Tootfinder

CI/CD #Pipeline #Architecture: Complete Guide to Building Robust CI and CD Pipelines
https://

Ukraine, Hungary spat highlights tensions over pipeline attack: https://benborges.xyz/2025/08/25/ukraine-hungary-spat-highlights-tensions.html

The music industry is developing tools to detect AI-generated music across the music pipeline, with a focus on licensing and control rather than takedowns (Jack Buehrer/The Verge)
https://www.theverge.com/ai-artificial-int

The music industry is building the tech to hunt down AI songs
With no way to stop the onslaught of AI music, the industry is taking a different approach: figuring out how to make money off of it.

A Scalable Post-Processing Pipeline for Large-Scale Free-Space Multi-Agent Path Planning with PiBT
Arjo Chakravarty, Michael X. Grey, M. A. Viraj J. Muthugala, Mohan Rajesh Elara
https://arxiv.org/abs/2506.16748

A Scalable Post-Processing Pipeline for Large-Scale Free-Space Multi-Agent Path Planning with PiBT
Free-space multi-agent path planning remains challenging at large scales. Most existing methods either offer optimality guarantees but do not scale beyond a few dozen agents, or rely on grid-world assumptions that do not generalize well to continuous space. In this work, we propose a hybrid, rule-based planning framework that combines Priority Inheritance with Backtracking (PiBT) with a novel safety-aware path smoothing method. Our approach extends PiBT to 8-connected grids and selectively appl…

We built a regulatory system for a reason - our history is replete with pipeline breaks resulting in leaks and explosions (natural gas, for example), oil well leaks and fires, chemical plant releases, exposure of workers to hazardous conditions. We have seen over and over again how industry's cost cutting ends up killing and injuring people. Yet the #GOP is bound and determined to gut the policies …

We built a regulatory system for a reason - our history is replete with pipeline breaks resulting in leaks and explosions (natural gas, for example), oil well leaks and fires, chemical plant releases, exposure of workers to hazardous conditions. We have seen over and over again how industry's cost cutting ends up killing and injuring people. Yet the #GOP is bound and determined to gut the policies …

Argentina pushes pipeline exports through protected Patagonian waters https://news.mongabay.com/2025/08/argentina-pushes-pipeline-exports-through-protected-patagonian-waters/

Argentina pushes pipeline exports through protected Patagonian waters
San Matías Gulf, on the northern coast of Argentina’s Patagonia region, is one of the most biodiverse areas in the Argentine Sea. The Valdés Peninsula — a UNESCO World Heritage Site since 1999 and a biosphere reserve since 2014 — forms its southern border. It’s surrounded by Islote Lobos National Park, the Puerto Lobos and […]

The music industry is developing tools to detect AI-generated music across the music pipeline, with a focus on licensing and control rather than takedowns (Jack Buehrer/The Verge)
https://www.theverge.com/ai-artificial-int

The music industry is building the tech to hunt down AI songs
With no way to stop the onslaught of AI music, the industry is taking a different approach: figuring out how to make money off of it.

Lookup Table-based Multiplication-free All-digital DNN Accelerator Featuring Self-Synchronous Pipeline Accumulation
Hiroto Tagata, Takashi Sato, Hiromitsu Awano
https://arxiv.org/abs/2506.16800

Lookup Table-based Multiplication-free All-digital DNN Accelerator Featuring Self-Synchronous Pipeline Accumulation
Deep neural networks (DNNs) have been widely applied in our society, yet reducing power consumption due to large-scale matrix computations remains a critical challenge. MADDNESS is a known approach to improving energy efficiency by substituting matrix multiplication with table lookup operations. Previous research has employed large analog computing circuits to convert inputs into LUT addresses, which presents challenges to area efficiency and computational accuracy. This paper proposes a novel …

Bundeswirtschaftsministerium: Angriff hat keine Auswirkungen auf Ölversorgung
Die Unterbrechung von Öllieferungen über die Druschba-Pipeline hat dem Bundeswirtschaftsministerium zufolge keine Auswirkungen auf die Versorgungssicherheit in Deutschland. Der Betrieb der PCK-Ölraffinerie im brandenburgischen Schwedt, die einen Teil ihrer Öllieferungen aus Kasachstan über die durch Russland verlaufende Druschba-Pipeline erhält, sei…
📑

Ukraine-Liveblog: ++ Selenskyj: Moskau will Gipfel verhindern ++
Der ukrainische Präsident Wolodymyr Selenskyj zweifelt an der Bereitschaft von Kremlchef Putin für ein Gipfeltreffen. Der Grünen-Politiker Nouripour fordert eine stärkere Unterstützung der Ukraine - auch mit Marschflugkörpern.

Declarative Data Pipeline for Large Scale ML Services
Yunzhao Yang, Runhui Wang, Xuanqing Liu, Adit Krishnan, Yefan Tao, Yuqian Deng, Kuangyou Yao, Peiyuan Sun, Henrik Johnson, Aditi sinha, Davor Golac, Gerald Friedland, Usman Shakeel, Daryl Cooke, Joe Sullivan, Chris Kong
https://arxiv.org/abs/2508.15105

Declarative Data Pipeline for Large Scale ML Services
Modern distributed data processing systems face significant challenges in balancing system performance with code maintainability and developer productivity, particularly when integrating machine learning capabilities at scale. In large collaborative environments, these challenges are amplified by high communication overhead between teams and the complexity of coordinating development across multiple groups. This paper presents a novel "Declarative Data Pipeline" architecture that addresses thes…

The damage being done by the Republican party every week in the USA is enormous. There are the scandalous extremely bad major policies that are fairly well known.
But the depth and breath of the harm being caused is largely ignored. And the damage that will play out over years and decades is much greater and even more ignored.

Persistent Patterns in Eye Movements: A Topological Approach to Emotion Recognition
Arsha Niksa, Hooman Zare, Ali Shahrabi, Hanieh Hatami, Mohammadreza Razvan
https://arxiv.org/abs/2507.17450

Persistent Patterns in Eye Movements: A Topological Approach to Emotion Recognition
We present a topological pipeline for automated multiclass emotion recognition from eye-tracking data. Delay embeddings of gaze trajectories are analyzed using persistent homology. From the resulting persistence diagrams, we extract shape-based features such as mean persistence, maximum persistence, and entropy. A random forest classifier trained on these features achieves up to $75.6\%$ accuracy on four emotion classes, which are the quadrants the Circumplex Model of Affect. The results demons…

Binary black hole population inference combining confident and marginal events from the $\tt{IAS\text{-}HM}$ search pipeline
Ajit Kumar Mehta, Digvijay Wadekar, Isha Anantpurkar, Javier Roulet, Tejaswi Venumadhav, Tousif Islam, Jonathan Mushkin, Barak Zackay, Matias Zaldarriaga
https://arxiv.org/abs/2508.15350

