Tootfinder

You want to customize the settings, right? Of course you do. Thats why you need settings for the settings.
But…what if you want to customer the •customizer•???
The why we invented Settings Settings Settings. Only from Apple. https://mastodon.social/@_inside/114655449845163418

New functionality for #Jellyfin for #Roku.
I've added new settings that allow you to select which sections to show on the home screen.
By default, the app uses your settings from the web client, but that limits your ability to set where you want to display the My List and Favorit…

Great opportunity to show people the accessibility settings.
https://mastodon.social/@andjsch/114655906181034852

Andreas Schantl (@andjsch@mastodon.social)
Attached: 2 images Just installed the iOS 26 Beta for a test drive on my spare iPhone and while the glassy look is fancy to some degree, it makes things poorly visible. I am sure a lot of people will have troubles reading text on such transparent layers and generally finding small icons with bad contrast. #ios26 #apple #wwdc25 #accessibility

Actually, how come the cookie settings are per website and not set once and for all by one's browser (with possibility for exceptions)?
#Internet #Cookies

Emergent misalignment as prompt sensitivity: A research note
Tim Wyse, Twm Stone, Anna Soligo, Daniel Tan
https://arxiv.org/abs/2507.06253 https://

Emergent misalignment as prompt sensitivity: A research note
Betley et al. (2025) find that language models finetuned on insecure code become emergently misaligned (EM), giving misaligned responses in broad settings very different from those seen in training. However, it remains unclear as to why emergent misalignment occurs. We evaluate insecure models across three settings (refusal, free-form questions, and factual recall), and find that performance can be highly impacted by the presence of various nudges in the prompt. In the refusal and free-form q…

In its ruling letting Trump bar AP, a US appeals court says small settings like the Oval Office are essentially invite-only and not covered by 1A protections (New York Times)
https://www.nytimes.com/2025/06/06/us/politics/appeals-court-asso…

Trump Can Restrict A.P. Journalists’ Access, Appeals Court Rules
By a 2-to-1 vote, a three-judge panel found that the president can bar the news outlet from small settings such as the Oval Office or Air Force One, reversing at least for now a lower court’s ruling.

Real-Time AI-Driven Pipeline for Automated Medical Study Content Generation in Low-Resource Settings: A Kenyan Case Study
Emmanuel Korir, Eugene Wechuli
https://arxiv.org/abs/2507.05212

Real-Time AI-Driven Pipeline for Automated Medical Study Content Generation in Low-Resource Settings: A Kenyan Case Study
Juvenotes is a real-time AI-driven pipeline that automates the transformation of academic documents into structured exam-style question banks, optimized for low-resource medical education settings in Kenya. The system combines Azure Document Intelligence for OCR and Azure AI Foundry (OpenAI o3-mini) for question and answer generation in a microservices architecture, with a Vue/TypeScript frontend and AdonisJS backend. Mobile-first design, bandwidth-sensitive interfaces, institutional tagging, a…

Learning To Communicate Over An Unknown Shared Network
Shivangi Agarwal, Adi Asija, Sanjit K. Kaul, Arani Bhattacharya, Saket Anand
https://arxiv.org/abs/2507.06499

Learning To Communicate Over An Unknown Shared Network
As robots (edge-devices, agents) find uses in an increasing number of settings and edge-cloud resources become pervasive, wireless networks will often be shared by flows of data traffic that result from communication between agents and corresponding edge-cloud. In such settings, agent communicating with the edge-cloud is unaware of state of network resource, which evolves in response to not just agent's own communication at any given time but also to communication by other agents, which stays u…

Epistemically-guided forward-backward exploration
N\'uria Armengol Urp\'i, Marin Vlastelica, Georg Martius, Stelian Coros
https://arxiv.org/abs/2507.05477

Epistemically-guided forward-backward exploration
Zero-shot reinforcement learning is necessary for extracting optimal policies in absence of concrete rewards for fast adaptation to future problem settings. Forward-backward representations (FB) have emerged as a promising method for learning optimal policies in absence of rewards via a factorization of the policy occupancy measure. However, up until now, FB and many similar zero-shot reinforcement learning algorithms have been decoupled from the exploration problem, generally relying on other …

OK, this feels deliberate now. Updated my Mac laptop and that also caused settings to be wiped. WTF?

MST-Distill: Mixture of Specialized Teachers for Cross-Modal Knowledge Distillation
Hui Li, Pengfei Yang, Juanyang Chen, Le Dong, Yanxin Chen, Quan Wang
https://arxiv.org/abs/2507.07015

MST-Distill: Mixture of Specialized Teachers for Cross-Modal Knowledge Distillation
Knowledge distillation as an efficient knowledge transfer technique, has achieved remarkable success in unimodal scenarios. However, in cross-modal settings, conventional distillation methods encounter significant challenges due to data and statistical heterogeneities, failing to leverage the complementary prior knowledge embedded in cross-modal teacher models. This paper empirically reveals two critical issues in existing approaches: distillation path selection and knowledge drift. To address …

Bluesky rolls out Activity Notifications, letting users receive notifications of new posts and replies from selected accounts, as it expands its sports presence (Richard Lawler/The Verge)
https://www.theverge.com/news/699671/bluesky-push-n…

Bluesky can really keep up with the news now that it has activity notifications
Bluesky’s latest update added more notification settings, as well as activity notifications to see whenever certain accounts have a new post.

SCoRE: Streamlined Corpus-based Relation Extraction using Multi-Label Contrastive Learning and Bayesian kNN
Luca Mariotti, Veronica Guidetti, Federica Mandreoli
https://arxiv.org/abs/2507.06895

SCoRE: Streamlined Corpus-based Relation Extraction using Multi-Label Contrastive Learning and Bayesian kNN
The growing demand for efficient knowledge graph (KG) enrichment leveraging external corpora has intensified interest in relation extraction (RE), particularly under low-supervision settings. To address the need for adaptable and noise-resilient RE solutions that integrate seamlessly with pre-trained large language models (PLMs), we introduce SCoRE, a modular and cost-effective sentence-level RE system. SCoRE enables easy PLM switching, requires no finetuning, and adapts smoothly to diverse cor…

Conformal Prediction for Long-Tailed Classification
Tiffany Ding, Jean-Baptiste Fermanian, Joseph Salmon
https://arxiv.org/abs/2507.06867 https://

Conformal Prediction for Long-Tailed Classification
Many real-world classification problems, such as plant identification, have extremely long-tailed class distributions. In order for prediction sets to be useful in such settings, they should (i) provide good class-conditional coverage, ensuring that rare classes are not systematically omitted from the prediction sets, and (ii) be a reasonable size, allowing users to easily verify candidate labels. Unfortunately, existing conformal prediction methods, when applied to the long-tailed setting, for…

DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation
Young Hun Kim, Seungyeon Kim, Yonghyeon Lee, Frank Chongwoo Park
https://arxiv.org/abs/2507.05627

DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation
Partial-view 3D recognition -- reconstructing 3D geometry and identifying object instances from a few sparse RGB images -- is an exceptionally challenging yet practically essential task, particularly in cluttered, occluded real-world settings where full-view or reliable depth data are often unavailable. Existing methods, whether based on strong symmetry priors or supervised learning on curated datasets, fail to generalize to such scenarios. In this work, we introduce DreamGrasp, a framework tha…

