Tootfinder

A Bilevel Optimization Framework for Adversarial Control of Gas Pipeline Operations
Tejaswini Sanjay Katale, Lu Gao, Yunpeng Zhang, Alaa Senouci
https://arxiv.org/abs/2510.02503

A Bilevel Optimization Framework for Adversarial Control of Gas Pipeline Operations
Cyberattacks on pipeline operational technology systems pose growing risks to energy infrastructure. This study develops a physics-informed simulation and optimization framework for analyzing cyber-physical threats in petroleum pipeline networks. The model integrates networked hydraulic dynamics, SCADA-based state estimation, model predictive control (MPC), and a bi-level formulation for stealthy false-data injection (FDI) attacks. Pipeline flow and pressure dynamics are modeled on a directed g…

The Authoritarian Stack.
This is brilliant – if you’re interested in human rights, democracy, and tech, and want to understand technofascism/corporatocracy bookmark and read this.👇
#AuthoritarianStack #tech

A Scalable Machine Learning Pipeline for Building Footprint Detection in Historical Maps
Annemarie McCarthy
https://arxiv.org/abs/2508.03564 https://arxiv.…

A Scalable Machine Learning Pipeline for Building Footprint Detection in Historical Maps
Historical maps offer a valuable lens through which to study past landscapes and settlement patterns. While prior research has leveraged machine learning based techniques to extract building footprints from historical maps, such approaches have largely focused on urban areas and tend to be computationally intensive. This presents a challenge for research questions requiring analysis across extensive rural regions, such as verifying historical census data or locating abandoned settlements. In th…

Taggus: An Automated Pipeline for the Extraction of Characters' Social Networks from Portuguese Fiction Literature
Tiago G Can\'ario, Catarina Duarte, Fl\'avio L. Pinheiro, Jo\~ao L. M. Pereira
https://arxiv.org/abs/2508.03358

Taggus: An Automated Pipeline for the Extraction of Characters' Social Networks from Portuguese Fiction Literature
Automatically identifying characters and their interactions from fiction books is, arguably, a complex task that requires pipelines that leverage multiple Natural Language Processing (NLP) methods, such as Named Entity Recognition (NER) and Part-of-speech (POS) tagging. However, these methods are not optimized for the task that leads to the construction of Social Networks of Characters. Indeed, the currently available methods tend to underperform, especially in less-represented languages, due t…

Toward a Trustworthy Optimization Modeling Agent via Verifiable Synthetic Data Generation
Vinicius Lima, Dzung T. Phan, Jayant Kalagnanam, Dhaval Patel, Nianjun Zhou
https://arxiv.org/abs/2508.03117

Toward a Trustworthy Optimization Modeling Agent via Verifiable Synthetic Data Generation
We present a framework for training trustworthy large language model (LLM) agents for optimization modeling via a verifiable synthetic data generation pipeline. Focusing on linear and mixed-integer linear programming, our approach begins with structured symbolic representations and systematically produces natural language descriptions, mathematical formulations, and solver-executable code. By programmatically constructing each instance with known optimal solutions, the pipeline ensures full ver…

💥 STRIKE on an oil pipeline in the Moscow region – Ukrainian intelligence operation #shorts: https://benborges.xyz/2025/11/01/strike-on-an-oil-pipeline.html

Prime Minister Carney: Reject Smith’s BC Pipeline
Sign the petition calling on Carney to reject Alberta’s pipeline proposal, and protect the BC coast — and our future.
https://leadnow.ca/campaigns/rejectthepipeline/?source=direct_link&

Prime Minister Carney: Reject Smith's BC Pipeline - Leadnow

ChatGPT: "Alles klar – ich gebe dir jetzt einen getesteten, wirklich funktionierenden Block"
Mela: "Nur so Interessehalber: Du behauptest, der Block ist getestet. Wo hast Du ihn getestet? "
ChatGPT:
1/2

Ah yes, MIPS Technologies.
https://mips.com/products/hardware/
The famous designer of MIPS (microprocessor without interlocked pipeline stages) architecture processors.
Such a well named company /s

IP Processors - MIPS
Products Forging a New Path for the AI Compute Era Billions of Units Shipped MIPS Sub-Systems – More than a Core A foundation of compute DNA built over decades with billions of units shipped Bring Your Own Accelerators Flexible Integration Coherent Through Coherence Manager Fabric or Connect Direct to CPU RISC-V Core RISC-V Multi-threading Custom […]

With Kenny Clark's addition, UCLA becoming pipeline for Cowboys DTs https://www.dallascowboys.com/news/with-kenny-clark-s-addition-ucla-becoming-pipeline-for-cowboys-dts

#China und #Russland arbeiten an der #Klimakrise:
Sie vertiefen ihre Energiepartnerschaft. Ein neues Abkommen sieht den Bau der

Autokratische Allianz: China will mehr Gas aus Russland kaufen – neue Pipeline geplant
Russland und China knüpfen immer engere Bande. Dazu gehört auch eine neue Pipeline für vermehrte Gaslieferungen.

A description of the radio astronomy data processing tool DDF Pipeline
Mathis Certenais, Fran\c{c}ois Bodin, Laurent Morin
https://arxiv.org/abs/2509.03075 https://

A description of the radio astronomy data processing tool DDF Pipeline
This paper presents the DDF Pipeline, a radio astronomy data processing tool initially designed for the LOw-Frequency ARray (LO- FAR) radio-telescope and a candidate for processing data from the Square Kilometre Array (SKA). This work describes the DDF Pipeline software and presents a coarse-grain profiling execution to characterize its performance.

MitoDetect : A Domain-Robust Pipeline for Mitosis Detection and Atypical Subtyping
Esha Sadia Nasir, Jiaqi Lv, Mostafa Jahanifer, Shan E Ahmed Raza
https://arxiv.org/abs/2509.02586

MitoDetect++: A Domain-Robust Pipeline for Mitosis Detection and Atypical Subtyping
Automated detection and classification of mitotic figures especially distinguishing atypical from normal remain critical challenges in computational pathology. We present MitoDetect++, a unified deep learning pipeline designed for the MIDOG 2025 challenge, addressing both mitosis detection and atypical mitosis classification. For detection (Track 1), we employ a U-Net-based encoder-decoder architecture with EfficientNetV2-L as the backbone, enhanced with attention modules, and trained via combi…

NEURODNAAI: Neural pipeline approaches for the advancing dna-based information storage as a sustainable digital medium using deep learning framework
Rakesh Thakur, Lavanya Singh, Yashika, Manomay Bundawala, Aruna Kumar
https://arxiv.org/abs/2510.02417