Binary black hole population inference combining confident and marginal events from the $\tt{IAS\text{-}HM}$ search pipeline
We present the population properties of binary black hole mergers identified by the $\tt{IAS\text{-}HM}$ pipeline (which incorporates higher-order modes in the search templates) during the third observing run (O3) of the LIGO, Virgo, and KAGRA (LVK) detectors. In our population inference analysis, instead of only using events above a sharp cut based on a particular detection threshold (e.g., false alarm rate), we use a Bayesian framework to consistently include both marginal and confident event…

MExECON: Multi-view Extended Explicit Clothed humans Optimized via Normal integration
Fulden Ece U\u{g}ur, Rafael Redondo, Albert Barreiro, Stefan Hristov, Roger Mar\'i
https://arxiv.org/abs/2508.15500

MExECON: Multi-view Extended Explicit Clothed humans Optimized via Normal integration
This work presents MExECON, a novel pipeline for 3D reconstruction of clothed human avatars from sparse multi-view RGB images. Building on the single-view method ECON, MExECON extends its capabilities to leverage multiple viewpoints, improving geometry and body pose estimation. At the core of the pipeline is the proposed Joint Multi-view Body Optimization (JMBO) algorithm, which fits a single SMPL-X body model jointly across all input views, enforcing multi-view consistency. The optimized body …

Experimental Setup and Software Pipeline to Evaluate Optimization based Autonomous Multi-Robot Search Algorithms
Aditya Bhatt, Mary Katherine Corra, Franklin Merlo, Prajit KrisshnaKumar, Souma Chowdhury
https://arxiv.org/abs/2506.16710

Experimental Setup and Software Pipeline to Evaluate Optimization based Autonomous Multi-Robot Search Algorithms
Signal source localization has been a problem of interest in the multi-robot systems domain given its applications in search \& rescue and hazard localization in various industrial and outdoor settings. A variety of multi-robot search algorithms exist that usually formulate and solve the associated autonomous motion planning problem as a heuristic model-free or belief model-based optimization process. Most of these algorithms however remains tested only in simulation, thereby losing the opportu…

Ukraine strikes back: burning oil pipeline, “Flamingo” missile, and Kremlin panic: https://benborges.xyz/2025/08/22/ukraine-strikes-back-burning-oil.html

Design and Evaluation of a Scalable Data Pipeline for AI-Driven Air Quality Monitoring in Low-Resource Settings
Richard Sserujongi, Daniel Ogenrwot, Nicholas Niwamanya, Noah Nsimbe, Martin Bbaale, Benjamin Ssempala, Noble Mutabazi, Raja Fidel Wabinyai, Deo Okure, Engineer Bainomugisha
https://arxiv.org/abs/2508.14451

Design and Evaluation of a Scalable Data Pipeline for AI-Driven Air Quality Monitoring in Low-Resource Settings
The increasing adoption of low-cost environmental sensors and AI-enabled applications has accelerated the demand for scalable and resilient data infrastructures, particularly in data-scarce and resource-constrained regions. This paper presents the design, implementation, and evaluation of the AirQo data pipeline: a modular, cloud-native Extract-Transform-Load (ETL) system engineered to support both real-time and batch processing of heterogeneous air quality data across urban deployments in Afri…

Standard Applicability Judgment and Cross-jurisdictional Reasoning: A RAG-based Framework for Medical Device Compliance
Yu Han, Aaron Ceross, Jeroen H. M. Bergmann
https://arxiv.org/abs/2506.18511

Standard Applicability Judgment and Cross-jurisdictional Reasoning: A RAG-based Framework for Medical Device Compliance
Identifying the appropriate regulatory standard applicability remains a critical yet understudied challenge in medical device compliance, frequently necessitating expert interpretation of fragmented and heterogeneous documentation across different jurisdictions. To address this challenge, we introduce a modular AI system that leverages a retrieval-augmented generation (RAG) pipeline to automate standard applicability determination. Given a free-text device description, our system retrieves cand…

UFL to NFL Pipeline: Tracking Every UFL Player Signed to a 2025 NFL Roster https://www.foxsports.com/stories/nfl/ufl-nfl-pipeline-tracking-every-ufl-player-signed-2025-nfl-roster

AutoWISP: Automated Processing of Wide-Field Color Images
Angel E. Romero, Kaloyan Penev, S. Javad Jafarzadeh, Zoltan Csubry, Joel D. Hartman, Gaspar A. Bakos
https://arxiv.org/abs/2507.15830

AutoWISP: Automated Processing of Wide-Field Color Images
We have developed a software pipeline, AutoWISP, for extracting high-precision photometry from citizen scientists' observations made with consumer-grade color digital cameras (digital single-lens reflex, or DSLR, cameras), based on our previously developed tool, AstroWISP. The new pipeline is designed to convert these observations, including color images, into high-precision light curves of stars. We outline the individual steps of the pipeline and present a case study using a Sony-alpha 7R II …

Design high-confidence computers using trusted instructional set architecture and emulators
Shuangbao Paul Wang
https://arxiv.org/abs/2506.18780 https://…

Design high-confidence computers using trusted instructional set architecture and emulators
High-confidence computing relies on trusted instructional set architecture, sealed kernels, and secure operating systems. Cloud computing depends on trusted systems for virtualization tasks. Branch predictions and pipelines are essential in improving performance of a CPU/GPU. But Spectre and Meltdown make modern processors vulnerable to be exploited. Disabling the prediction and pipeline is definitely not a good solution. On the other hand, current software patches can only address non-essentia…

ah of course the hero image on this blog post about big data streaming should feature a processing pipeline feeding a data lake
but before i worked out what they intended i thought it was a corroded waste outflow pouring into a reservoir

This interested me because of my history with XSLT and how the WHATWG ‘discussion’ to remove it devolved:
https://www.igalia.com/chats/xslt-liam
WHATWG issue:

RIP XSLT? | Igalia
Igalia is an open source consultancy specialised in the development of innovative projects and solutions. Our engineers have expertise in a wide range of technological areas, including browsers and client-side web technologies, graphics pipeline, compilers and virtual machines. We have the most WPE, WebKit, Chromium/Blink and Firefox expertise found in the consulting business, including many reviewers and committers. Igalia designs, develops, customises and optimises GNU/Linux-based solutions f…

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT
Shahil Kumar, Manu Pande, Anay Yatin Damle
https://arxiv.org/abs/2506.18297

Comparative Analysis of Lion and AdamW Optimizers for Cross-Encoder Reranking with MiniLM, GTE, and ModernBERT
Modern information retrieval systems often employ a two-stage pipeline: an efficient initial retrieval stage followed by a computationally intensive reranking stage. Cross-encoders have shown strong effectiveness for reranking due to their deep analysis of query-document pairs. This paper studies the impact of the Lion optimizer, a recent alternative to AdamW, during fine-tuning of cross-encoder rerankers. We fine-tune three transformer models-MiniLM, GTE, and ModernBERT-on the MS MARCO passage…