Toward Neurodivergent-Aware Productivity: A Systems and AI-Based Human-in-the-Loop Framework for ADHD-Affected Professionals
Raghavendra Deshmukh
https://arxiv.org/abs/2507.06864 …

Toward Neurodivergent-Aware Productivity: A Systems and AI-Based Human-in-the-Loop Framework for ADHD-Affected Professionals
Digital work environments in IT and knowledge-based sectors demand high levels of attention management, task juggling, and self-regulation. For adults with ADHD, these settings often amplify challenges such as time blindness, digital distraction, emotional reactivity, and executive dysfunction. These individuals prefer low-touch, easy-to-use interventions for daily tasks. Conventional productivity tools often fail to support the cognitive variability and overload experienced by neurodivergent p…

Implicit Neural Representation-Based MRI Reconstruction Method with Sensitivity Map Constraints
Lixuan Rao, Xinlin Zhang, Yiman Huang, Tao Tan, Tong Tong
https://arxiv.org/abs/2506.06043

Implicit Neural Representation-Based MRI Reconstruction Method with Sensitivity Map Constraints
Magnetic Resonance Imaging (MRI) is a widely utilized diagnostic tool in clinical settings, but its application is limited by the relatively long acquisition time. As a result, fast MRI reconstruction has become a significant area of research. In recent years, Implicit Neural Representation (INR), as a scan-specific method, has demonstrated outstanding performance in fast MRI reconstruction without fully-sampled images for training. High acceleration reconstruction poses a challenging problem, …

Stochastic Moving Anchor Algorithms and a Popov's Scheme with Moving Anchor
James Alcala, Yat Tin Chow, Mahesh Sunkula
https://arxiv.org/abs/2506.07290

Stochastic Moving Anchor Algorithms and a Popov's Scheme with Moving Anchor
Since their introduction, anchoring methods in extragradient-type saddlepoint problems have inspired a flurry of research due to their ability to provide order-optimal rates of accelerated convergence in very general problem settings. Such guarantees are especially important as researchers consider problems in artificial intelligence (AI) and machine learning (ML), where large problem sizes demand immense computational power. Much of the more recent works explore theoretical aspects of this new…

Inward and Outward Spillover Effects of One Unit's Treatment on Network Neighbors under Partial Interference
Fei Fang, Edoardo M Airoldi, Laura Forastiere
https://arxiv.org/abs/2506.06615

Inward and Outward Spillover Effects of One Unit's Treatment on Network Neighbors under Partial Interference
In settings where interference is present, direct effects are commonly defined as the average effect of a unit's treatment on their own outcome while fixing the treatment status or probability among interfering units, and spillover effects measure the average effect of a change in the latter while the individual's treatment status is kept fixed. Here, we define the average causal effect of a unit's treatment status on the outcome of their network neighbors, while fixing the treatment probabilit…

Oh my goodness! You can enable login and logout sounds in KDE Plasma! 🎶
The Oxygen sound theme has such a nice jingle, too. It reminds me of the good old days. I'm keeping that.
You can enable it in Settings − Apps & Windows − Notifications − Login − Play a sound (See screenshot)
You can set a custom sound file, but a file from your current sound theme should already be default if there is one.

Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systems
Armin Lederer, Erfaun Noorani, Andreas Krause
https://arxiv.org/abs/2506.07347

Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systems
Ensuring safety in multi-agent systems is a significant challenge, particularly in settings where centralized coordination is impractical. In this work, we propose a novel risk-sensitive safety filter for discrete-time multi-agent systems with uncertain dynamics that leverages control barrier functions (CBFs) defined through value functions. Our approach relies on centralized risk-sensitive safety conditions based on exponential risk operators to ensure robustness against model uncertainties. W…

High Frequency Quoting Under Liquidity Constraints
Aditya Nittur Anantha, Shashi Jain, Shivam Goyal, Dhruv Misra
https://arxiv.org/abs/2507.05749 https://

High Frequency Quoting Under Liquidity Constraints
Quoting algorithms are fundamental to electronic trading systems, enabling participants to post limit orders in a systematic and adaptive manner. In multi-asset or multi-contract settings, selecting the appropriate reference instrument for pricing quotes is essential to managing execution risk and minimizing trading costs. This work presents a framework for reference selection based on predictive modeling of short-term price stability. We employ multivariate Hawkes processes to model the tempor…

If you're trying to do the trick that was shared for #nintendoswitch2 where you can use a USB HDMI capture dongle HDMI to USB-C cable to use your iPhone as a camera - don't use the True Visage app mentioned in the video
That will rely on screen sharing mirroring and you'll potentially get an aspect ratio mismatch and see the dynamic island floating in your view
I'd personally suggest the free "Blackmagic Camera" app instead, and then go to:
Settings->Monitor->HDMI Out
Switch from Mirror Display to Video Feed and it will cleanly output your camera. Works for a Lightning to HDMI out adapter for older phones, too
Then just tap on Lense and FPS and pick which combo you like.
Also, if you're not getting any output signal make sure the capture dongle you're using supports 1080p out - not sure the Switch 2 supports anything higher as camera input

UAV-Based Remote Sensing of Soil Moisture Across Diverse Land Covers: Validation and Bayesian Uncertainty Characterization
Runze Zhang, Ishfaq Aziz, Derek Houtz, Yuxiang Zhao, Trent W. Ford, Adam C. Watts, Mohamad Alipour
https://arxiv.org/abs/2506.05572

UAV-Based Remote Sensing of Soil Moisture Across Diverse Land Covers: Validation and Bayesian Uncertainty Characterization
High-resolution soil moisture (SM) observations are critical for agricultural monitoring, forestry management, and hazard prediction, yet current satellite passive microwave missions cannot directly provide retrievals at tens-of-meter spatial scales. Unmanned aerial vehicle (UAV) mounted microwave radiometry presents a promising alternative, but most evaluations to date have focused on agricultural settings, with limited exploration across other land covers and few efforts to quantify retrieval…

High-Dimensional Unfolding in Large Backgrounds
Alexandre Falc\~ao, Adam Takacs
https://arxiv.org/abs/2507.06291 https://arxiv.org/pd…

High-Dimensional Unfolding in Large Backgrounds
We propose new methodologies in multi-dimensional unfolding in dense environments, and show that incorporating auxiliary observables can significantly improve performance. Our approach builds on the ML-based OmniFold algorithm, which we extend to account for background, detector acceptance, efficiency, and uncertainties, enabling its application in high-luminosity and heavy-ion collision settings. We derive this algorithm and demonstrate its mathematical and numerical equivalence to expectation…

So, if you want to start qBittorrent hidden in the system tray (v5.1.2, KDE), you must:
* check "Minimise qBittorrent to notification area" in Settings/Behaviour (which, oddly, doesn't hide it from the taskbar)
* open ~/config/qbittorrent/qBittorrent.conf and set StartUpWindowState to Minimized

This morning I added support for using percentage line properties in subtitle style data to #Jellyfin for #Roku.

We’re closing in on this month’s release of OmniFocus 4.7, with planned dates, mutually-exclusive tags, and improved repeats!
Updated test builds are now available, adding new settings for Forecast counts and summary dots, adding support for keyboard modifiers when using drag-and-drop in Forecast on iPad and visionOS (bringing parity with the support already on Mac), and making other improvements and bug fixes.