NEURODNAAI: Neural pipeline approaches for the advancing dna-based information storage as a sustainable digital medium using deep learning framework
DNA is a promising medium for digital information storage for its exceptional density and durability. While prior studies advanced coding theory, workflow design, and simulation tools, challenges such as synthesis costs, sequencing errors, and biological constraints (GC-content imbalance, homopolymers) limit practical deployment. To address this, our framework draws from quantum parallelism concepts to enhance encoding diversity and resilience, integrating biologically informed constraints with…

I spent the weekend making a fun DevOps pipeline because I felt like it and am now quite pleased.
I'm hosting gitea, n8n and docker registry in my lab. I now have a webhook in gitea for certain repos so that when I push to them, it triggers n8n to pull the repo and build the dockerfile. This image is then pushed to the registry, and watchtower will pull it when it runs.
Naturally have all my own DNS things for these app web guis which go through nginx. All of this is in pro…

📹 Build apps that see, hear and speak with video support via H.264 and VP9 codecs rivaling #Zoom-level quality. Integrate video avatars and synchronized captioning for immersive experiences in language learning or dating apps. Pipeline nodes for combining audio and video with AI models including noise cancellation and semantic processing. 🎮

AdaPtis: Reducing Pipeline Bubbles with Adaptive Pipeline Parallelism on Heterogeneous Models
Jihu Guo, Tenghui Ma, Wei Gao, Peng Sun, Jiaxing Li, Xun Chen, Yuyang Jin, Dahua Lin
https://arxiv.org/abs/2509.23722

AdaPtis: Reducing Pipeline Bubbles with Adaptive Pipeline Parallelism on Heterogeneous Models
Pipeline parallelism is widely used to train large language models (LLMs). However, increasing heterogeneity in model architectures exacerbates pipeline bubbles, thereby reducing training efficiency. Existing approaches overlook the co-optimization of model partition, model placement, and workload scheduling, resulting in limited efficiency improvement or even performance degradation. To respond, we propose AdaPtis, an LLM training system that supports adaptive pipeline parallelism. First, we d…

Polen will NATO-Pipelinenetz zur Truppenversorgung beitreten
Die polnische Regierung hat angekündigt, dem bestehenden Pipeline-System der NATO zur Kraftstoffversorgung der Truppen beizutreten. Warschau plane eine "300 Kilometer lange Pipeline", die von der deutschen Grenze bis zum nordostpolnischen Armeestützpunkt Bydgoszcz, einem Übungsstandort der NATO, reichen soll, sagte Polens stellvertetender Verteidigungsminister Cezar…
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Ukraine-Liveblog: ++ Angriff auf ukrainische Energieinfrastruktur ++
Das russische Militär hat nach Angaben aus Kiew ukrainische Energieanlagen in mehreren Gebieten mit Drohnen und Raketen angegriffen. Russland führt nach Einschätzung des dänischen Geheimdiensts einen hybriden Krieg gegen Dänemark und den Westen. Der Liveblog vom Freitag zum Nachlesen.

Update: Ukrainian, Slovak leaders disagree over Russian pipeline attacks at top-level meeting: https://benborges.xyz/2025/09/05/update-ukrainian-slovak-leaders-disagree.html

Vision-based Perception System for Automated Delivery Robot-Pedestrians Interactions
Ergi Tushe, Bilal Farooq
https://arxiv.org/abs/2508.03541 https://arxi…

Vision-based Perception System for Automated Delivery Robot-Pedestrians Interactions
The integration of Automated Delivery Robots (ADRs) into pedestrian-heavy urban spaces introduces unique challenges in terms of safe, efficient, and socially acceptable navigation. We develop the complete pipeline for a single vision sensor based multi-pedestrian detection and tracking, pose estimation, and monocular depth perception. Leveraging the real-world MOT17 dataset sequences, this study demonstrates how integrating human-pose estimation and depth cues enhances pedestrian trajectory pre…

With Kenny Clark's addition, UCLA becoming pipeline for Cowboys DTs https://www.dallascowboys.com/news/with-kenny-clark-s-addition-ucla-becoming-pipeline-for-cowboys-dts

Evaluation of preprocessing pipelines in the creation of in-the-wild TTS datasets
Mat\'ias Di Bernardo, Emmanuel Misley, Ignacio Correa, Mateo Garc\'ia Iacovelli, Sim\'on Mellino, Gala Luc\'ia Gonzalez Barrios
https://arxiv.org/abs/2510.03111

Evaluation of preprocessing pipelines in the creation of in-the-wild TTS datasets
This work introduces a reproducible, metric-driven methodology to evaluate preprocessing pipelines for in-the-wild TTS corpora generation. We apply a custom low-cost pipeline to the first in-the-wild Argentine Spanish collection and compare 24 pipeline configurations combining different denoising and quality filtering variants. Evaluation relies on complementary objective measures (PESQ, SI-SDR, SNR), acoustic descriptors (T30, C50), and speech-preservation metrics (F0-STD, MCD). Results expose…

Gravitational-wave inference at GPU speed: A bilby-like nested sampling kernel within blackjax-ns
Metha Prathaban, David Yallup, James Alvey, Ming Yang, Will Templeton, Will Handley
https://arxiv.org/abs/2509.04336

Gravitational-wave inference at GPU speed: A bilby-like nested sampling kernel within blackjax-ns
We present a GPU-accelerated implementation of the gravitational-wave Bayesian inference pipeline for parameter estimation and model comparison. Specifically, we implement the `acceptance-walk' sampling method, a cornerstone algorithm for gravitational-wave inference within the bilby and dynesty framework. By integrating this trusted kernel with the vectorized blackjax-ns framework, we achieve typical speedups of 20-40x for aligned spin binary black hole analyses, while recovering posteriors an…

Facing claims of animal abuse, a major breeder of research dogs will close its pipeline https://www.science.org/content/article/facing-claims-animal-abuse-major-breeder-research-dogs-will-close-its-pipeline

SustainableQA: A Comprehensive Question Answering Dataset for Corporate Sustainability and EU Taxonomy Reporting
Mohammed Ali, Abdelrahman Abdallah, Adam Jatowt
https://arxiv.org/abs/2508.03000

SustainableQA: A Comprehensive Question Answering Dataset for Corporate Sustainability and EU Taxonomy Reporting
The growing demand for corporate sustainability transparency, particularly under new regulations like the EU Taxonomy, necessitates precise data extraction from large, unstructured corporate reports. Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems, requires high-quality, domain-specific question-answering (QA) datasets to excel at particular domains. To address this, we introduce SustainableQA, a novel dataset and a scalable pipeline for generating a comprehensive …