Spatio-spectral diarization of meetings by combining TDOA-based segmentation and speaker embedding-based clustering
Tobias Cord-Landwehr, Tobias Gburrek, Marc Deegen, Reinhold Haeb-Umbach
https://arxiv.org/abs/2506.16228

Spatio-spectral diarization of meetings by combining TDOA-based segmentation and speaker embedding-based clustering
We propose a spatio-spectral, combined model-based and data-driven diarization pipeline consisting of TDOA-based segmentation followed by embedding-based clustering. The proposed system requires neither access to multi-channel training data nor prior knowledge about the number or placement of microphones. It works for both a compact microphone array and distributed microphones, with minor adjustments. Due to its superior handling of overlapping speech during segmentation, the proposed pipeline …

If you are using "docker:latest" in your CI pipeline, you may find your builds broken with a weird "failed to find target default" error.
In that case, switching to "docker:28.3.1" will fit this.
Issue on GitHub: https://github.com/docker/buildx/issues/3328…

A Deep Convolutional Neural Network-Based Novel Class Balancing for Imbalance Data Segmentation
Atifa Kalsoom, M. A. Iftikhar, Amjad Ali, Zubair Shah, Shidin Balakrishnan, Hazrat Ali
https://arxiv.org/abs/2506.18474

A Deep Convolutional Neural Network-Based Novel Class Balancing for Imbalance Data Segmentation
Retinal fundus images provide valuable insights into the human eye's interior structure and crucial features, such as blood vessels, optic disk, macula, and fovea. However, accurate segmentation of retinal blood vessels can be challenging due to imbalanced data distribution and varying vessel thickness. In this paper, we propose BLCB-CNN, a novel pipeline based on deep learning and bi-level class balancing scheme to achieve vessel segmentation in retinal fundus images. The BLCB-CNN scheme uses …

UFL to NFL Pipeline: Tracking Every UFL Player Signed to a 2025 NFL Roster https://www.foxsports.com/stories/nfl/ufl-nfl-pipeline-tracking-every-ufl-player-signed-2025-nfl-roster

On Temporal Guidance and Iterative Refinement in Audio Source Separation
Tobias Morocutti, Jonathan Greif, Paul Primus, Florian Schmid, Gerhard Widmer
https://arxiv.org/abs/2507.17297

On Temporal Guidance and Iterative Refinement in Audio Source Separation
Spatial semantic segmentation of sound scenes (S5) involves the accurate identification of active sound classes and the precise separation of their sources from complex acoustic mixtures. Conventional systems rely on a two-stage pipeline - audio tagging followed by label-conditioned source separation - but are often constrained by the absence of fine-grained temporal information critical for effective separation. In this work, we address this limitation by introducing a novel approach for S5 th…

At Berlin Buzzwords 2025, Ved Prakash discussed how Siphon transformed their data pipeline using Apache Iceberg to successfully stream quality data into both Snowflake and Clickhouse simultaneously. In this short talk, you’ll learn about their battle-tested architecture, the performance improvements they’ve achieved, and their strategies for maintaining data consistency across two analytics engines.
Watch the full session:

All for law and law for all: Adaptive RAG Pipeline for Legal Research
Figarri Keisha, Prince Singh, Pallavi, Dion Fernandes, Aravindh Manivannan, Ilham Wicaksono, Faisal Ahmad
https://arxiv.org/abs/2508.13107

All for law and law for all: Adaptive RAG Pipeline for Legal Research
Retrieval-Augmented Generation (RAG) mitigates hallucinations by grounding large language model outputs in cited sources, a capability that is especially critical in the legal domain. We present an end-to-end RAG pipeline that revisits and extends the LegalBenchRAG baseline with three targeted enhancements: (i) a context-aware query translator that disentangles document references from natural-language questions and adapts retrieval depth and response style based on expertise and specificity, (…

⚡ End-to-end pipeline solution that transforms document processing workflows from inefficient manual work to automated intelligent extraction
🔍 Converts complex document layouts into well-formatted structured data while maintaining context and relationships between different document elements

Trump 'very angry' about Ukrainian drone strikes on Russian oil pipeline: https://benborges.xyz/2025/08/22/trump-very-angry-about-ukrainian.html

Neural-GASh: A CGA-based neural radiance prediction pipeline for real-time shading
Efstratios Geronikolakis, Manos Kamarianakis, Antonis Protopsaltis, George Papagiannakis
https://arxiv.org/abs/2507.13917

Neural-GASh: A CGA-based neural radiance prediction pipeline for real-time shading
This paper presents Neural-GASh, a novel real-time shading pipeline for 3D meshes, that leverages a neural radiance field architecture to perform image-based rendering (IBR) using Conformal Geometric Algebra (CGA)-encoded vertex information as input. Unlike traditional Precomputed Radiance Transfer (PRT) methods, that require expensive offline precomputations, our learned model directly consumes CGA-based representations of vertex positions and normals, enabling dynamic scene shading without pr…

AutoMAT: A Hierarchical Framework for Autonomous Alloy Discovery
Penghui Yang, Chendong Zhao, Bijun Tang, Zhonghan Zhang, Xinrun Wang, Yanchen Deng, Yuhao Lu, Cuntai Guan, Zheng Liu, Bo An
https://arxiv.org/abs/2507.16005

AutoMAT: A Hierarchical Framework for Autonomous Alloy Discovery
Alloy discovery is central to advancing modern industry but remains hindered by the vastness of compositional design space and the costly validation. Here, we present AutoMAT, a hierarchical and autonomous framework grounded in and validated by experiments, which integrates large language models, automated CALPHAD-based simulations, and AI-driven search to accelerate alloy design. Spanning the entire pipeline from ideation to validation, AutoMAT achieves high efficiency, accuracy, and interpret…

Scalable FPGA Framework for Real-Time Denoising in High-Throughput Imaging: A DRAM-Optimized Pipeline using High-Level Synthesis
Weichien Liao
https://arxiv.org/abs/2508.14917 h…

Scalable FPGA Framework for Real-Time Denoising in High-Throughput Imaging: A DRAM-Optimized Pipeline using High-Level Synthesis
High-throughput imaging workflows, such as Parallel Rapid Imaging with Spectroscopic Mapping (PRISM), generate data at rates that exceed conventional real-time processing capabilities. We present a scalable FPGA-based preprocessing pipeline for real-time denoising, implemented via High-Level Synthesis (HLS) and optimized for DRAM-backed buffering. Our architecture performs frame subtraction and averaging directly on streamed image data, minimizing latency through burst-mode AXI4 interfaces. The…

LLaPipe: LLM-Guided Reinforcement Learning for Automated Data Preparation Pipeline Construction
Jing Chang, Chang Liu, Jinbin Huang, Rui Mao, Jianbin Qin
https://arxiv.org/abs/2507.13712

LLaPipe: LLM-Guided Reinforcement Learning for Automated Data Preparation Pipeline Construction
Automated data preparation is crucial for democratizing machine learning, yet existing reinforcement learning (RL) based approaches suffer from inefficient exploration in the vast space of possible preprocessing pipelines. We present LLaPipe, a novel framework that addresses this exploration bottleneck by integrating Large Language Models (LLMs) as intelligent policy advisors. Unlike traditional methods that rely solely on statistical features and blind trial-and-error, LLaPipe leverages the se…

