in a bounded noncylindrical domain $E \subset \mathbb{R}^{n+1}$. Further, we suppose that $x \mapsto f(x,u,ξ)$ is integrable, that $(u,ξ) \mapsto f(x,u,ξ)$ is convex, and that $f$ satisfies a $p$-growth and -coercivity condition for some $p>\max \big\…

Is Your Training Pipeline Production-Ready? A Case Study in the Healthcare Domain
Daniel Lawand (University of S\~ao Paulo), Lucas Quaresma (University of S\~ao Paulo), Roberto Bolgheroni (University of S\~ao Paulo), Alfredo Goldman (University of S\~ao Paulo), Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)

Is Your Training Pipeline Production-Ready? A Case Study in the Healthcare Domain
Deploying a Machine Learning (ML) training pipeline into production requires robust software engineering practices. This differs significantly from experimental workflows. This experience report investigates this challenge in SPIRA, a project whose goal is to create an ML-Enabled System (MLES) to pre-diagnose insufficiency respiratory via speech analysis. The first version of SPIRA's training pipeline lacked critical software quality attributes. This paper presents an overview of the MLES, then…

Heute vor 60 Jahren: Am 11.06.1965 zündeten die #USA im Rahmen von Operation Whetstone die Atombombe "Organdy". Whetstone war eine Serie von #Kernwaffentests bei der 1964/65 insgesamt 46 Bomben größtenteils im Testgebiet in

Sampling Theory for Super-Resolution with Implicit Neural Representations
Mahrokh Najaf, Gregory Ongie
https://arxiv.org/abs/2506.09949 https://

Sampling Theory for Super-Resolution with Implicit Neural Representations
Implicit neural representations (INRs) have emerged as a powerful tool for solving inverse problems in computer vision and computational imaging. INRs represent images as continuous domain functions realized by a neural network taking spatial coordinates as inputs. However, unlike traditional pixel representations, little is known about the sample complexity of estimating images using INRs in the context of linear inverse problems. Towards this end, we study the sampling requirements for recove…

Toward Low-Altitude Airspace Management and UAV Operations: Requirements, Architecture and Enabling Technologies
Guiyang Luo, Jinglin Li, Qixun Zhang, Zhiyong Feng, Quan Yuan, Yijing Lin, Hui Zhang, Nan Cheng, Ping Zhang
https://arxiv.org/abs/2506.08579

Toward Low-Altitude Airspace Management and UAV Operations: Requirements, Architecture and Enabling Technologies
The low-altitude economy (LAE) is rapidly advancing toward intelligence, connectivity, and coordination, bringing new challenges in dynamic airspace management, unmanned aerial vehicle (UAV) operation, and security management. Existing systems remain fragmented and lack effective coordination. To bridge these gaps, we propose UTICN (Ubiquitous and Trusted Intelligent Cellular-native Network) for LAE, a unified cellular-native architecture that integrates multi-domain sensing, high-precision pos…

HotelMatch-LLM: Joint Multi-Task Training of Small and Large Language Models for Efficient Multimodal Hotel Retrieval
Arian Askari, Emmanouil Stergiadis, Ilya Gusev, Moran Beladev
https://arxiv.org/abs/2506.07296

HotelMatch-LLM: Joint Multi-Task Training of Small and Large Language Models for Efficient Multimodal Hotel Retrieval
We present HotelMatch-LLM, a multimodal dense retrieval model for the travel domain that enables natural language property search, addressing the limitations of traditional travel search engines which require users to start with a destination and editing search parameters. HotelMatch-LLM features three key innovations: (1) Domain-specific multi-task optimization with three novel retrieval, visual, and language modeling objectives; (2) Asymmetrical dense retrieval architecture combining a small …

Heute vor 61 Jahren: Am 11. Juni 1964 zündeten die USA im Rahmen von #Niblick die 39. #Atombombe "Ace" (Operation Plowshare, zivile Nutzbarmachung von Nuklearexplosionen). Niblick war eine Serie von Tests zwischen 63/64 bei der insgesamt 41

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new/
[1/1]:
Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub

Structured Semantics from Unstructured Notes: Language Model Approaches to EHR-Based Decision Support
Wu Hao Ran, Xi Xi, Furong Li, Jingyi Lu, Jian Jiang, Hui Huang, Yuzhuan Zhang, Shi Li
https://arxiv.org/abs/2506.06340

Structured Semantics from Unstructured Notes: Language Model Approaches to EHR-Based Decision Support
The advent of large language models (LLMs) has opened new avenues for analyzing complex, unstructured data, particularly within the medical domain. Electronic Health Records (EHRs) contain a wealth of information in various formats, including free text clinical notes, structured lab results, and diagnostic codes. This paper explores the application of advanced language models to leverage these diverse data sources for improved clinical decision support. We will discuss how text-based features, …

Enhancing Synthetic CT from CBCT via Multimodal Fusion: A Study on the Impact of CBCT Quality and Alignment
Maximilian Tschuchnig, Lukas Lamminger, Philipp Steininger, Michael Gadermayr
https://arxiv.org/abs/2506.08716

Enhancing Synthetic CT from CBCT via Multimodal Fusion: A Study on the Impact of CBCT Quality and Alignment
Cone-Beam Computed Tomography (CBCT) is widely used for real-time intraoperative imaging due to its low radiation dose and high acquisition speed. However, despite its high resolution, CBCT suffers from significant artifacts and thereby lower visual quality, compared to conventional Computed Tomography (CT). A recent approach to mitigate these artifacts is synthetic CT (sCT) generation, translating CBCT volumes into the CT domain. In this work, we enhance sCT generation through multimodal learn…

MLOps with Microservices: A Case Study on the Maritime Domain
Renato Cordeiro Ferreira (Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University), Rowanne Trapmann (Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University), Willem-Jan van den Heuvel (Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)

MLOps with Microservices: A Case Study on the Maritime Domain
This case study describes challenges and lessons learned on building Ocean Guard: a Machine Learning-Enabled System (MLES) for anomaly detection in the maritime domain. First, the paper presents the system's specification, and architecture. Ocean Guard was designed with a microservices' architecture to enable multiple teams to work on the project in parallel. Then, the paper discusses how the developers adapted contract-based design to MLOps for achieving that goal. As a MLES, Ocean Guard emplo…

Heute vor 59 Jahren: Am 10. Juni 1966 zündeten die USA im Rahmen von Operation #Flintlock die 45. #Atombombe "Puce". Flintlock war eine Serie von Tests zwischen 65/66 bei der insgesamt 47 #Kernwaffentests

Optimal Transport Driven Asymmetric Image-to-Image Translation for Nuclei Segmentation of Histological Images
Suman Mahapatra, Pradipta Maji
https://arxiv.org/abs/2506.07023

Optimal Transport Driven Asymmetric Image-to-Image Translation for Nuclei Segmentation of Histological Images
Segmentation of nuclei regions from histological images enables morphometric analysis of nuclei structures, which in turn helps in the detection and diagnosis of diseases under consideration. To develop a nuclei segmentation algorithm, applicable to different types of target domain representations, image-to-image translation networks can be considered as they are invariant to target domain image representations. One of the important issues with image-to-image translation models is that they fai…