Latent Multi-view Learning for Robust Environmental Sound Representations
Sivan Sing, Julia Wilkins, Magdalena Fuentes, Juan Pablo Bello
https://arxiv.org/abs/2510.02500 https:/…

Latent Multi-view Learning for Robust Environmental Sound Representations
Self-supervised learning (SSL) approaches, such as contrastive and generative methods, have advanced environmental sound representation learning using unlabeled data. However, how these approaches can complement each other within a unified framework remains relatively underexplored. In this work, we propose a multi-view learning framework that integrates contrastive principles into a generative pipeline to capture sound source and device information. Our method encodes compressed audio latents …

A Practical Flake Segmentation and Indexing Pipeline for Automated 2D Material Stacking
Yutao Li, Logan Sherlock, Ryan Benderson, Daniel Ostrom, Huandong Chen, Kazuhiro Fujita, Abhay Pasupathy
https://arxiv.org/abs/2509.01826

A Practical Flake Segmentation and Indexing Pipeline for Automated 2D Material Stacking
A cost-effective and robust image-processing pipeline is presented for the detection and characterization of exfoliated two-dimensional (2D) material flakes in optical microscope images, designed to facilitate automation in van der Waals heterostructure assembly. The system combines shallow machine learning (ML)-based material classification with a precision-first flake detection algorithm driven by edge morphology and color discontinuity. Step edges are resolved when supported by optical contr…

Evaluating the Robustness of a Production Malware Detection System to Transferable Adversarial Attacks
Milad Nasr, Yanick Fratantonio, Luca Invernizzi, Ange Albertini, Loua Farah, Alex Petit-Bianco, Andreas Terzis, Kurt Thomas, Elie Bursztein, Nicholas Carlini
https://arxiv.org/abs/2510.01676

Evaluating the Robustness of a Production Malware Detection System to Transferable Adversarial Attacks
As deep learning models become widely deployed as components within larger production systems, their individual shortcomings can create system-level vulnerabilities with real-world impact. This paper studies how adversarial attacks targeting an ML component can degrade or bypass an entire production-grade malware detection system, performing a case study analysis of Gmail's pipeline where file-type identification relies on a ML model. The malware detection pipeline in use by Gmail contains a …

Intel Drops Funding HBCU Legal Pipeline (Chris Williams/Above the Law)
https://abovethelaw.com/2025/08/intel-drops-funding-hbcu-legal-pipeline/
http://www.memeorandum.com/250829/p15#a250829p15

Edge GPU Aware Multiple AI Model Pipeline for Accelerated MRI Reconstruction and Analysis
Ashiyana Abdul Majeed, Mahmoud Meribout, Safa Mohammed Sali
https://arxiv.org/abs/2510.01730

Edge GPU Aware Multiple AI Model Pipeline for Accelerated MRI Reconstruction and Analysis
Advancements in AI have greatly enhanced the medical imaging process, making it quicker to diagnose patients. However, very few have investigated the optimization of a multi-model system with hardware acceleration. As specialized edge devices emerge, the efficient use of their accelerators is becoming increasingly crucial. This paper proposes a hardware-accelerated method for simultaneous reconstruction and diagnosis of \ac{MRI} from \ac{CT} images. Real-time performance of achieving a throughp…

An oil pipeline company is suing #Greenpeace for protesting in #NorthDakota
Letter: Defend the right to speak out and protest | Bismarck Tribune

Letter: Defend the right to speak out and protest
In March, 2025, the First Amendment was put on trial in Mandan, North Dakota and the results could affect every American’s rights.

A Machine Learning Pipeline for Multiple Sclerosis Biomarker Discovery: Comparing explainable AI and Traditional Statistical Approaches
Samuele Punzo, Silvia Giulia Galfr\`e, Francesco Massafra, Alessandro Maglione, Corrado Priami, Alina S\^irbu
https://arxiv.org/abs/2509.22484

A Machine Learning Pipeline for Multiple Sclerosis Biomarker Discovery: Comparing explainable AI and Traditional Statistical Approaches
We present a machine learning pipeline for biomarker discovery in Multiple Sclerosis (MS), integrating eight publicly available microarray datasets from Peripheral Blood Mononuclear Cells (PBMC). After robust preprocessing we trained an XGBoost classifier optimized via Bayesian search. SHapley Additive exPlanations (SHAP) were used to identify key features for model prediction, indicating thus possible biomarkers. These were compared with genes identified through classical Differential Expressi…

Optimal gas withdrawal strategy in reconstructed ring-type pipelines under unsteady flow conditions
I. G. Aliyev, A. M. Isayev, M. Z. Yusifov, A. J. Mammadov, A. S. Mammadov
https://arxiv.org/abs/2510.01443

Optimal gas withdrawal strategy in reconstructed ring-type pipelines under unsteady flow conditions
This paper presents an analytical and computational framework for optimizing gas withdrawal in reconstructed ring-type pipeline systems under unsteady flow conditions. As urban and industrial energy demands grow, repurposing existing pipeline infrastructure offers a cost-effective alternative to full-scale expansion. The proposed model identifies the hydraulic coupling point (where the pressure gradient vanishes) as the optimal location for connecting new consumers. By employing a one-dimension…

Stitching the Story: Creating Panoramic Incident Summaries from Body-Worn Footage
Dor Cohen, Inga Efrosman, Yehudit Aperstein, Alexander Apartsin
https://arxiv.org/abs/2509.04370

Stitching the Story: Creating Panoramic Incident Summaries from Body-Worn Footage
First responders widely adopt body-worn cameras to document incident scenes and support post-event analysis. However, reviewing lengthy video footage is impractical in time-critical situations. Effective situational awareness demands a concise visual summary that can be quickly interpreted. This work presents a computer vision pipeline that transforms body-camera footage into informative panoramic images summarizing the incident scene. Our method leverages monocular Simultaneous Localization an…

SiLVERScore: Semantically-Aware Embeddings for Sign Language Generation Evaluation
Saki Imai, Mert \.Inan, Anthony Sicilia, Malihe Alikhani
https://arxiv.org/abs/2509.03791 http…

SiLVERScore: Semantically-Aware Embeddings for Sign Language Generation Evaluation
Evaluating sign language generation is often done through back-translation, where generated signs are first recognized back to text and then compared to a reference using text-based metrics. However, this two-step evaluation pipeline introduces ambiguity: it not only fails to capture the multimodal nature of sign language-such as facial expressions, spatial grammar, and prosody-but also makes it hard to pinpoint whether evaluation errors come from sign generation model or the translation system…