PAT : a cautionary tale about generative visual augmentation for Object Re-identification
Leonardo Santiago Benitez Pereira, Arathy Jeevan
https://arxiv.org/abs/2507.15888

PAT++: a cautionary tale about generative visual augmentation for Object Re-identification
Generative data augmentation has demonstrated gains in several vision tasks, but its impact on object re-identification - where preserving fine-grained visual details is essential - remains largely unexplored. In this work, we assess the effectiveness of identity-preserving image generation for object re-identification. Our novel pipeline, named PAT++, incorporates Diffusion Self-Distillation into the well-established Part-Aware Transformer. Using the Urban Elements ReID Challenge dataset, we c…

Ungarn: Bekommen wieder Öl durch russische Pipeline
Durch die russische Pipeline "Druschba" fließt nach einem Angriff der ukrainischen Armee nach Angaben aus Budapest wieder Erdöl. Die Öllieferungen nach Ungarn seien wieder aufgenommen worden, schrieb Außenminister Peter Szijjarto am Abend auf X. Er habe sich bei der russischen Regierung für die schnelle Reparatur bedankt.
Szijjarto fügte hinzu: "Wir erwarten von der Ukraine…
📑

Ukraine-Liveblog: ++ Trump beriet offenbar mit Orban über Ukraine und EU ++
Laut Medienberichten haben US-Präsident Trump und Ungarns Ministerpräsident Orban über einen EU-Beitritt der Ukraine gesprochen. Russland meldet Stromausfälle in Teilen der besetzten Region Saporischschja.

LOCOFY Large Design Models -- Design to code conversion solution
Sohaib Muhammad, Ashwati Vipin, Karan Shetti, Honey Mittal
https://arxiv.org/abs/2507.16208

LOCOFY Large Design Models -- Design to code conversion solution
Despite rapid advances in Large Language Models and Multimodal Large Language Models (LLMs), numerous challenges related to interpretability, scalability, resource requirements and repeatability remain, related to their application in the design-to-code space. To address this, we introduce the Large Design Models (LDMs) paradigm specifically trained on designs and webpages to enable seamless conversion from design-to-code. We have developed a training and inference pipeline by incorporating dat…

This feels eerily like the mistakes the tech industry made during the dot com crash when they stopped hiring all junior devs for several years and decimated the talent pipeline. The industry suffered with long development timelines and huge salary costs for a long time.

Goodbye, $165,000 Tech Jobs. Student Coders Seek Work at Chipotle.
As companies like Amazon and Microsoft lay off workers and embrace A.I. coding tools, computer science graduates say they’re struggling to land tech jobs.

Puts a "Deploy" button in Strapi so rather than webhooks triggering builds on every change, you can decide when to publish. Supports self-hosted Gitlab instances and pipeline variables.
Built from a fork-of-a-fork plus some retracted PRs.
All I really did was swear at `npm` until I figured out that I was missing a "prepare" step in package.json

GitHub - jaygooby/strapi-plugin-gitlab-publish: Trigger GitLab pipelines. Supports self-hosted GitLab instances and custom variable passing to the pipeline
Trigger GitLab pipelines. Supports self-hosted GitLab instances and custom variable passing to the pipeline - jaygooby/strapi-plugin-gitlab-publish

From The Energy Mix via The Canadian Press
Cenovus, Enbridge Misled Investors, Greenwashing Complaint Alleges
A shareholder advocacy group has filed a greenwashing complaint with Alberta’s securities watchdog alleging Cenovus Inc., one of Canada’s biggest oilsands producers, and Enbridge Inc., the country’s biggest crude pipeline operator, have misled investors in their environmental disclosures.

From ⁨⁨⁨⁨⁨#AnnafromUkraine⁩⁩⁩⁩⁩ @AnnafromUkraine@youtube.com
#PUTIN SHOCKED: DRUZHBA OIL PIPELINE DESTROYED IN RUSSIA Vlog 1141: War in Ukraine
The Armed Forces of #Ukraine stopped the

Replaced article(s) found for cs.IR. https://arxiv.org/list/cs.IR/new
[1/1]:
- Text2Struct: A Machine Learning Pipeline for Mining Structured Data from Text
Chaochao Zhou, Bo Yang

Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems
Haowei Yang, Yu Tian, Zhongheng Yang, Zhao Wang, Chengrui Zhou, Dannier Li
https://arxiv.org/abs/2506.17551

Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems
With the rapid adoption of large language models (LLMs) in recommendation systems, the computational and communication bottlenecks caused by their massive parameter sizes and large data volumes have become increasingly prominent. This paper systematically investigates two classes of optimization methods-model parallelism and data parallelism-for distributed training of LLMs in recommendation scenarios. For model parallelism, we implement both tensor parallelism and pipeline parallelism, and int…

Pipeline attack stops Russian oil flow to Hungary, Slovakia: https://benborges.xyz/2025/08/19/pipeline-attack-stops-russian-oil.html

Unify, which offers AI-powered tools to boost revenue pipelines, raised a $40M Series B led by Battery Ventures, valuing it at $260M, following a $12M Series A (Alex Konrad/Upstarts Media)
https://www.upstartsmedia.com/p/unify-ai-gtm-pipeline-40-million-raise

Unify, AI Startup Boosting Sales At Cursor And Perplexity, Raises $40 Million
Unify's Series B funding round values it at $260 million as revenue grows 17% month over month past $5 million. But a clash with GTM darling Clay may be coming up.

Filling out this form will show you a list of state legislative and local offices where you can run based on your current address.
No matter the results, you will also be added automatically to the Run for Something candidate pipeline — check your inbox in the next few days for next steps.
Note: Data includes offices on the ballot in 2025 and 2026. Updates will be made throughout the year as additional information is available.

Where Can I Run — Run for Something Action Fund
Use this tool to find out when, where and how you can run for office.