Canine Clinical Gait Analysis for Orthopedic and Neurological Disorders: An Inertial Deep-Learning Approach
Netta Palez, L\'eonie Stra{\ss}, Sebastian Meller, Holger Volk, Anna Zamansky, Itzik Klein
https://arxiv.org/abs/2507.05671

Canine Clinical Gait Analysis for Orthopedic and Neurological Disorders: An Inertial Deep-Learning Approach
Canine gait analysis using wearable inertial sensors is gaining attention in veterinary clinical settings, as it provides valuable insights into a range of mobility impairments. Neurological and orthopedic conditions cannot always be easily distinguished even by experienced clinicians. The current study explored and developed a deep learning approach using inertial sensor readings to assess whether neurological and orthopedic gait could facilitate gait analysis. Our investigation focused on opt…

Explaining Surface Layer Theory Departures in Marine Flux Profiles with Data-Driven Discovery
Jack Foxabbott, Leo Mckee-Reid, Andrew Cusick, Robbie McCorkell, Jugal Patel, Jamie Rumbelow, Jessica Rumbelow, Zohreh Shams, Arush Tagade, Patrick Hawbecker, Sue Ellen Haupt
https://arxiv.org/abs/2507.06425

Explaining Surface Layer Theory Departures in Marine Flux Profiles with Data-Driven Discovery
Monin--Obukhov Similarity Theory (MOST), which underpins nearly all bulk estimates of surface fluxes in the atmospheric surface layer, assumes monotonic wind profiles and vertically uniform momentum and heat fluxes. Here, we show that conditions frequently arise in coastal marine settings where these assumptions do not hold. Using flux measurements from the Coastal Land-Air-Sea Interaction (CLASI) project's Air-Sea Interaction Spar (ASIS) buoys with wind and flux measurements at typically ~3m a…

Irregular traces of multiple SLE(0) systems with multiple marked points
Jiaxin Zhang
https://arxiv.org/abs/2506.07513 https://arxiv.o…

Irregular traces of multiple SLE(0) systems with multiple marked points
In this supplementary note, we study the traces of multiple SLE(0) systems with two or more additional marked points. For general chordal configurations, the traces correspond to the real locus of real rational functions; in the radial case, they correspond to the horizontal trajectories of residue-free quadratic differentials. In both settings, we establish the regularity of the trajectories near singularities: no spiraling occurs, and no two trajectories asymptotically converge to the same …

If you wonder how much disk space you need to run your own Mastodon instance, here are our stats of a server with a handful of active accounts:
Attachments: 8.51 GB (462 MB local)
Custom emoji: 477 MB (74.8 KB local)
Preview cards: 457 MB
Avatars: 4.29 GB (1.89 MB local)
Headers: 7.57 GB (3.58 MB local)
Backups: 0 Bytes
Imports: 0 Bytes
Settings: 34.4 KB
Disk usage is pretty stable after a while. We do backups with our own mechanism, so YMMV.

Can Contextualized Physics Problems Enhance Student Motivation? Uncovering a Student-Teacher Perception Gap through a Large-Scale Survey
Yajun Wei, Xinting Peng, Yi Zhong, Feipeng Pi, Yanfang Zhai, Lei Bao
https://arxiv.org/abs/2507.05586

Can Contextualized Physics Problems Enhance Student Motivation? Uncovering a Student-Teacher Perception Gap through a Large-Scale Survey
Embedding physics problems unreal-world settings, here termed contextualized physics problems (CPP), is widely believed to foster students' interest, motivation, and learning. However, firm evidence for this claim remains scarce. To explore this issue, we surveyed 868 secondary students and 154 teachers to examine their attitudes toward CPP and investigate whether students and teachers perceive these problems as promoting student interest and motivation in learning physics. The findings reveal …

Non-asymptotic confidence regions on RKHS. The Paley-Wiener and standard Sobolev space cases
Fabrice Gamboa (IMT, RT-UQ, ANITI), Olivier Roustant (IMT, INSA Toulouse, RT-UQ, ANITI)
https://arxiv.org/abs/2507.06657

Non-asymptotic confidence regions on RKHS. The Paley-Wiener and standard Sobolev space cases
We consider the problem of constructing a global, probabilistic, and non-asymptotic confidence region for an unknown function observed on a random design. The unknown function is assumed to lie in a reproducing kernel Hilbert space (RKHS). We show that this construction can be reduced to accurately estimating the RKHS norm of the unknown function. Our analysis primarily focuses both on the Paley-Wiener and on the standard Sobolev space settings.

@CoMaps@floss.social
The settings offer the option to use ‘Google Fused Location Services’. I assume this is so that this Google service can still be used even if it is disabled in the Android settings. @… offers an alternative #locationService

KAConvText: Novel Approach to Burmese Sentence Classification using Kolmogorov-Arnold Convolution
Ye Kyaw Thu, Thura Aung, Thazin Myint Oo, Thepchai Supnithi
https://arxiv.org/abs/2507.06753

KAConvText: Novel Approach to Burmese Sentence Classification using Kolmogorov-Arnold Convolution
This paper presents the first application of Kolmogorov-Arnold Convolution for Text (KAConvText) in sentence classification, addressing three tasks: imbalanced binary hate speech detection, balanced multiclass news classification, and imbalanced multiclass ethnic language identification. We investigate various embedding configurations, comparing random to fastText embeddings in both static and fine-tuned settings, with embedding dimensions of 100 and 300 using CBOW and Skip-gram models. Baselin…

You might want to fine-tune your notification settings:
“Apple Gave Governments Data on Thousands of Push Notifications” - https://www.404media.co/apple-gave-governments-data-on-thousands-of-push-notifications/

Apple Gave Governments Data on Thousands of Push Notifications
Push notification data can sometimes include the unencrypted content of notifications. Requests include from the U.S., U.K., Germany, and Israel.

Frage an alle #GitLab Admins: gibt es eine Möglichkeit, einen User automatisch auf alle Projekte Instanz-weit in GitLab self hosted zuzulassen?
Für eine Überprüfung reicht ein Audit User nicht aus und ich möchte das so weit wir möglich automatisieren, auch für neue Projekte.

Post-Processing in Local Differential Privacy: An Extensive Evaluation and Benchmark Platform
Alireza Khodaie, Berkay Kemal Balioglu, Mehmet Emre Gursoy
https://arxiv.org/abs/2507.05875

Post-Processing in Local Differential Privacy: An Extensive Evaluation and Benchmark Platform
Local differential privacy (LDP) has recently gained prominence as a powerful paradigm for collecting and analyzing sensitive data from users' devices. However, the inherent perturbation added by LDP protocols reduces the utility of the collected data. To mitigate this issue, several post-processing (PP) methods have been developed. Yet, the comparative performance of PP methods under diverse settings remains underexplored. In this paper, we present an extensive benchmark comprising 6 popular L…

Continual Recommender Systems
Hyunsik Yoo, SeongKu Kang, Hanghang Tong
https://arxiv.org/abs/2507.03861 https://arxiv.org/pdf/2507.03…

Continual Recommender Systems
Modern recommender systems operate in uniquely dynamic settings: user interests, item pools, and popularity trends shift continuously, and models must adapt in real time without forgetting past preferences. While existing tutorials on continual or lifelong learning cover broad machine learning domains (e.g., vision and graphs), they do not address recommendation-specific demands-such as balancing stability and plasticity per user, handling cold-start items, and optimizing recommendation metrics…