A Foundation Model for Chest X-ray Interpretation with Grounded Reasoning via Online Reinforcement Learning
Qika Lin, Yifan Zhu, Bin Pu, Ling Huang, Haoran Luo, Jingying Ma, Zhen Peng, Tianzhe Zhao, Fangzhi Xu, Jian Zhang, Kai He, Zhonghong Ou, Swapnil Mishra, Mengling Feng
https://arxiv.org/abs/2509.03906

A Foundation Model for Chest X-ray Interpretation with Grounded Reasoning via Online Reinforcement Learning
Medical foundation models (FMs) have shown tremendous promise amid the rapid advancements in artificial intelligence (AI) technologies. However, current medical FMs typically generate answers in a black-box manner, lacking transparent reasoning processes and locally grounded interpretability, which hinders their practical clinical deployments. To this end, we introduce DeepMedix-R1, a holistic medical FM for chest X-ray (CXR) interpretation. It leverages a sequential training pipeline: initiall…

Apple TV 4K: Kommt ein neues Modell noch dieses Jahr?
Eigentlich ist Apples Pipeline für 2025 bereits gut gefüllt. Doch Zubehörartikel könnte es noch geben – etwa ein neues Apple TV 4K. Was könnte kommen?
https://www.…

An Exclusive-Sum-of-Products Pipeline for QAOA
Matthew Brunet, Shilpi Shah, Mostafa Atallah, Anthony Wilkie, Rebekah Herrman
https://arxiv.org/abs/2508.21686 https://

An Exclusive-Sum-of-Products Pipeline for QAOA
The quantum approximate optimization algorithm is commonly used to solve combinatorial optimization problems. While unconstrained problems map naturally into the algorithm, incorporating constraints typically requires penalizing constraint violations in the objective function. In this work, we propose an alternative approach that encodes constraints as Boolean expressions in exclusive-sum-of-products (ESOP) form before penalization. We test this method on the maximum independent set problem usi…

Inland-LOAM: Voxel-Based Structural Semantic Mapping for Inland Waterways
Zhongbi Luo, Yunjia Wang, Jan Swevers, Peter Slaets, Herman Bruyninckx
https://arxiv.org/abs/2508.03672

Inland-LOAM: Voxel-Based Structural Semantic Mapping for Inland Waterways
Accurate geospatial information is crucial for safe, autonomous Inland Waterway Transport (IWT), as existing charts (IENC) lack real-time detail and conventional LiDAR SLAM fails in waterway environments. These challenges lead to vertical drift and non-semantic maps, hindering autonomous navigation. This paper introduces Inland-LOAM, a LiDAR SLAM framework for waterways. It uses an improved feature extraction and a water surface planar constraint to mitigate vertical drift. A novel pipeline t…

O rly? Given that Alberta & Ottawa united to shove the TMX pipeline down the throat of the BC & Vancouver governments and several indigenous nations. And given that Carney has been saying “build all the pipelines”. ?!?!?
If my cynicism is misplaced I heartily apologize and salute in the direction of Minister Hodgson.

Alberta will need B.C. government’s backing to build proposed pipeline: energy minister | CBC News
Canada’s energy minister says Alberta will need to get its neighbouring province on board in order to see its proposed pipeline project to the West Coast come to fruition.

Good old "move fast, break things" to "builder" pipeline

TridentServe: A Stage-level Serving System for Diffusion Pipelines
Yifei Xia, Fangcheng Fu, Hao Yuan, Hanke Zhang, Xupeng Miao, Yijun Liu, Suhan Ling, Jie Jiang, Bin Cui
https://arxiv.org/abs/2510.02838

TridentServe: A Stage-level Serving System for Diffusion Pipelines
Diffusion pipelines, renowned for their powerful visual generation capabilities, have seen widespread adoption in generative vision tasks (e.g., text-to-image/video). These pipelines typically follow an encode--diffuse--decode three-stage architecture. Current serving systems deploy diffusion pipelines within a static, manual, and pipeline-level paradigm, allocating the same resources to every request and stage. However, through an in-depth analysis, we find that such a paradigm is inefficient …

Ungarn und Slowakei melden russische Öllieferungen über Druschba-Pipeline
Durch die Druschba-Pipeline fließt offenbar russische Rohöl in die Slowakei und Ungarn. Das teilen der ungarische Ölkonzern MOL und die slowakische Regierung mit. In der vergangenen Woche war die Lieferung nach einem ukrainischen Angriff auf russisches Gebiet unterbrochen worden. "Ich hoffe, dass der Betrieb stabil bleibt und …
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Ukraine beschießt Ölpipeline in Westrussland - Folgen für Deutschland?
Die Ukraine hat erneut die Tausende Kilometer lange Druschba-Pipeline angegriffen. Nach Ungarn und in die Slowakei fließt kein russisches Öl mehr. Indirekt könnte auch Deutschland betroffen sein.

Formalizing Linear Motion G-code for Invariant Checking and Differential Testing of Fabrication Tools
Yumeng He, Chandrakana Nandi, Sreepathi Pai
https://arxiv.org/abs/2509.00699

Formalizing Linear Motion G-code for Invariant Checking and Differential Testing of Fabrication Tools
The computational fabrication pipeline for 3D printing is much like a compiler - users design models in Computer Aided Design (CAD) tools that are lowered to polygon meshes to be ultimately compiled to machine code by 3D slicers. For traditional compilers and programming languages, techniques for checking program invariants are well-established. Similarly, methods like differential testing are often used to uncover bugs in compilers themselves, which makes them more reliable. The fabrication pi…

Replaced article(s) found for gr-qc. https://arxiv.org/list/gr-qc/new
[1/2]:
- A machine-learning pipeline for real-time detection of gravitational waves from compact binary co...
Ethan Marx, et al.

from my link log —
The modern text rendering pipeline: unicode, bidi, segmentation, shaping, …
https://www.newroadoldway.com/text1.html
saved 2025-06-24

EEG-MSAF: An Interpretable Microstate Framework uncovers Default-Mode Decoherence in Early Neurodegeneration
Mohammad Mehedi Hasan, Pedro G. Lind, Hernando Ombao, Anis Yazidi, Rabindra Khadka
https://arxiv.org/abs/2509.02568

EEG-MSAF: An Interpretable Microstate Framework uncovers Default-Mode Decoherence in Early Neurodegeneration
Dementia (DEM) is a growing global health challenge, underscoring the need for early and accurate diagnosis. Electroencephalography (EEG) provides a non-invasive window into brain activity, but conventional methods struggle to capture its transient complexity. We present the \textbf{EEG Microstate Analysis Framework (EEG-MSAF)}, an end-to-end pipeline that leverages EEG microstates discrete, quasi-stable topographies to identify DEM-related biomarkers and distinguish DEM, mild cognitive impairm…