NetIntent: Leveraging Large Language Models for End-to-End Intent-Based SDN Automation
Md. Kamrul Hossain, Walid Aljoby
https://arxiv.org/abs/2507.14398 ht…

NetIntent: Leveraging Large Language Models for End-to-End Intent-Based SDN Automation
Intent-Based Networking (IBN) often leverages the programmability of Software-Defined Networking (SDN) to simplify network management. However, significant challenges remain in automating the entire pipeline, from user-specified high-level intents to device-specific low-level configurations. Existing solutions often rely on rigid, rule-based translators and fixed APIs, limiting extensibility and adaptability. By contrast, recent advances in large language models (LLMs) offer a promising pathway…

UFL to NFL Pipeline: Tracking Every UFL Player Signed to a 2025 NFL Roster https://www.foxsports.com/stories/nfl/ufl-nfl-pipeline-tracking-every-ufl-player-signed-2025-nfl-roster

Fuck marketing. And the post is conveniently silent about the problem of a hundred tracking pixels on most commercial sites, but this is quite good:
https://www.jonoalderson.com/conjecture/javascript-broke-the-web-and-called-it-progress/

🔄 Significantly improves #RAG pipeline performance by creating context-rich, high-quality text from documents that enhances #AI application accuracy
💼 Addresses critical business challenges where traditional #OCR

Large-scale LH2 pipeline infrastructure concept for airports
H. A. Krog, Y. Jooss, H. Fyhn, P. Neks{\aa}, I. Hjorth
https://arxiv.org/abs/2506.09410 https:…

Large-scale LH2 pipeline infrastructure concept for airports
Infrastructure and processes for handling of liquid hydrogen (LH2) is needed to enable large-scale decarbonization of aviation with hydrogen aircraft. At large airports, pipeline and hydrant systems will be important for a mature hydrogen-powered air travel market. As the vaporization of LH2 is a challenge in fuel handling, the pipeline infrastructure must be designed and operated such that the fuel is subcooled. Through modelling and simulation of aircraft tanks refuelling by a pipeline infras…

UFL to NFL Pipeline: Tracking Every UFL Player Signed to a 2025 NFL Roster https://www.foxsports.com/stories/nfl/ufl-nfl-pipeline-tracking-every-ufl-player-signed-2025-nfl-roster

BlenderFusion: 3D-Grounded Visual Editing and Generative Compositing
Jiacheng Chen, Ramin Mehran, Xuhui Jia, Saining Xie, Sanghyun Woo
https://arxiv.org/abs/2506.17450

BlenderFusion: 3D-Grounded Visual Editing and Generative Compositing
We present BlenderFusion, a generative visual compositing framework that synthesizes new scenes by recomposing objects, camera, and background. It follows a layering-editing-compositing pipeline: (i) segmenting and converting visual inputs into editable 3D entities (layering), (ii) editing them in Blender with 3D-grounded control (editing), and (iii) fusing them into a coherent scene using a generative compositor (compositing). Our generative compositor extends a pre-trained diffusion model to …

I built GeoOpen as an open, historical IP geolocation database. I didn’t expect it to attract so many different users so fast.
Tenzir’s latest blog shows how they use it: "Time-Travel for Investigators: Live & Retro GeoIP/ASN Enrichment".
🔗 Tenzir blog post https://tenzir.com/blog/live-and-retro

Time-Travel for Investigators: Live & Retro GeoIP/ASN Enrichment - Tenzir
The data pipeline engine for security teams. Collect, shape, optimize, enrich, store, replay, and route your security data. As a data pipeline management (DPM) solution, Tenzir automates security data across its entire lifecycle.

Futurity as Infrastructure: A Techno-Philosophical Interpretation of the AI Lifecycle
Mark Cote, Susana Aires
https://arxiv.org/abs/2508.15680 https://arxi…

Futurity as Infrastructure: A Techno-Philosophical Interpretation of the AI Lifecycle
This paper argues that a techno-philosophical reading of the EU AI Act provides insight into the long-term dynamics of data in AI systems, specifically, how the lifecycle from ingestion to deployment generates recursive value chains that challenge existing frameworks for Responsible AI. We introduce a conceptual tool to frame the AI pipeline, spanning data, training regimes, architectures, feature stores, and transfer learning. Using cross-disciplinary methods, we develop a technically grounded…

America’s Gas Pipeline Buildout Is Mainly for Exports—Not Energy Independence – Mother Jones
https://www.motherjones.com/politics/2025/06/liquid-natural-gas-pipeline-projects-exports-energy-independence/

PreQRAG -- Classify and Rewrite for Enhanced RAG
Damian Martinez, Catalina Riano, Hui Fang
https://arxiv.org/abs/2506.17493 https://a…

PreQRAG -- Classify and Rewrite for Enhanced RAG
This paper presents the submission of the UDInfo team to the SIGIR 2025 LiveRAG Challenge. We introduce PreQRAG, a Retrieval Augmented Generation (RAG) architecture designed to improve retrieval and generation quality through targeted question preprocessing. PreQRAG incorporates a pipeline that first classifies each input question as either single-document or multi-document type. For single-document questions, we employ question rewriting techniques to improve retrieval precision and generation…

Fair Play in the Newsroom: Actor-Based Filtering Gender Discrimination in Text Corpora
Stefanie Urchs, Veronika Thurner, Matthias A{\ss}enmacher, Christian Heumann, Stephanie Thiemichen
https://arxiv.org/abs/2508.13169

Fair Play in the Newsroom: Actor-Based Filtering Gender Discrimination in Text Corpora
Large language models are increasingly shaping digital communication, yet their outputs often reflect structural gender imbalances that originate from their training data. This paper presents an extended actor-level pipeline for detecting and mitigating gender discrimination in large-scale text corpora. Building on prior work in discourse-aware fairness analysis, we introduce new actor-level metrics that capture asymmetries in sentiment, syntactic agency, and quotation styles. The pipeline supp…

DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications
Bohao Wang, Zehua Jiang, Zhenyu Yang, Chongwen Huang, Yongliang Shen, Siming Jiang, Chen Zhu, Zhaohui Yang, Richeng Jin, Zhaoyang Zhang, Sami Muhaidat, Merouane Debbah
https://arxiv.org/abs/2508.14507

DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications
Domain-specific datasets are the foundation for unleashing artificial intelligence (AI)-driven wireless innovation. Yet existing wireless AI corpora are slow to produce, offer limited modeling fidelity, and cover only narrow scenario types. To address the challenges, we create DeepTelecom, a three-dimension (3D) digital-twin channel dataset. Specifically, a large language model (LLM)-assisted pipeline first builds the third level of details (LoD3) outdoor and indoor scenes with segmentable mate…

Russland: Ukrainischer Angriff verursacht Brand an Kraftwerk
Eine Ölpumpstation in Unetscha in der westrussischen Region Brjansk, durch die die nach Europa führende Druschba-Ölpipeline verläuft, ist infolge ukrainischer Raketen- und Drohnenangriffe in Brand geraten. Das sagte der Gouverneur der Region, Alexander Bogomaz. Das Feuer ist demnach gelöscht. Ungarn hatte zuvor erklärt, die Öllieferungen über die Druschba-Pipeline s…
📑

Evaluation Pipeline for systematically searching for Anomaly Detection Systems
Florian Rokohl, Alexander Lehnert, Marc Reichenbach
https://arxiv.org/abs/2506.15388

Evaluation Pipeline for systematically searching for Anomaly Detection Systems
Digitalization in the medical world provides major benefits while making it a target for attackers and thus hard to secure. To deal with network intruders we propose an anomaly detection system on hardware to detect malicious clients in real-time. We meet real-time and power restrictions using FPGAs. Overall system performance is achieved via the presented holistic system evaluation.