Adaptive Parallel Downloader for Large Genomic Datasets
Rasman Mubtasim Swargo, Engin Arslan, Md Arifuzzaman
https://arxiv.org/abs/2508.05511 https://arxiv…

Adaptive Parallel Downloader for Large Genomic Datasets
Modern next-generation sequencing (NGS) projects routinely generate terabytes of data, which researchers commonly download from public repositories such as SRA or ENA. Existing download tools often employ static concurrency settings, leading to inefficient bandwidth utilization and prolonged download times due to their inability to adapt to dynamic network conditions. We introduce FastBioDL, a parallel file downloader designed for large biological datasets, featuring an adaptive concurrency con…

Beating the Best Constant Rebalancing Portfolio in Long-Term Investment: A Generalization of the Kelly Criterion and Universal Learning Algorithm for Markets with Serial Dependence
Duy Khanh Lam
https://arxiv.org/abs/2507.05994

Beating the Best Constant Rebalancing Portfolio in Long-Term Investment: A Generalization of the Kelly Criterion and Universal Learning Algorithm for Markets with Serial Dependence
In the online portfolio optimization framework, existing learning algorithms generate strategies that yield significantly poorer cumulative wealth compared to the best constant rebalancing portfolio in hindsight, despite being consistent in asymptotic growth rate. While this unappealing performance can be improved by incorporating more side information, it raises difficulties in feature selection and high-dimensional settings. Instead, the inherent serial dependence of assets' returns, such as …

Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning
Yangui Fang, Jing Peng, Xu Li, Yu Xi, Chengwei Zhang, Guohui Zhong, Kai Yu
https://arxiv.org/abs/2506.05671

Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning
Recent advances in automatic speech recognition (ASR) have combined speech encoders with large language models (LLMs) through projection, forming Speech LLMs with strong performance. However, adapting them to new domains remains challenging, especially in low-resource settings where paired speech-text data is scarce. We propose a text-only fine-tuning strategy for Speech LLMs using unpaired target-domain text without requiring additional audio. To preserve speech-text alignment, we introduce a …

Bluesky rolls out Activity Notifications, letting users receive notifications of new posts and replies from selected accounts, as it expands its sports presence (Richard Lawler/The Verge)
https://www.theverge.com/news/699671/bluesky-push-n…

Bluesky can really keep up with the news now that it has activity notifications
Bluesky’s latest update added more notification settings, as well as activity notifications to see whenever certain accounts have a new post.

Maximizing the Margin between Desirable and Undesirable Elements in a Covering Problem
Sophie Boileau, Andrew Hong, David Liben-Nowell, Alistair Pattison, Anna N. Rafferty, Charlie Roslansky
https://arxiv.org/abs/2507.03817

Maximizing the Margin between Desirable and Undesirable Elements in a Covering Problem
In many covering settings, it is natural to consider the simultaneous presence of desirable elements (that we seek to include) and undesirable elements (that we seek to avoid). This paper introduces a novel combinatorial problem formalizing this tradeoff: from a collection of sets containing both "desirable" and "undesirable" items, pick the subcollection that maximizes the margin between the number of desirable and undesirable elements covered. We call this the Target Approximation Problem (TA…

OneDB: A Distributed Multi-Metric Data Similarity Search System
Tang Qian, Yifan Zhu, Lu Chen, Xiangyu Ke, Jingwen Zhao, Tianyi Li, Yunjun Gao, Christian S. Jensen
https://arxiv.org/abs/2507.04256

OneDB: A Distributed Multi-Metric Data Similarity Search System
Increasingly massive volumes of multi-modal data are being accumulated in many {real world} settings, including in health care and e-commerce. This development calls for effective general-purpose data management solutions for multi-modal data. Such a solution must facilitate user-friendly and accurate retrieval of any multi-modal data according to diverse application requirements. Further, such a solution must be capable of efficient and scalable retrieval. To address this need, we present On…

Listened in to the Academy Software Foundation's virtual townhall about the Open Review Initiative. I knew that project because they provide very useful ffmpeg command lines and they're doing all the encoding tests to figure out good quality settings and obscure parameters.
Learnings from the presentation: prores by ffmpeg is sadly pretty crappy compared to the real prores from Apple. There's the OpenAPV codec that is gearing up to be an open-source alternative:

🎉 New Kitten¹ Release: A little housekeeping 🧹
Today’s release only concerns production servers:
• Kitten no longer counts all *hits* in its stats. You can still see which of your *pages* are most popular, etc., and see stats for missing URLs, etc., as before from either the web interface or the interactive shell, but not every hit is logged. Instead, you can see the latest 25 served routes in Kitten’s Settings (at /🐱/settings/state/requests/ via the web on your server).
- …

app/CHANGELOG.md at main
app - A web development kit that’s small, purrs, and loves you.

Optimized projection-free algorithms for online learning: construction and worst-case analysis
Julien Weibel (SIERRA), Pierre Gaillard (Thoth), Wouter M. Koolen (CWI), Adrien Taylor (SIERRA)
https://arxiv.org/abs/2506.05855

Optimized projection-free algorithms for online learning: construction and worst-case analysis
This work studies and develop projection-free algorithms for online learning with linear optimization oracles (a.k.a. Frank-Wolfe) for handling the constraint set. More precisely, this work (i) provides an improved (optimized) variant of an online Frank-Wolfe algorithm along with its conceptually simple potential-based proof, and (ii) shows how to leverage semidefinite programming to jointly design and analyze online Frank-Wolfe-type algorithms numerically in a variety of settings-that include …

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…

Bonded braids and the Markov theorem
Paolo Cavicchioli, Bo\v{s}tjan Gabrov\v{s}ek, Matic Simoni\v{c}
https://arxiv.org/abs/2507.04565 https://

Bonded braids and the Markov theorem
Bonded knots arise naturally in topological protein modeling, where intramolecular interactions such as disulfide bridges stabilize folded configurations. These structures extend classical knot theory by incorporating embedded graphs, and have been formalized as bonded knots. In this paper, we develop the algebraic theory of bonded braids, introducing the bonded braid monoid in the topological and rigid settings, which encodes both classical braid crossings and (rigid) bonded connections. We pr…

Permutations accelerate Approximate Bayesian Computation
Antoine Luciano, Charly Andral, Christian P. Robert, Robin J. Ryder
https://arxiv.org/abs/2507.06037

Permutations accelerate Approximate Bayesian Computation
Approximate Bayesian Computation (ABC) methods have become essential tools for performing inference when likelihood functions are intractable or computationally prohibitive. However, their scalability remains a major challenge in hierarchical or high-dimensional models. In this paper, we introduce permABC, a new ABC framework designed for settings with both global and local parameters, where observations are grouped into exchangeable compartments. Building upon the Sequential Monte Carlo ABC …

There was a time when, if you were lucky enough to get a piece of software stable and working well, it continued to be stable and work well.
Now we have relentless updates, whose problems are exemplified by an overnight update of my Mac to OS X 15.6 doing a factory reset of the settings of many of my programs.
Let's not even go into the behavioral changes that get pushed that invalidate all your muscle memory in order to boost some product manager's performance review.
I like stability, but it's just another casualty of this endless churn.