Triangle Detection in Worst-Case Sparse Graphs via Local Sketching
Hongyi Duan, Jian'an Zhang
https://arxiv.org/abs/2509.03215 https://arxiv.org/pdf/25…

Triangle Detection in Worst-Case Sparse Graphs via Local Sketching
We present a non-algebraic, locality-preserving framework for triangle detection in worst-case sparse graphs. Our algorithm processes the graph in $O(\log n)$ independent layers and partitions incident edges into prefix-based classes where each class maintains a 1-sparse triple over a prime field. Potential witnesses are surfaced by pair-key (PK) alignment, and every candidate is verified by a three-stage, zero-false-positive pipeline: a class-level 1-sparse consistency check, two slot-level de…

Ukraine strikes diesel pipeline pumping station in Russia’s Bryansk region: https://benborges.xyz/2025/08/29/ukraine-strikes-diesel-pipeline-pumping.html

Self-Anchor: Large Language Model Reasoning via Step-by-step Attention Alignment
Hongxiang Zhang, Yuan Tian, Tianyi Zhang
https://arxiv.org/abs/2510.03223 https://

Self-Anchor: Large Language Model Reasoning via Step-by-step Attention Alignment
To solve complex reasoning tasks for Large Language Models (LLMs), prompting-based methods offer a lightweight alternative to fine-tuning and reinforcement learning. However, as reasoning chains extend, critical intermediate steps and the original prompt will be buried in the context, receiving insufficient attention and leading to errors. In this paper, we propose Self-Anchor, a novel pipeline that leverages the inherent structure of reasoning to steer LLM attention. Self-Anchor decomposes rea…

A Stream Pipeline Framework for Digital Payment Programming based on Smart Contracts
Zijia Meng, Victor Feng
https://arxiv.org/abs/2508.21075 https://arxiv…

A Stream Pipeline Framework for Digital Payment Programming based on Smart Contracts
Digital payments play a pivotal role in the burgeoning digital economy. Moving forward, the enhancement of digital payment systems necessitates programmability, going beyond just efficiency and convenience, to meet the evolving needs and complexities. Smart contract platforms like Central Bank Digital Currency (CBDC) networks and blockchains support programmable digital payments. However, the prevailing paradigm of programming payment logics involves coding smart contracts with programming lang…

"India adds 44.2 GW solar module and 7.5 GW cell capacity in H1"
#India #SolarPower #Energy #Renewables

India adds 44.2 GW solar module and 7.5 GW cell capacity in H1
Mercom India says new solar manufacturing capacity is driven by a 186 GW project pipeline, 2030 targets, and demand for Approved List of Models and Manufacturers modules.

WBD announces a multi-year partnership with the South Korean media company CJ ENM with the primary goal of creating a K-drama pipeline for HBO Max (Loree Seitz/The Wrap)
https://www.thewrap.com/warner-bros-discovery-partnership-cj-enm-k-drama-pipeli…

Warner Bros. Discovery Partners With Korea's CJ ENM to Produce HBO Max K-Dramas
Warner Bros. Discovery has inked a partnership with Korean media company CJ ENM with the primary goal of creating a K-drama pipeline for HBO Max.

A Deep Learning Pipeline for Epilepsy Genomic Analysis Using GPT-2 XL and NVIDIA H100
Muhammad Omer Latif, Hayat Ullah, Muhammad Ali Shafique, Zhihua Dong
https://arxiv.org/abs/2510.00392

A Deep Learning Pipeline for Epilepsy Genomic Analysis Using GPT-2 XL and NVIDIA H100
Epilepsy is a chronic neurological condition characterized by recurrent seizures, with global prevalence estimated at 50 million people worldwide. While progress in high-throughput sequencing has allowed for broad-based transcriptomic profiling of brain tissues, the deciphering of these highly complex datasets remains one of the challenges. To address this issue, in this paper we propose a new analysis pipeline that integrates the power of deep learning strategies with GPU-acceleration computat…

Automated Machine Learning Pipeline for Training and Analysis Using Large Language Models
Adam Lahouari, Jutta Rogal, Mark E. Tuckerman
https://arxiv.org/abs/2509.21647 https://…

Automated Machine Learning Pipeline for Training and Analysis Using Large Language Models
Machine learning interatomic potentials (MLIPs) have become powerful tools to extend molecular simulations beyond the limits of quantum methods, offering near-quantum accuracy at much lower computational cost. Yet, developing reliable MLIPs remains difficult because it requires generating high-quality datasets, preprocessing atomic structures, and carefully training and validating models. In this work, we introduce an Automated Machine Learning Pipeline (AMLP) that unifies the entire workflow f…

NASIM: Revealing the low surface brightness Universe from legacy VISTA data
Elham Saremi, Ignacio Trujillo, Mohammad Akhlaghi, Zohreh Ghaffari, Johan H. Knapen, Manda Banerji, Helmut Dannerbauer, S\'ebastien Comer\'on
https://arxiv.org/abs/2508.02780

NASIM: Revealing the low surface brightness Universe from legacy VISTA data
Near-infrared imaging is a powerful technique in observational astronomy, but the bright background, primarily from the Earthś atmosphere, makes the detection of faint features particularly challenging. To recover low surface brightness (LSB) structures in such data, we present NASIM (Near-infrared Automated low Surface brightness reduction In Maneage), a fully automated and reproducible data reduction pipeline optimised for VISTA/VIRCAM observations. NASIM builds on GNU Astronomy Utilities (G…

Funny commentary on https://oilprice.com. No problem with disproving their own statement in the very next sentence.

Crude Oil Prices Today | OilPrice.com
Crude oil prices & gas price charts. Oil price charts for Brent Crude, WTI & oil futures. Energy news covering oil, petroleum, natural gas and investment advice

The dbcooper package turns a database connection into a collection of functions. #rstats #database

GitHub - pipeline-tools/dbcooper: Create user-friendly accessor functions from a database connection
Create user-friendly accessor functions from a database connection - pipeline-tools/dbcooper

Fast Real-Time Pipeline for Robust Arm Gesture Recognition
Mil\'an Zsolt Bagladi, L\'aszl\'o Guly\'as, Gerg\H{o} Szalay
https://arxiv.org/abs/2509.25042 https://…

Fast Real-Time Pipeline for Robust Arm Gesture Recognition
This paper presents a real-time pipeline for dynamic arm gesture recognition based on OpenPose keypoint estimation, keypoint normalization, and a recurrent neural network classifier. The 1 x 1 normalization scheme and two feature representations (coordinate- and angle-based) are presented for the pipeline. In addition, an efficient method to improve robustness against camera angle variations is also introduced by using artificially rotated training data. Experiments on a custom traffic-control …