Accent Normalization Using Self-Supervised Discrete Tokens with Non-Parallel Data
Qibing Bai, Sho Inoue, Shuai Wang, Zhongjie Jiang, Yannan Wang, Haizhou Li
https://arxiv.org/abs/2507.17735

Accent Normalization Using Self-Supervised Discrete Tokens with Non-Parallel Data
Accent normalization converts foreign-accented speech into native-like speech while preserving speaker identity. We propose a novel pipeline using self-supervised discrete tokens and non-parallel training data. The system extracts tokens from source speech, converts them through a dedicated model, and synthesizes the output using flow matching. Our method demonstrates superior performance over a frame-to-frame baseline in naturalness, accentedness reduction, and timbre preservation across multi…

Russia halts oil supplies to Hungary after Ukrainian strike on Druzhba pipeline: https://benborges.xyz/2025/08/18/russia-halts-oil-supplies-to.html

eSapiens: A Real-World NLP Framework for Multimodal Document Understanding and Enterprise Knowledge Processing
Isaac Shi, Zeyuan Li, Wenli Wang, Lewei He, Yang Yang, Tianyu Shi
https://arxiv.org/abs/2506.16768

eSapiens: A Real-World NLP Framework for Multimodal Document Understanding and Enterprise Knowledge Processing
We introduce eSapiens, a unified question-answering system designed for enterprise settings, which bridges structured databases and unstructured textual corpora via a dual-module architecture. The system combines a Text-to-SQL planner with a hybrid Retrieval-Augmented Generation (RAG) pipeline, enabling natural language access to both relational data and free-form documents. To enhance answer faithfulness, the RAG module integrates dense and sparse retrieval, commercial reranking, and a citatio…

VIPCALs: A fully-automated calibration pipeline for VLBI data
Diego \'Alvarez-Ortega, Carolina Casadio, Felix M. P\"otzl, Avinash Kumar, Michael Janssen
https://arxiv.org/abs/2508.13282

VIPCALs: A fully-automated calibration pipeline for VLBI data
Very long baseline interferometry (VLBI) is a powerful technique that can achieve sub-milliarcsecond resolution. However, it requires complex and often manual post-correlation calibration to correct for instrumental, geometric, and propagation-related errors. Unlike connected-element interferometers, VLBI arrays typically provide raw visibilities rather than science-ready data, and existing pipelines are largely semi-automated and reliant on user supervision. We present VIPCALs, a fully automat…

Multi-Object Sketch Animation with Grouping and Motion Trajectory Priors
Guotao Liang, Juncheng Hu, Ximing Xing, Jing Zhang, Qian Yu
https://arxiv.org/abs/2508.15535 https://

Multi-Object Sketch Animation with Grouping and Motion Trajectory Priors
We introduce GroupSketch, a novel method for vector sketch animation that effectively handles multi-object interactions and complex motions. Existing approaches struggle with these scenarios, either being limited to single-object cases or suffering from temporal inconsistency and poor generalization. To address these limitations, our method adopts a two-stage pipeline comprising Motion Initialization and Motion Refinement. In the first stage, the input sketch is interactively divided into seman…

EchoVoices: Preserving Generational Voices and Memories for Seniors and Children
Haiying Xu, Haoze Liu, Mingshi Li, Siyu Cai, Guangxuan Zheng, Yuhuang Jia, Jinghua Zhao, Yong Qin
https://arxiv.org/abs/2507.15221

EchoVoices: Preserving Generational Voices and Memories for Seniors and Children
Recent breakthroughs in intelligent speech and digital human technologies have primarily targeted mainstream adult users, often overlooking the distinct vocal patterns and interaction styles of seniors and children. These demographics possess distinct vocal characteristics, linguistic styles, and interaction patterns that challenge conventional ASR, TTS, and LLM systems. To address this, we introduce EchoVoices, an end-to-end digital human pipeline dedicated to creating persistent digital perso…

GraSP: A Unified Graph-Based Framework for Scalable Generation, Quality Tagging, and Management of Synthetic Data for SFT and DPO
Bidyapati Pradhan, Surajit Dasgupta, Amit Kumar Saha, Omkar Anustoop, Sriram Puttagunta, Vipul Mittal, Gopal Sarda
https://arxiv.org/abs/2508.15432

GraSP: A Unified Graph-Based Framework for Scalable Generation, Quality Tagging, and Management of Synthetic Data for SFT and DPO
The advancement of large language models (LLMs) is critically dependent on the availability of high-quality datasets for Supervised Fine-Tuning (SFT), alignment tasks like Direct Preference Optimization (DPO), etc. In this work, we present a comprehensive synthetic data generation framework that facilitates scalable, configurable, and high-fidelity generation of synthetic data tailored for these training paradigms. Our approach employs a modular and configuration-based pipeline capable of model…

UniCoM: A Universal Code-Switching Speech Generator
Sangmin Lee, Woojin Chung, Seyun Um, Hong-Goo Kang
https://arxiv.org/abs/2508.15244 https://arxiv.org/p…

UniCoM: A Universal Code-Switching Speech Generator
Code-switching (CS), the alternation between two or more languages within a single speaker's utterances, is common in real-world conversations and poses significant challenges for multilingual speech technology. However, systems capable of handling this phenomenon remain underexplored, primarily due to the scarcity of suitable datasets. To resolve this issue, we propose Universal Code-Mixer (UniCoM), a novel pipeline for generating high-quality, natural CS samples without altering sentence sema…

TD-Pipe: Temporally-Disaggregated Pipeline Parallelism Architecture for High-Throughput LLM Inference
Hongbin Zhang, Taosheng Wei, Zhenyi Zheng, Jiangsu Du, Zhiguang Chen, Yutong Lu
https://arxiv.org/abs/2506.10470

TD-Pipe: Temporally-Disaggregated Pipeline Parallelism Architecture for High-Throughput LLM Inference
As the model size continuously increases, pipeline parallelism shows great promise in throughput-oriented LLM inference due to its low demand on communications. However, imbalanced pipeline workloads and complex data dependencies in the prefill and decode phases result in massive pipeline bubbles and further severe performance reduction. To better exploit the pipeline parallelism for high-throughput LLM inference, we propose TD-Pipe, with the key idea lies in the temporally-disaggregated pipeli…

orGAN: A Synthetic Data Augmentation Pipeline for Simultaneous Generation of Surgical Images and Ground Truth Labels
Niran Nataraj, Maina Sogabe, Kenji Kawashima
https://arxiv.org/abs/2506.14303

orGAN: A Synthetic Data Augmentation Pipeline for Simultaneous Generation of Surgical Images and Ground Truth Labels
Deep learning in medical imaging faces obstacles: limited data diversity, ethical issues, high acquisition costs, and the need for precise annotations. Bleeding detection and localization during surgery is especially challenging due to the scarcity of high-quality datasets that reflect real surgical scenarios. We propose orGAN, a GAN-based system for generating high-fidelity, annotated surgical images of bleeding. By leveraging small "mimicking organ" datasets, synthetic models that replicate t…