#EroticMusings Week 6 (July 6-12) Setting: Do you prefer to work with constrained settings or expansive? Revisit locations or move on?
Expansive locations, with some recurring. I want to evoke a sense of familiarity and homecoming - describing a place for the second, third, fourth time giving the reader a cozy feeling of having been there so often, that every part of the place is kn…

today's "I can't believe I wasted half a hour on this" computer issue: whether Wireshark validates the Ethernet checksum in packets depends on the capture format you're using
.snoop: nope, and ignores all settings including "always check FCS"
.pcap: yep
saving a .snoop file as a .pcap file makes it immediately check all FCS

The Spatial Regime Conversion Method
Charles G. Cameron, Cameron A. Smith, Christian A. Yates
https://arxiv.org/abs/2507.04937 https://

The Spatial Regime Conversion Method
We present the spatial regime conversion method (SRCM), a novel hybrid modelling framework for simulating reaction-diffusion systems that adaptively combines stochastic discrete and deterministic continuum representations. Extending the regime conversion method (RCM) to spatial settings, the SRCM employs a discrete reaction-diffusion master equation (RDME) representation in regions of low concentration and continuum partial differential equations (PDEs) where concentrations are high, dynamicall…

Neural Unfolding of the Chiral Magnetic Effect in Heavy-Ion Collisions
Shuang Guo, Lingxiao Wang, Kai Zhou, Guo-Liang Ma
https://arxiv.org/abs/2507.05808 h…

Neural Unfolding of the Chiral Magnetic Effect in Heavy-Ion Collisions
The search for the chiral magnetic effect (CME) in relativistic heavy-ion collisions (HICs) is challenged by significant background contamination. We present a novel deep learning approach based on a U-Net architecture to time-reversely unfold CME dynamics, enabling the reconstruction of the CME signal across the entire evolution of HICs. Trained on the events simulated by a multi-phase transport model with different cases of CME settings, our model learns to recover the CME-induced charge sepa…

Causal Mediation in Natural Experiments
Senan Hogan-Hennessy
https://arxiv.org/abs/2508.05449 https://arxiv.org/pdf/2508.05449

Causal Mediation in Natural Experiments
Natural experiments are a cornerstone of applied economics, providing settings for estimating causal effects with a compelling argument for treatment randomisation, but give little indication of the mechanisms behind causal effects. Causal Mediation (CM) provides a framework to analyse mechanisms by identifying the average direct and indirect effects (CM effects), yet conventional CM methods require the relevant mediator is as-good-as-randomly assigned. When people choose the mediator based on …

I wrote a blog post about my experience installing #Meshcore on my #Heltec V3 device and the settings you need when you are in Canada.
This is not an intro blog post to mesh in general, this assume existing knowledge about

Modeling human reputation-seeking behavior in a spatio-temporally complex public good provision game
Edward Hughes, Tina O. Zhu, Martin J. Chadwick, Raphael Koster, Antonio Garc\'ia Casta\~neda, Charles Beattie, Thore Graepel, Matthew M. Botvinick, Joel Z. Leibo
https://arxiv.org/abs/2506.06032…

Modeling human reputation-seeking behavior in a spatio-temporally complex public good provision game
Multi-agent reinforcement learning algorithms are useful for simulating social behavior in settings that are too complex for other theoretical approaches like game theory. However, they have not yet been empirically supported by laboratory experiments with real human participants. In this work we demonstrate how multi-agent reinforcement learning can model group behavior in a spatially and temporally complex public good provision game called Clean Up. We show that human groups succeed in Clean …

Looks like I have this to investigate now because a warning is appearing on my admin dashboard. Whatever the problem is seems to affect Elasticsearch 7.17.23 and Mastodon 4.4.2 , because this never appeared on Mastodon 4.3.x and the suggested command only returns this error:
[400] {"error":{"root_cause":[{"type":"illegal_argument_exception","reason":"unknown setting [index.analysis] please check that any required plugins are installed, or check the breaking changes documentation for removed settings"}],"type":"illegal_argument_exception","reason":"unknown setting [index.analysis] please check that any required plugins are installed, or check the breaking changes documentation for removed settings"},"status":400}
#MastoAdmin

omg. so because my new modem has a router I can't use my existing router? REALLY? I feel like I should be able to have another router with it's own settings.

On Hermite type sampling Kantorovich operators in the settings of mixed norm Spaces
Puja Sonawane, A. Sathish Kumar
https://arxiv.org/abs/2506.02468 https:…

On Hermite type sampling Kantorovich operators in the settings of mixed norm Spaces
In this paper, we analyze the convergence behavior of Hermite-type sampling Kantorovich operators in the context of mixed norm spaces. We prove certain direct approximation theorems, including the uniform convergence theorem, the Voronovskaja-type asymptotic formula, and an estimate of error in the approximation in terms of the modulus of continuity in mixed norm settings. Next, we estimate the rate of convergence of these sampling Kantorovich operators in terms of the modulus of continuity. In…

CogBench: A Large Language Model Benchmark for Multilingual Speech-Based Cognitive Impairment Assessment
Feng Rui, Zhiyao Luo, Wei Wang, Yuting Song, Yong Liu, Tingting Zhu, Jianqing Li, Xingyao Wang
https://arxiv.org/abs/2508.03360

CogBench: A Large Language Model Benchmark for Multilingual Speech-Based Cognitive Impairment Assessment
Automatic assessment of cognitive impairment from spontaneous speech offers a promising, non-invasive avenue for early cognitive screening. However, current approaches often lack generalizability when deployed across different languages and clinical settings, limiting their practical utility. In this study, we propose CogBench, the first benchmark designed to evaluate the cross-lingual and cross-site generalizability of large language models (LLMs) for speech-based cognitive impairment assessme…

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why
Chenhao Li, Marco Hutter, Andreas Krause
https://arxiv.org/abs/2507.05906 https://…

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why
This survey provides a comparative analysis of feature-based and GAN-based approaches to learning from demonstrations, with a focus on the structure of reward functions and their implications for policy learning. Feature-based methods offer dense, interpretable rewards that excel at high-fidelity motion imitation, yet often require sophisticated representations of references and struggle with generalization in unstructured settings. GAN-based methods, in contrast, use implicit, distributional s…

Coalitional stability under myopic expectations and externalities
Agustin G. Bonifacio, Maria Haydee Fonseca-Mairena, Pablo Neme
https://arxiv.org/abs/2507.03259

Coalitional stability under myopic expectations and externalities
We study coalition formation problems in the presence of externalities, focusing on settings where agents exhibit myopic expectations, that is, they evaluate potential deviations based solely on the immediate outcome, assuming no further reactions or reorganization by others. First, we establish a sufficient condition for the non-emptiness of both the core and the stable set. In the case of the core, our condition for ensuring non-emptiness also provides a characterization of all core partition…

Trustworthy Provenance for Big Data Science: a Modular Architecture Leveraging Blockchain in Federated Settings
Nicola Giuseppe Marchioro, Yannis Velegrakis, Valentine Anantharaj, Ian Foster, Sandro Luigi Fiore
https://arxiv.org/abs/2505.24675

Trustworthy Provenance for Big Data Science: a Modular Architecture Leveraging Blockchain in Federated Settings
Ensuring the trustworthiness and long-term verifiability of scientific data is a foundational challenge in the era of data-intensive, collaborative research. Provenance metadata plays a key role in this context, capturing the origin, transformation, and usage of research artifacts. However, existing solutions often fall short when applied to distributed, multi-institutional settings. This paper introduces a modular, domain-agnostic architecture for provenance tracking in federated environments,…

Safe and Socially Aware Multi-Robot Coordination in Multi-Human Social Care Settings
Ayodeji O. Abioye, Jayati Deshmukh, Athina Georgara, Dominic Price, Tuyen Nguyen, Aleksandra Landowska, Amel Bennaceur, Joel E. Fischer, Sarvapali D. Ramchurn
https://arxiv.org/abs/2507.02521

Safe and Socially Aware Multi-Robot Coordination in Multi-Human Social Care Settings
This research investigates strategies for multi-robot coordination in multi-human environments. It proposes a multi-objective learning-based coordination approach to addressing the problem of path planning, navigation, task scheduling, task allocation, and human-robot interaction in multi-human multi-robot (MHMR) settings.