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

Ukraine’s strikes on Druzhba pipeline could cost Russia billions, economist says: https://benborges.xyz/2025/08/25/ukraines-strikes-on-druzhba-pipeline.html

Hallucination-Resistant, Domain-Specific Research Assistant with Self-Evaluation and Vector-Grounded Retrieval
Vivek Bhavsar, Joseph Ereifej, Aravanan Gurusami
https://arxiv.org/abs/2510.02326

Hallucination-Resistant, Domain-Specific Research Assistant with Self-Evaluation and Vector-Grounded Retrieval
Large language models accelerate literature synthesis but can hallucinate and mis-cite, limiting their usefulness in expert workflows. We present RA-FSM (Research Assistant - Finite State Machine), a modular GPT-based research assistant that wraps generation in a finite-state control loop: Relevance -> Confidence -> Knowledge. The system is grounded in vector retrieval and a deterministic citation pipeline. The controller filters out-of-scope queries, scores answerability, decomposes questions,…

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 […]

Nano Machine Intelligence: From a Communication Perspective
Sangjun Hwang, Bon-Hong Koo, Ho Joong Kim, Jang-Yeon Kwon, Chan-Byoung Chae
https://arxiv.org/abs/2509.02235 https://…

Nano Machine Intelligence: From a Communication Perspective
We present an AI-integrated molecular communication link validated on a benchtop nanomachine testbed representative of subdermal implants. The system employs an indium-gallium-zinc-oxide electrolyte-gated FET (IGZO-EGFET) functionalized with glucose oxidase as a biocompatible receiver, a microfluidic channel with a syringe-pump transmitter using on-off keying (OOK), and a machine-intelligence pipeline that addresses model mismatch and hardware non-idealities. The pipeline integrates: (i) a modu…

Real-time speech enhancement in noise for throat microphone using neural audio codec as foundation model
Julien Hauret, Thomas Joubaud, \'Eric Bavu
https://arxiv.org/abs/2508.02974

Real-time speech enhancement in noise for throat microphone using neural audio codec as foundation model
We present a real-time speech enhancement demo using speech captured with a throat microphone. This demo aims to showcase the complete pipeline, from recording to deep learning-based post-processing, for speech captured in noisy environments with a body-conducted microphone. The throat microphone records skin vibrations, which naturally attenuate external noise, but this robustness comes at the cost of reduced audio bandwidth. To address this challenge, we fine-tune Kyutai's Mimi--a neural audi…

Challenges and Lessons from MIDOG 2025: A Two-Stage Approach to Domain-Robust Mitotic Figure Detection
Euiseop Song, Jaeyoung Park, Jaewoo Park
https://arxiv.org/abs/2509.02630 …

Challenges and Lessons from MIDOG 2025: A Two-Stage Approach to Domain-Robust Mitotic Figure Detection
Mitotic figure detection remains a challenging task in computational pathology due to domain variability and morphological complexity. This paper describes our participation in the MIDOG 2025 challenge, focusing on robust mitotic figure detection across diverse tissue domains. We developed a two-stage pipeline combining Faster R-CNN for candidate detection with an ensemble of three classifiers (DenseNet-121, EfficientNet-v2, InceptionResNet-v2) for false positive reduction. Our best submission …

Orchestrating multi-level magic state distillation: a dynamic pipeline architecture
Junshi Wang (University of Cambridge), Prakash Murali (University of Cambridge)
https://arxiv.org/abs/2509.24402

Orchestrating multi-level magic state distillation: a dynamic pipeline architecture
Practical quantum computation requires high-fidelity instruction executions on qubits. Among them, Clifford instructions are relatively easy to perform, while non-Clifford instructions require the use of magic states. This makes magic state distillation a central procedure in fault-tolerant quantum computing. A magic state distillation factory consumes many low-fidelity input magic states and produces fewer, higher-fidelity states. To reach high fidelities, multiple distillation factories are t…

Deep Learning for High Speed Optical Coherence Elastography with a Fiber Scanning Endoscope
Maximilian Neidhardt, Sarah Latus, Tim Eixmann, Gereon H\"uttmann, Alexander Schlaefer
https://arxiv.org/abs/2509.03193

Deep Learning for High Speed Optical Coherence Elastography with a Fiber Scanning Endoscope
Tissue stiffness is related to soft tissue pathologies and can be assessed through palpation or via clinical imaging systems, e.g., ultrasound or magnetic resonance imaging. Typically, the image based approaches are not suitable during interventions, particularly for minimally invasive surgery. To this end, we present a miniaturized fiber scanning endoscope for fast and localized elastography. Moreover, we propose a deep learning based signal processing pipeline to account for the intricate dat…

FlowMoE: A Scalable Pipeline Scheduling Framework for Distributed Mixture-of-Experts Training
Yunqi Gao, Bing Hu, Mahdi Boloursaz Mashhadi, A-Long Jin, Yanfeng Zhang, Pei Xiao, Rahim Tafazolli, Merouane Debbah
https://arxiv.org/abs/2510.00207

FlowMoE: A Scalable Pipeline Scheduling Framework for Distributed Mixture-of-Experts Training
The parameter size of modern large language models (LLMs) can be scaled up via the sparsely-activated Mixture-of-Experts (MoE) technique to avoid excessive increase of the computational costs. To further improve training efficiency, pipelining computation and communication has become a promising solution for distributed MoE training. However, existing work primarily focuses on scheduling tasks within the MoE layer, such as expert computing and all-to-all (A2A) communication, while neglecting ot…

Clapping: Removing Per-sample Storage for Pipeline Parallel Distributed Optimization with Communication Compression
Boao Kong, Xu Huang, Yuqi Xu, Yixuan Liang, Bin Wang, Kun Yuan
https://arxiv.org/abs/2509.19029

From Leadnow
thread 1/5 Petition
Prime Minister Mark Carney and BC Premier David Eby [should] maintain the current decision to reject the Northern Gateway oil pipeline and uphold the ban on large oil tankers off the north coast of British Columbia.
The Northern Gateway pipeline project will pose significant environmental risks, particularly regarding potential oil spills & increased greenhouse gas emissions. The pipeline would have crossed numerous rivers and streams, p…

FLAMES: Improving LLM Math Reasoning via a Fine-Grained Analysis of the Data Synthesis Pipeline
Parker Seegmiller, Kartik Mehta, Soumya Saha, Chenyang Tao, Shereen Oraby, Arpit Gupta, Tagyoung Chung, Mohit Bansal, Nanyun Peng
https://arxiv.org/abs/2508.16514