Ukraine behind new pipeline explosion in Siberia that supplies Russian military-industrial complex, source claims: https://benborges.xyz/2025/07/11/ukraine-behind-new-pipeline-explosion.html

MatchPlant: An Open-Source Pipeline for UAV-Based Single-Plant Detection and Data Extraction
Worasit Sangjan, Piyush Pandey, Norman B. Best, Jacob D. Washburn
https://arxiv.org/abs/2506.12295

MatchPlant: An Open-Source Pipeline for UAV-Based Single-Plant Detection and Data Extraction
Accurate identification of individual plants from unmanned aerial vehicle (UAV) images is essential for advancing high-throughput phenotyping and supporting data-driven decision-making in plant breeding. This study presents MatchPlant, a modular, graphical user interface-supported, open-source Python pipeline for UAV-based single-plant detection and geospatial trait extraction. MatchPlant enables end-to-end workflows by integrating UAV image processing, user-guided annotation, Convolutional Neu…

Accelerating GenAI Workloads by Enabling RISC-V Microkernel Support in IREE
Adeel Ahmad, Ahmad Tameem Kamal, Nouman Amir, Bilal Zafar, Saad Bin Nasir
https://arxiv.org/abs/2508.14899

Accelerating GenAI Workloads by Enabling RISC-V Microkernel Support in IREE
This project enables RISC-V microkernel support in IREE, an MLIR-based machine learning compiler and runtime. The approach begins by enabling the lowering of MLIR linalg dialect contraction ops to linalg.mmt4d op for the RISC-V64 target within the IREE pass pipeline, followed by the development of optimized microkernels for RISC-V. The performance gains are compared with upstream IREE and Llama.cpp for the Llama-3.2-1B-Instruct model.

A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline
Yupeng Jiang, Yao Deng, Sebastian Schroder, Linfeng Liang, Suhaas Gambhir, Alice James, Avishkar Seth, James Pirrie, Yihao Zhang, Xi Zheng
https://arxiv.org/abs/2506.11400

A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline
Autonomous drones are rapidly reshaping industries ranging from aerial delivery and infrastructure inspection to environmental monitoring and disaster response. Ensuring the safety, reliability, and efficiency of these systems is paramount as they transition from research prototypes to mission-critical platforms. This paper presents a step-by-step guide to establishing a robust autonomous drone testing pipeline, covering each critical stage: Software-in-the-Loop (SIL) Simulation Testing, Hardwa…

UFL to NFL Pipeline: Tracking Every UFL Player Signed to a 2025 NFL Roster https://www.foxsports.com/stories/nfl/ufl-nfl-pipeline-tracking-every-ufl-player-signed-2025-nfl-roster

UFL to NFL Pipeline: Tracking Every UFL Player Signed to a 2025 NFL Roster https://www.foxsports.com/stories/nfl/ufl-nfl-pipeline-tracking-every-ufl-player-signed-2025-nfl-roster

Enhanced DeepLab Based Nerve Segmentation with Optimized Tuning
Akhil John Thomas, Christiaan Boerkamp
https://arxiv.org/abs/2507.13394 https://

Enhanced DeepLab Based Nerve Segmentation with Optimized Tuning
Nerve segmentation is crucial in medical imaging for precise identification of nerve structures. This study presents an optimized DeepLabV3-based segmentation pipeline that incorporates automated threshold fine-tuning to improve segmentation accuracy. By refining preprocessing steps and implementing parameter optimization, we achieved a Dice Score of 0.78, an IoU of 0.70, and a Pixel Accuracy of 0.95 on ultrasound nerve imaging. The results demonstrate significant improvements over baseline mod…

SpeedLLM: An FPGA Co-design of Large Language Model Inference Accelerator
Peipei Wang, Wu Guan, Liping Liang, Zhijun Wang, Hanqing Luo, Zhibin Zhang
https://arxiv.org/abs/2507.14139

SpeedLLM: An FPGA Co-design of Large Language Model Inference Accelerator
This paper introduces SpeedLLM, a neural network accelerator designed on the Xilinx Alevo U280 platform and optimized for the Tinyllama framework to enhance edge computing performance. Key innovations include data stream parallelism, a memory reuse strategy, and Llama2 operator fusion, which collectively reduce latency and energy consumption. SpeedLLM's data pipeline architecture optimizes the read-compute-write cycle, while the memory strategy minimizes FPGA resource demands. The operator fusi…

SurveyGen-I: Consistent Scientific Survey Generation with Evolving Plans and Memory-Guided Writing
Jing Chen, Zhiheng Yang, Yixian Shen, Jie Liu, Adam Belloum, Chrysa Papagainni, Paola Grosso
https://arxiv.org/abs/2508.14317

SurveyGen-I: Consistent Scientific Survey Generation with Evolving Plans and Memory-Guided Writing
Survey papers play a critical role in scientific communication by consolidating progress across a field. Recent advances in Large Language Models (LLMs) offer a promising solution by automating key steps in the survey-generation pipeline, such as retrieval, structuring, and summarization. However, existing LLM-based approaches often struggle with maintaining coherence across long, multi-section surveys and providing comprehensive citation coverage. To address these limitations, we introduce Sur…

Ukrainian detained in Italy for alleged Nord Stream sabotage, media report: https://benborges.xyz/2025/08/21/ukrainian-detained-in-italy-for.html

CoFi: A Fast Coarse-to-Fine Few-Shot Pipeline for Glomerular Basement Membrane Segmentation
Hongjin Fang, Daniel Reisenb\"uchler, Kenji Ikemura, Mert R. Sabuncu, Yihe Yang, Ruining Deng
https://arxiv.org/abs/2508.11469

CoFi: A Fast Coarse-to-Fine Few-Shot Pipeline for Glomerular Basement Membrane Segmentation
Accurate segmentation of the glomerular basement membrane (GBM) in electron microscopy (EM) images is fundamental for quantifying membrane thickness and supporting the diagnosis of various kidney diseases. While supervised deep learning approaches achieve high segmentation accuracy, their reliance on extensive pixel-level annotation renders them impractical for clinical workflows. Few-shot learning can reduce this annotation burden but often struggles to capture the fine structural details nece…

A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProx
Rodrigo Tertulino
https://arxiv.org/abs/2508.10017 https…

A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProx
Federated Learning (FL) presents a groundbreaking approach for collaborative health research, allowing model training on decentralized data while safeguarding patient privacy. FL offers formal security guarantees when combined with Differential Privacy (DP). The integration of these technologies, however, introduces a significant trade-off between privacy and clinical utility, a challenge further complicated by the severe class imbalance often present in medical datasets. The research presented…

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…

UFL to NFL Pipeline: Tracking Every UFL Player Signed to a 2025 NFL Roster https://www.foxsports.com/stories/nfl/ufl-nfl-pipeline-tracking-every-ufl-player-signed-2025-nfl-roster

KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problems
Stepan Kulibaba, Artem Dzhalilov, Roman Pakhomov, Oleg Svidchenko, Alexander Gasnikov, Aleksei Shpilman
https://arxiv.org/abs/2508.10177

KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problems
Recent Large Language Model (LLM)-based AutoML systems demonstrate impressive capabilities but face significant limitations such as constrained exploration strategies and a severe execution bottleneck. Exploration is hindered by one-shot methods lacking diversity and Monte Carlo Tree Search (MCTS) approaches that fail to recombine strong partial solutions. The execution bottleneck arises from lengthy code validation cycles that stifle iterative refinement. To overcome these challenges, we intro…

Ukraine meldet Angriff auf russische Ölpumpstation
Die Ukraine hat nach Angaben des Militärs eine Ölpumpstation in der russischen Oblast Tambow mit Drohnen angegriffen. Dies habe zur Unterbrechung der Versorgung über die Druschba-Pipeline geführt. "Infolge des Angriffs brach in der Anlage ein Feuer aus. Die Ölförderung durch die Druschba-Hauptpipeline wurde vollständig eingestellt", heißt es in einer Erklärung des ukrainische…
📑

Ukraine-Liveblog: ++ Trump empfängt Selenskyj im Weißen Haus ++
Der ukrainische Präsident Selenskyj ist von US-Präsident Trump mit Handschlag im Weißen Haus empfangen worden. Zuvor waren bereits mehrere europäische Staatschefs dort zu Gesprächen über den Ukraine-Krieg eingetroffen.

DINOv3 with Test-Time Training for Medical Image Registration
Shansong Wang, Mojtaba Safari, Mingzhe Hu, Qiang Li, Chih-Wei Chang, Richard LJ Qiu, Xiaofeng Yang
https://arxiv.org/abs/2508.14809

DINOv3 with Test-Time Training for Medical Image Registration
Prior medical image registration approaches, particularly learning-based methods, often require large amounts of training data, which constrains clinical adoption. To overcome this limitation, we propose a training-free pipeline that relies on a frozen DINOv3 encoder and test-time optimization of the deformation field in feature space. Across two representative benchmarks, the method is accurate and yields regular deformations. On Abdomen MR-CT, it attained the best mean Dice score (DSC) of 0.7…

Russian oil supplies to EU resume after Ukrainian drone strike, Hungary says: https://benborges.xyz/2025/08/20/russian-oil-supplies-to-eu.html

BugGen: A Self-Correcting Multi-Agent LLM Pipeline for Realistic RTL Bug Synthesis
Surya Jasper, Minh Luu, Evan Pan, Aakash Tyagi, Michael Quinn, Jiang Hu, David Kebo Houngninou
https://arxiv.org/abs/2506.10501

BugGen: A Self-Correcting Multi-Agent LLM Pipeline for Realistic RTL Bug Synthesis
Hardware complexity continues to strain verification resources, motivating the adoption of machine learning (ML) methods to improve debug efficiency. However, ML-assisted debugging critically depends on diverse and scalable bug datasets, which existing manual or automated bug insertion methods fail to reliably produce. We introduce BugGen, a first of its kind, fully autonomous, multi-agent pipeline leveraging Large Language Models (LLMs) to systematically generate, insert, and validate realisti…

Towards Unified Multimodal Financial Forecasting: Integrating Sentiment Embeddings and Market Indicators via Cross-Modal Attention
Sarthak Khanna, Armin Berger, David Berghaus, Tobias Deusser, Lorenz Sparrenberg, Rafet Sifa
https://arxiv.org/abs/2508.13327

Towards Unified Multimodal Financial Forecasting: Integrating Sentiment Embeddings and Market Indicators via Cross-Modal Attention
We propose STONK (Stock Optimization using News Knowledge), a multimodal framework integrating numerical market indicators with sentiment-enriched news embeddings to improve daily stock-movement prediction. By combining numerical & textual embeddings via feature concatenation and cross-modal attention, our unified pipeline addresses limitations of isolated analyses. Backtesting shows STONK outperforms numeric-only baselines. A comprehensive evaluation of fusion strategies and model configuratio…

Ukraine war latest: Kyiv behind new pipeline explosion in Siberia, drone strikes reported at Russian aircraft plant: https://benborges.xyz/2025/07/11/ukraine-war-latest-kyiv-behind.html

GaussianArt: Unified Modeling of Geometry and Motion for Articulated Objects
Licheng Shen, Saining Zhang, Honghan Li, Peilin Yang, Zihao Huang, Zongzheng Zhang, Hao Zhao
https://arxiv.org/abs/2508.14891

GaussianArt: Unified Modeling of Geometry and Motion for Articulated Objects
Reconstructing articulated objects is essential for building digital twins of interactive environments. However, prior methods typically decouple geometry and motion by first reconstructing object shape in distinct states and then estimating articulation through post-hoc alignment. This separation complicates the reconstruction pipeline and restricts scalability, especially for objects with complex, multi-part articulation. We introduce a unified representation that jointly models geometry and …

Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning
Shashanka Venkataramanan, Valentinos Pariza, Mohammadreza Salehi, Lukas Knobel, Spyros Gidaris, Elias Ramzi, Andrei Bursuc, Yuki M. Asano
https://arxiv.org/abs/2507.14137

Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning
We present Franca (pronounced Fran-ka): free one; the first fully open-source (data, code, weights) vision foundation model that matches and in many cases surpasses the performance of state-of-the-art proprietary models, e.g., DINOv2, CLIP, SigLIPv2, etc. Our approach is grounded in a transparent training pipeline inspired by Web-SSL and uses publicly available data: ImageNet-21K and a subset of ReLAION-2B. Beyond model release, we tackle critical limitations in SSL clustering methods. While mo…

Design and Implementation of an OCR-Powered Pipeline for Table Extraction from Invoices
Parshva Dhilankumar Patel
https://arxiv.org/abs/2507.07029 https://…

Design and Implementation of an OCR-Powered Pipeline for Table Extraction from Invoices
This paper presents the design and development of an OCR-powered pipeline for efficient table extraction from invoices. The system leverages Tesseract OCR for text recognition and custom post-processing logic to detect, align, and extract structured tabular data from scanned invoice documents. Our approach includes dynamic preprocessing, table boundary detection, and row-column mapping, optimized for noisy and non-standard invoice formats. The resulting pipeline significantly improves data extr…

Speak2Sign3D: A Multi-modal Pipeline for English Speech to American Sign Language Animation
Kazi Mahathir Rahman, Naveed Imtiaz Nafis, Md. Farhan Sadik, Mohammad Al Rafi, Mehedi Hasan Shahed
https://arxiv.org/abs/2507.06530

Speak2Sign3D: A Multi-modal Pipeline for English Speech to American Sign Language Animation
Helping deaf and hard-of-hearing people communicate more easily is the main goal of Automatic Sign Language Translation. Although most past research has focused on turning sign language into text, doing the reverse, turning spoken English into sign language animations, has been largely overlooked. That's because it involves multiple steps, such as understanding speech, translating it into sign-friendly grammar, and generating natural human motion. In this work, we introduce a complete pipeline …