HyperSumm-RL: A Dialogue Summarization Framework for Modeling Leadership Perception in Social Robots
Subasish Das
https://arxiv.org/abs/2507.04160 https://…

HyperSumm-RL: A Dialogue Summarization Framework for Modeling Leadership Perception in Social Robots
This paper introduces HyperSumm-RL, a hypertext-aware summarization and interaction analysis framework designed to investigate human perceptions of social robot leadership through long-form dialogue. The system utilizes a structured Natural Language Processing (NLP) workflow that combines transformer-based long dialogue summarization, leadership style modeling, and user response analysis, enabling scalable evaluation of social robots in complex human-robot interaction (HRI) settings. Unlike pri…

Uncovering Synergistic Educational Injustices of COVID-19 and AI
Ahmad Banyasady (Malayer university)
https://arxiv.org/abs/2507.03095 https://

Uncovering Synergistic Educational Injustices of COVID-19 and AI
Grounded in critical realism and using narrative inquiry, this article explores this article explores the long-term consequences of the COVID-19 pandemic and the rapid proliferation of artificial intelligence within higher education. Through the analysis of student narratives collected in Iranian university settings, the study reveals that learning experiences during and after the pandemic, coupled with unprepared exposure to AI tools, have generated hidden yet impactful layers of educational i…

RAM-W600: A Multi-Task Wrist Dataset and Benchmark for Rheumatoid Arthritis
Songxiao Yang, Haolin Wang, Yao Fu, Ye Tian, Tamotsu Kamishima, Masayuki Ikebe, Yafei Ou, Masatoshi Okutomi
https://arxiv.org/abs/2507.05193

RAM-W600: A Multi-Task Wrist Dataset and Benchmark for Rheumatoid Arthritis
Rheumatoid arthritis (RA) is a common autoimmune disease that has been the focus of research in computer-aided diagnosis (CAD) and disease monitoring. In clinical settings, conventional radiography (CR) is widely used for the screening and evaluation of RA due to its low cost and accessibility. The wrist is a critical region for the diagnosis of RA. However, CAD research in this area remains limited, primarily due to the challenges in acquiring high-quality instance-level annotations. (i) The w…

Characterizing Compute-Communication Overlap in GPU-Accelerated Distributed Deep Learning: Performance and Power Implications
Seonho Lee, Jihwan Oh, Junkyum Kim, Seokjin Go, Jongse Park, Divya Mahajan
https://arxiv.org/abs/2507.03114

Characterizing Compute-Communication Overlap in GPU-Accelerated Distributed Deep Learning: Performance and Power Implications
This paper provides an in-depth characterization of GPU-accelerated systems, to understand the interplay between overlapping computation and communication which is commonly employed in distributed training settings. Due to the large size of models, distributing them across multiple devices is required. Overlapping strategies, which enable concurrent computation and communication, are critical for mitigating communication bottlenecks and maximizing GPU utilization. However, the current consensus…

I hate it that without even noticing, I've somehow managed to agree to this option being on 😡 ->
How to stop Microsoft Edge from monitoring your Chrome browser history and settings
https://www.pocnetwork.net/tips/how-to-sto

How to stop Microsoft Edge from monitoring your Chrome browser history and settings | Poc Network // Tech
It shouldn't be surprising that any company might be interested in spying on you. Analytics is one of the many ways these companies thrive by learning everything they can about their customers. Including their browsing habits within other browsers, which is exactly what Microsoft Edge browser does. You'd think a browser would be limited to

Put Teacher in Student's Shoes: Cross-Distillation for Ultra-compact Model Compression Framework
Maolin Wang, Jun Chu, Sicong Xie, Xiaoling Zang, Yao Zhao, Wenliang Zhong, Xiangyu Zhao
https://arxiv.org/abs/2507.04636

Put Teacher in Student's Shoes: Cross-Distillation for Ultra-compact Model Compression Framework
In the era of mobile computing, deploying efficient Natural Language Processing (NLP) models in resource-restricted edge settings presents significant challenges, particularly in environments requiring strict privacy compliance, real-time responsiveness, and diverse multi-tasking capabilities. These challenges create a fundamental need for ultra-compact models that maintain strong performance across various NLP tasks while adhering to stringent memory constraints. To this end, we introduce Edge…

A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes
Kemboi Cheruiyot, Nickson Kiprotich, Vyacheslav Kungurtsev, Kennedy Mugo, Vivian Mwirigi, Marvin Ngesa
https://arxiv.org/abs/2507.06278

A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes
The increasing interest in research and innovation towards the development of autonomous agents presents a number of complex yet important scenarios of multiple AI Agents interacting with each other in an environment. The particular setting can be understood as exhibiting three possibly topologies of interaction - centrally coordinated cooperation, ad-hoc interaction and cooperation, and settings with noncooperative incentive structures. This article presents a comprehensive survey of all three…

Overview of Controllability Definitions in Supervisory Control Theory
Jeroen J. A. Keiren, Michel A. Reniers
https://arxiv.org/abs/2508.05177 https://arxiv…

Overview of Controllability Definitions in Supervisory Control Theory
In the field of supervisory control theory, the literature often proposes different definitions for the same concept, making it difficult to understand how these definitions are related. This is definitely so for the fundamental notion of controllability of a supervisor w.r.t. a plant. This paper lists definitions of controllability found in the literature and studies their relationships in settings of both deterministic and nondeterministic automata. In the general context, where both the supe…

ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound
Pouyan Navard, Yasemin Ozkut, Srikar Adhikari, Elaine Situ-LaCasse, Josie Acu\~na, Adrienne Yarnish, Alper Yilmaz
https://arxiv.org/abs/2508.04735

ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound
Retinal detachment (RD) is a vision-threatening condition that requires timely intervention to preserve vision. Macular involvement -- whether the macula is still intact (macula-intact) or detached (macula-detached) -- is the key determinant of visual outcomes and treatment urgency. Point-of-care ultrasound (POCUS) offers a fast, non-invasive, cost-effective, and accessible imaging modality widely used in diverse clinical settings to detect RD. However, ultrasound image interpretation is limite…

BackFed: An Efficient & Standardized Benchmark Suite for Backdoor Attacks in Federated Learning
Thinh Dao, Dung Thuy Nguyen, Khoa D Doan, Kok-Seng Wong
https://arxiv.org/abs/2507.04903