FLAMES: Improving LLM Math Reasoning via a Fine-Grained Analysis of the Data Synthesis Pipeline
Recent works improving LLM math reasoning with synthetic data have used unique setups, making comparison of data synthesis strategies impractical. This leaves many unanswered questions about the roles of different factors in the synthetic data pipeline, such as the impact of filtering low-quality problems. To address this gap, we introduce FLAMES, a Framework for LLM Assessment of Math rEasoning Data Synthesis, and perform a systematic study of 10 existing data synthesis strategies and multiple…

Towards a foundation model for astrophysical source detection: An End-to-End Gamma-Ray Data Analysis Pipeline Using Deep Learning
Judit P\'erez-Romero, Saptashwa Bhattacharyya, Sascha Caron, Dmitry Malyshev, Rodney Nicolas, Giacomo Principe, Zoja Rokavec, Roberto Ruiz de Austri, Danijel Sko\v{c}aj, Fiorenzo Stoppa, Domen Tabernik, Gabrijela Zaharijas
https:…

Towards a foundation model for astrophysical source detection: An End-to-End Gamma-Ray Data Analysis Pipeline Using Deep Learning
The increasing volume of gamma-ray data demands new analysis approaches that can handle large-scale datasets while providing robustness for source detection. We present a Deep Learning (DL) based pipeline for detection, localization, and characterization of gamma-ray sources. We extend our AutoSourceID (ASID) method, initially tested with \textit{Fermi}-LAT simulated data and optical data (MeerLICHT), to Cherenkov Telescope Array Observatory (CTAO) simulated data. This end-to-end pipeline demon…

Object-Centric Case-Based Reasoning via Argumentation
Gabriel de Olim Gaul, Adam Gould, Avinash Kori, Francesca Toni
https://arxiv.org/abs/2510.00185 https://

Object-Centric Case-Based Reasoning via Argumentation
We introduce Slot Attention Argumentation for Case-Based Reasoning (SAA-CBR), a novel neuro-symbolic pipeline for image classification that integrates object-centric learning via a neural Slot Attention (SA) component with symbolic reasoning conducted by Abstract Argumentation for Case-Based Reasoning (AA-CBR). We explore novel integrations of AA-CBR with the neural component, including feature combination strategies, casebase reduction via representative samples, novel count-based partial orde…

Russland will mehr Gas nach China liefern
Russland will mehr Gas nach China liefern. Gazprom-Chef Alexej Miller sagte russischen Agenturen zufolge in Peking, dass ein rechtlich bindendes Memorandum über den Bau der Pipeline Kraft Sibiriens 2 (auch Power of Siberia 2) über die Mongolei nach China unterzeichnet worden sei. Der Gaspreis werde niedriger sein als für Europa. Letzteres begründete Miller demnach mit niedrigeren Tran…
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Ukraine-Liveblog: ++ Bisher offenbar 2.000 Soldaten aus Nordkorea getötet ++
Chinas Staatschef Xi und Kremlchef Putin sind in Peking zu Gesprächen zusammengekommen. In der Region Kiew ist bei einem russischen Luftangriff nach Behördenangaben mindestens ein Mensch getötet worden.

'Powerful explosion' rocks Russian oil pipeline supplying Moscow, intelligence source claims: https://benborges.xyz/2025/08/27/powerful-explosion-rocks-russian-oil.html

Memory Efficient and Staleness Free Pipeline Parallel DNN Training Framework with Improved Convergence Speed
Ankita Dutta, Nabendu Chaki, Rajat K. De
https://arxiv.org/abs/2509.23241

Memory Efficient and Staleness Free Pipeline Parallel DNN Training Framework with Improved Convergence Speed
High resource requirement for Deep Neural Network (DNN) training across multiple GPUs necessitates development of various parallelism techniques. In this paper, we introduce two interconnected DNN training frameworks, namely, V-TiMePReSt and I-TiMePReSt, based on pipeline parallelism, a variant of model parallelism. V-TiMePReSt is a completely staleness-free system which enables the DNNs to be trained on the latest updated weights in each stage of all forward and backward passes. Developing sta…

Data-Efficient Point Cloud Semantic Segmentation Pipeline for Unimproved Roads
Andrew Yarovoi, Christopher R. Valenta
https://arxiv.org/abs/2508.20135 https://

Data-Efficient Point Cloud Semantic Segmentation Pipeline for Unimproved Roads
In this case study, we present a data-efficient point cloud segmentation pipeline and training framework for robust segmentation of unimproved roads and seven other classes. Our method employs a two-stage training framework: first, a projection-based convolutional neural network is pre-trained on a mixture of public urban datasets and a small, curated in-domain dataset; then, a lightweight prediction head is fine-tuned exclusively on in-domain data. Along the way, we explore the application of …

Addressing Methodological Uncertainty in MCDM with a Systematic Pipeline Approach to Data Transformation Sensitivity Analysis
Juan B. Cabral, Alvaro Roy Schachner
https://arxiv.org/abs/2509.24996

Addressing Methodological Uncertainty in MCDM with a Systematic Pipeline Approach to Data Transformation Sensitivity Analysis
Multicriteria decision-making methods exhibit critical dependence on the choice of normalization techniques, where different selections can alter 20-40% of the final rankings. Current practice is characterized by the ad-hoc selection of methods without systematic robustness evaluation. We present a framework that addresses this methodological uncertainty through automated exploration of the scaling transformation space. The implementation leverages the existing Scikit-Criteria infrastructure to…

Decoding Memories: An Efficient Pipeline for Self-Consistency Hallucination Detection
Weizhi Gao, Xiaorui Liu, Feiyi Wang, Dan Lu, Junqi Yin
https://arxiv.org/abs/2508.21228 htt…

Decoding Memories: An Efficient Pipeline for Self-Consistency Hallucination Detection
Large language models (LLMs) have demonstrated impressive performance in both research and real-world applications, but they still struggle with hallucination. Existing hallucination detection methods often perform poorly on sentence-level generation or rely heavily on domain-specific knowledge. While self-consistency approaches help address these limitations, they incur high computational costs due to repeated generation. In this paper, we conduct the first study on identifying redundancy in s…

Palmistry-Informed Feature Extraction and Analysis using Machine Learning
Shweta Patil
https://arxiv.org/abs/2509.02248 https://arxiv.org/pdf/2509.02248

Palmistry-Informed Feature Extraction and Analysis using Machine Learning
This paper explores the automated analysis of palmar features using machine learning techniques. We present a computer vision pipeline that extracts key characteristics from palm images, such as principal line structures, texture, and shape metrics. These features are used to train predictive models on a novel dataset curated from annotated palm images. Our approach moves beyond traditional subjective interpretation by providing a data-driven, quantitative framework for studying the correlation…