BackFed: An Efficient & Standardized Benchmark Suite for Backdoor Attacks in Federated Learning
Federated Learning (FL) systems are vulnerable to backdoor attacks, where adversaries train their local models on poisoned data and submit poisoned model updates to compromise the global model. Despite numerous proposed attacks and defenses, divergent experimental settings, implementation errors, and unrealistic assumptions hinder fair comparisons and valid conclusions about their effectiveness in real-world scenarios. To address this, we introduce BackFed - a comprehensive benchmark suite desi…

Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification
Simon Baur, Wojciech Samek, Jackie Ma
https://arxiv.org/abs/2508.04457 https://…

Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification
Reliable uncertainty quantification is crucial for trustworthy decision-making and the deployment of AI models in medical imaging. While prior work has explored the ability of neural networks to quantify predictive, epistemic, and aleatoric uncertainties using an information-theoretical approach in synthetic or well defined data settings like natural image classification, its applicability to real life medical diagnosis tasks remains underexplored. In this study, we provide an extensive uncerta…

Local Fr'echet Regression via RKHS embedding and Its Applications to Data Analysis on Manifolds
Yuki Iida, Hiroshi Shiraishi, Hiroaki Ogata
https://arxiv.org/abs/2507.03288

Local Fr'echet Regression via RKHS embedding and Its Applications to Data Analysis on Manifolds
Local Fr'echet Regression (LFR) is a nonparametric regression method for settings in which the explanatory variable lies in a Euclidean space and the response variable lies in a metric space. It is used to estimate smooth trajectories in general metric spaces from noisy observations of random objects taking values in such spaces. Since metric spaces form a broad class of spaces that often lack algebraic structures such as addition or scalar multiplication characteristics typical of vector space…

Microsoft makes Windows 10's extended security updates free for an extra year for users who sync PC settings via a Microsoft Account and the Windows Backup app (Zac Bowden/Windows Central)
https://www.windowscentral.com/software-ap

Microsoft makes Windows 10 security updates FREE for an extra year — but there's a catch, and you might not like it
Windows 10 users will have the option to continue receiving security updates for free beyond October 2025, so long as they sync their PC settings data with the cloud via a Microsoft Account.

Riemannian Inexact Gradient Descent for Quadratic Discrimination
Uday Talwar, Meredith K. Kupinski, Afrooz Jalilzadeh
https://arxiv.org/abs/2507.04670 http…

Riemannian Inexact Gradient Descent for Quadratic Discrimination
We propose an inexact optimization algorithm on Riemannian manifolds, motivated by quadratic discrimination tasks in high-dimensional, low-sample-size (HDLSS) imaging settings. In such applications, gradient evaluations are often biased due to limited sample sizes. To address this, we introduce a novel Riemannian optimization algorithm that is robust to inexact gradient information and prove an $\mathcal O(1/K)$ convergence rate under standard assumptions. We also present a line search variant …

EoH-S: Evolution of Heuristic Set using LLMs for Automated Heuristic Design
Fei Liu, Yilu Liu, Qingfu Zhang, Xialiang Tong, Mingxuan Yuan
https://arxiv.org/abs/2508.03082 https:…

EoH-S: Evolution of Heuristic Set using LLMs for Automated Heuristic Design
Automated Heuristic Design (AHD) using Large Language Models (LLMs) has achieved notable success in recent years. Despite the effectiveness of existing approaches, they only design a single heuristic to serve all problem instances, often inducing poor generalization across different distributions or settings. To address this issue, we propose Automated Heuristic Set Design (AHSD), a new formulation for LLM-driven AHD. The aim of AHSD is to automatically generate a small-sized complementary heur…

LOOM-Scope: a comprehensive and efficient LOng-cOntext Model evaluation framework
Zecheng Tang, Haitian Wang, Quantong Qiu, Baibei Ji, Ruoxi Sun, Keyan Zhou, Juntao Li, Min Zhang
https://arxiv.org/abs/2507.04723

LOOM-Scope: a comprehensive and efficient LOng-cOntext Model evaluation framework
Long-context processing has become a fundamental capability for large language models~(LLMs). To assess model's long-context performance, numerous long-context evaluation benchmarks have been proposed. However, variations in evaluation settings across these benchmarks lead to inconsistent results, making it difficult to draw reliable comparisons. Besides, the high computational cost of long-context evaluation poses a significant barrier for the community to conduct comprehensive assessments of …

Inverse Probability Weighting for Recurrent Event Models
Jiren Sun, Tobias Mutze, Richard Cook, Tianmeng Lyu
https://arxiv.org/abs/2507.04567 https://

Inverse Probability Weighting for Recurrent Event Models
Recurrent events are common and important clinical trial endpoints in many disease areas, e.g., cardiovascular hospitalizations in heart failure, relapses in multiple sclerosis, or exacerbations in asthma. During a trial, patients may experience intercurrent events, that is, events after treatment assignment which affect the interpretation or existence of the outcome of interest. In many settings, a treatment effect in the scenario in which the intercurrent event would not occur is of clinical …

Are AI-Generated Fixes Secure? Analyzing LLM and Agent Patches on SWE-bench
Amirali Sajadi, Kostadin Damevski, Preetha Chatterjee
https://arxiv.org/abs/2507.02976

Are AI-Generated Fixes Secure? Analyzing LLM and Agent Patches on SWE-bench
Large Language Models (LLMs) and their agentic frameworks are increasingly adopted to automate software development tasks such as issue resolution and program repair. While prior work has identified security risks in LLM-generated code, most evaluations have focused on synthetic or isolated settings, leaving open questions about the security of these systems in real-world development contexts. In this study, we present the first large-scale security analysis of LLM-generated patches using 20,00…

AdS: Adapter-state Sharing Framework for Multimodal Sarcasm Detection
Soumyadeep Jana, Sahil Danayak, Sanasam Ranbir Singh
https://arxiv.org/abs/2507.04508

AdS: Adapter-state Sharing Framework for Multimodal Sarcasm Detection
The growing prevalence of multimodal image-text sarcasm on social media poses challenges for opinion mining, especially under resource constraints. Existing approaches rely on full fine-tuning of large pre-trained models, making them unsuitable for low-resource settings. While recent parameter-efficient fine-tuning (PEFT) methods offer promise, their off-the-shelf use underperforms on complex tasks like sarcasm detection. We propose AdS (Adapter-State Sharing), a lightweight framework built on …

Environmental (in)considerations in the Design of Smartphone Settings
Thomas Thibault, L\'ea Mosesso, Camille Adam, Aur\'elien Tabard, Ana\"elle Beignon, Nolwenn Maudet
https://arxiv.org/abs/2507.19094

Environmental (in)considerations in the Design of Smartphone Settings
Designing for sufficiency is one of many approaches that could foster more moderate and sustainable digital practices. Based on the Sustainable Information and Communication Technologies (ICT) and Human-Computer Interaction (HCI) literature, we identify five environmental settings categories. However, our analysis of three mobile OS and nine representative applications shows an overall lack of environmental concerns in settings design, leading us to identify six pervasive anti-patterns. Environ…

Discriminating Distal Ischemic Stroke from Seizure-Induced Stroke Mimics Using Dynamic Susceptibility Contrast MRI
Marijn Borghouts, Richard McKinley, Josien Pluim, Manuel K\"ostner, Roland Wiest, Ruisheng Su
https://arxiv.org/abs/2508.04404

Discriminating Distal Ischemic Stroke from Seizure-Induced Stroke Mimics Using Dynamic Susceptibility Contrast MRI
Distinguishing acute ischemic strokes (AIS) from stroke mimics (SMs), particularly in cases involving medium and small vessel occlusions, remains a significant diagnostic challenge. While computed tomography (CT) based protocols are commonly used in emergency settings, their sensitivity for detecting distal occlusions is limited. This study explores the potential of magnetic resonance perfusion (MRP) imaging as a tool for differentiating distal AIS from epileptic seizures, a prevalent SM. Using…

Beyond Policy Optimization: A Data Curation Flywheel for Sparse-Reward Long-Horizon Planning
Yutong Wang, Pengliang Ji, Kaixin Li, Baolong Bi, Tao Feng, Guillaume Sartoretti
https://arxiv.org/abs/2508.03018

Beyond Policy Optimization: A Data Curation Flywheel for Sparse-Reward Long-Horizon Planning
Large Language Reasoning Models have demonstrated remarkable success on static tasks, yet their application to multi-round agentic planning in interactive environments faces two fundamental challenges. First, the intractable credit assignment problem renders conventional reinforcement learning ineffective in sparse-reward settings. Second, the computational overhead of verbose, step-by-step reasoning histories is prohibitive. To address these challenges, we propose BPO, a three-stage framework …

Practical considerations for Gaussian Process modeling for causal inference quasi-experimental studies with panel data
Sofia L. Vega, Rachel C. Nethery
https://arxiv.org/abs/2507.05128

Practical considerations for Gaussian Process modeling for causal inference quasi-experimental studies with panel data
Estimating causal effects in quasi-experiments with spatio-temporal panel data often requires adjusting for unmeasured confounding that varies across space and time. Gaussian Processes (GPs) offer a flexible, nonparametric modeling approach that can account for such complex dependencies through carefully chosen covariance kernels. In this paper, we provide a practical and interpretable framework for applying GPs to causal inference in panel data settings. We demonstrate how GPs generalize popul…

VirtLab: An AI-Powered System for Flexible, Customizable, and Large-scale Team Simulations
Mohammed Almutairi, Charles Chiang, Haoze Guo, Matthew Belcher, Nandini Banerjee, Maria Milkowski, Svitlana Volkova, Daniel Nguyen, Tim Weninger, Michael Yankoski, Trenton W. Ford, Diego Gomez-Zara
https://arxiv.org/abs/2508.04634

VirtLab: An AI-Powered System for Flexible, Customizable, and Large-scale Team Simulations
Simulating how team members collaborate within complex environments using Agentic AI is a promising approach to explore hypotheses grounded in social science theories and study team behaviors. We introduce VirtLab, a user-friendly, customizable, multi-agent, and scalable team simulation system that enables testing teams with LLM-based agents in spatial and temporal settings. This system addresses the current frameworks' design and technical limitations that do not consider flexible simulation s…

Non-Stationary Bandit Convex Optimization: An Optimal Algorithm with Two-Point Feedback
Chang He, Bo Jiang, Shuzhong Zhang
https://arxiv.org/abs/2508.04654 https://

Non-Stationary Bandit Convex Optimization: An Optimal Algorithm with Two-Point Feedback
This paper studies bandit convex optimization in non-stationary environments with two-point feedback, using dynamic regret as the performance measure. We propose an algorithm based on bandit mirror descent that extends naturally to non-Euclidean settings. Let $T$ be the total number of iterations and $\mathcal{P}_{T,p}$ the path variation with respect to the $\ell_p$-norm. In Euclidean space, our algorithm matches the optimal regret bound $\mathcal{O}(\sqrt{dT(1+\mathcal{P}_{T,2})})$, improving…

ToolVQA: A Dataset for Multi-step Reasoning VQA with External Tools
Shaofeng Yin, Ting Lei, Yang Liu
https://arxiv.org/abs/2508.03284 https://arxiv.org/pdf…

ToolVQA: A Dataset for Multi-step Reasoning VQA with External Tools
Integrating external tools into Large Foundation Models (LFMs) has emerged as a promising approach to enhance their problem-solving capabilities. While existing studies have demonstrated strong performance in tool-augmented Visual Question Answering (VQA), recent benchmarks reveal significant gaps in real-world tool-use proficiency, particularly in functionally diverse multimodal settings requiring multi-step reasoning. In this work, we introduce ToolVQA, a large-scale multimodal dataset compri…

PLA: Prompt Learning Attack against Text-to-Image Generative Models
Xinqi Lyu, Yihao Liu, Yanjie Li, Bin Xiao
https://arxiv.org/abs/2508.03696 https://arxi…

PLA: Prompt Learning Attack against Text-to-Image Generative Models
Text-to-Image (T2I) models have gained widespread adoption across various applications. Despite the success, the potential misuse of T2I models poses significant risks of generating Not-Safe-For-Work (NSFW) content. To investigate the vulnerability of T2I models, this paper delves into adversarial attacks to bypass the safety mechanisms under black-box settings. Most previous methods rely on word substitution to search adversarial prompts. Due to limited search space, this leads to suboptimal p…

Lightweight Transformers for Zero-Shot and Fine-Tuned Text-to-SQL Generation Using Spider
Chirag Seth, Utkarsh Singh
https://arxiv.org/abs/2508.04623 https://

Lightweight Transformers for Zero-Shot and Fine-Tuned Text-to-SQL Generation Using Spider
Text-to-SQL translation enables non-expert users to query relational databases using natural language, with applications in education and business intelligence. This study evaluates three lightweight transformer models - T5-Small, BART-Small, and GPT-2 - on the Spider dataset, focusing on low-resource settings. We developed a reusable, model-agnostic pipeline that tailors schema formatting to each model's architecture, training them across 1000 to 5000 iterations and evaluating on 1000 test sam…

MissDDIM: Deterministic and Efficient Conditional Diffusion for Tabular Data Imputation
Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal
https://arxiv.org/abs/2508.03083 https:…

MissDDIM: Deterministic and Efficient Conditional Diffusion for Tabular Data Imputation
Diffusion models have recently emerged as powerful tools for missing data imputation by modeling the joint distribution of observed and unobserved variables. However, existing methods, typically based on stochastic denoising diffusion probabilistic models (DDPMs), suffer from high inference latency and variable outputs, limiting their applicability in real-world tabular settings. To address these deficiencies, we present in this paper MissDDIM, a conditional diffusion framework that adapts Deno…

Adapting Language Models to Indonesian Local Languages: An Empirical Study of Language Transferability on Zero-Shot Settings
Rifki Afina Putri
https://arxiv.org/abs/2507.01645

Adapting Language Models to Indonesian Local Languages: An Empirical Study of Language Transferability on Zero-Shot Settings
In this paper, we investigate the transferability of pre-trained language models to low-resource Indonesian local languages through the task of sentiment analysis. We evaluate both zero-shot performance and adapter-based transfer on ten local languages using models of different types: a monolingual Indonesian BERT, multilingual models such as mBERT and XLM-R, and a modular adapter-based approach called MAD-X. To better understand model behavior, we group the target languages into three categori…