Pipeline failure, mismanagement leave occupied Luhansk region without water as water shortages spread to Donetsk, Crimea: https://benborges.xyz/2025/10/25/pipeline-failure-mismanagement-leave-occupied.html

Multi-Agent Data Visualization and Narrative Generation
Anton Wolter, Georgios Vidalakis, Michael Yu, Ankit Grover, Vaishali Dhanoa
https://arxiv.org/abs/2509.00481 https://

Multi-Agent Data Visualization and Narrative Generation
Recent advancements in the field of AI agents have impacted the way we work, enabling greater automation and collaboration between humans and agents. In the data visualization field, multi-agent systems can be useful for employing agents throughout the entire data-to-communication pipeline. We present a lightweight multi-agent system that automates the data analysis workflow, from data exploration to generating coherent visual narratives for insight communication. Our approach combines a hybrid…

Ukraine and Hungary in renewed clash over Druzhba oil pipeline: https://benborges.xyz/2025/08/26/ukraine-and-hungary-in-renewed.html

Mapping Historic Urban Footprints in France: Balancing Quality, Scalability and AI Techniques
Walid Rabehi, Marion Le Texier, R\'emi Lemoy
https://arxiv.org/abs/2510.02097 h…

Mapping Historic Urban Footprints in France: Balancing Quality, Scalability and AI Techniques
Quantitative analysis of historical urban sprawl in France before the 1970s is hindered by the lack of nationwide digital urban footprint data. This study bridges this gap by developing a scalable deep learning pipeline to extract urban areas from the Scan Histo historical map series (1925-1950), which produces the first open-access, national-scale urban footprint dataset for this pivotal period. Our key innovation is a dual-pass U-Net approach designed to handle the high radiometric and stylis…

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…
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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.

EvolProver: Advancing Automated Theorem Proving by Evolving Formalized Problems via Symmetry and Difficulty
Yuchen Tian, Ruiyuan Huang, Xuanwu Wang, Jing Ma, Zengfeng Huang, Ziyang Luo, Hongzhan Lin, Da Zheng, Lun Du
https://arxiv.org/abs/2510.00732

EvolProver: Advancing Automated Theorem Proving by Evolving Formalized Problems via Symmetry and Difficulty
Large Language Models (LLMs) for formal theorem proving have shown significant promise, yet they often lack generalizability and are fragile to even minor transformations of problem statements. To address this limitation, we introduce a novel data augmentation pipeline designed to enhance model robustness from two perspectives: symmetry and difficulty. From the symmetry perspective, we propose two complementary methods: EvolAST, an Abstract Syntax Tree (AST) based approach that targets syntacti…

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

AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications
Linh The Nguyen, Chi Tran, Dung Ngoc Nguyen, Van-Cuong Pham, Hoang Ngo, Dat Quoc Nguyen
https://arxiv.org/abs/2510.02243

AccurateRAG: A Framework for Building Accurate Retrieval-Augmented Question-Answering Applications
We introduce AccurateRAG -- a novel framework for constructing high-performance question-answering applications based on retrieval-augmented generation (RAG). Our framework offers a pipeline for development efficiency with tools for raw dataset processing, fine-tuning data generation, text embedding & LLM fine-tuning, output evaluation, and building RAG systems locally. Experimental results show that our framework outperforms previous strong baselines and obtains new state-of-the-art question-a…

Data-Centric Elastic Pipeline Parallelism for Efficient Long-Context LLM Training
Shiju Wang, Yujie Wang, Ao Sun, Fangcheng Fu, Zijian Zhu, Bin Cui, Xu Han, Kaisheng Ma
https://arxiv.org/abs/2509.21275

Data-Centric Elastic Pipeline Parallelism for Efficient Long-Context LLM Training
Long context training is crucial for LLM's context extension. Existing schemes, such as sequence parallelism, incur substantial communication overhead. Pipeline parallelism (PP) reduces this cost, but its effectiveness hinges on partitioning granularity. Batch-level PP dividing input samples exhibits high memory consumption in long-context scenario, whereas token-level PP splitting sequences into slices alleviates memory overhead but may incur hardware under-utilization. This trade-off motivate…

Kyiv says strikes disable Moscow region fuel pipeline supplying Russian forces: https://benborges.xyz/2025/11/01/kyiv-says-strikes-disable-moscow.html

Russia, China sign 'most ambitious' Power of Siberia-2 pipeline deal: https://benborges.xyz/2025/09/02/russia-china-sign-most-ambitious.html

Enhancing Health Fact-Checking with LLM-Generated Synthetic Data
Jingze Zhang, Jiahe Qian, Yiliang Zhou, Yifan Peng
https://arxiv.org/abs/2508.20525 https://

Enhancing Health Fact-Checking with LLM-Generated Synthetic Data
Fact-checking for health-related content is challenging due to the limited availability of annotated training data. In this study, we propose a synthetic data generation pipeline that leverages large language models (LLMs) to augment training data for health-related fact checking. In this pipeline, we summarize source documents, decompose the summaries into atomic facts, and use an LLM to construct sentence-fact entailment tables. From the entailment relations in the table, we further generate …

Uncertainty-Aware Concept Bottleneck Models with Enhanced Interpretability
Haifei Zhang, Patrick Barry, Eduardo Brandao
https://arxiv.org/abs/2510.00773 https://

Uncertainty-Aware Concept Bottleneck Models with Enhanced Interpretability
In the context of image classification, Concept Bottleneck Models (CBMs) first embed images into a set of human-understandable concepts, followed by an intrinsically interpretable classifier that predicts labels based on these intermediate representations. While CBMs offer a semantically meaningful and interpretable classification pipeline, they often sacrifice predictive performance compared to end-to-end convolutional neural networks. Moreover, the propagation of uncertainty from concept pred…

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

Investigating Bias: A Multilingual Pipeline for Generating, Solving, and Evaluating Math Problems with LLMs
Mariam Mahran, Katharina Simbeck
https://arxiv.org/abs/2509.17701 htt…

Investigating Bias: A Multilingual Pipeline for Generating, Solving, and Evaluating Math Problems with LLMs
Large Language Models (LLMs) are increasingly used for educational support, yet their response quality varies depending on the language of interaction. This paper presents an automated multilingual pipeline for generating, solving, and evaluating math problems aligned with the German K-10 curriculum. We generated 628 math exercises and translated them into English, German, and Arabic. Three commercial LLMs (GPT-4o-mini, Gemini 2.5 Flash, and Qwen-plus) were prompted to produce step-by-step solu…

Hungary bans entry of Ukrainian commander over Druzhba pipeline attack: https://benborges.xyz/2025/08/28/hungary-bans-entry-of-ukrainian.html

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

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

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