Tootfinder

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

SiPipe: Bridging the CPU-GPU Utilization Gap for Efficient Pipeline-Parallel LLM Inference
Yongchao He, Bohan Zhao, Zheng Cao
https://arxiv.org/abs/2506.22033

SiPipe: Bridging the CPU-GPU Utilization Gap for Efficient Pipeline-Parallel LLM Inference
As inference workloads for large language models (LLMs) scale to meet growing user demand, pipeline parallelism (PP) has become a widely adopted strategy for multi-GPU deployment, particularly in cross-node setups, to improve key-value (KV) cache capacity and inference throughput. However, PP suffers from inherent inefficiencies caused by three types of execution bubbles-load-imbalance, intra-stage, and inter-stage-which limit pipeline saturation. We present SiPipe, a heterogeneous pipeline des…

Introducing HALC: A general pipeline for finding optimal prompting strategies for automated coding with LLMs in the computational social sciences
Andreas Reich, Claudia Thoms, Tobias Schrimpf
https://arxiv.org/abs/2507.21831

Introducing HALC: A general pipeline for finding optimal prompting strategies for automated coding with LLMs in the computational social sciences
LLMs are seeing widespread use for task automation, including automated coding in the social sciences. However, even though researchers have proposed different prompting strategies, their effectiveness varies across LLMs and tasks. Often trial and error practices are still widespread. We propose HALC$-$a general pipeline that allows for the systematic and reliable construction of optimal prompts for any given coding task and model, permitting the integration of any prompting strategy deemed rel…

RATE: An LLM-Powered Retrieval Augmented Generation Technology-Extraction Pipeline
Karan Mirhosseini, Arya Aftab, Alireza Sheikh
https://arxiv.org/abs/2507.21125 https://…

RATE: An LLM-Powered Retrieval Augmented Generation Technology-Extraction Pipeline
In an era of radical technology transformations, technology maps play a crucial role in enhancing decision making. These maps heavily rely on automated methods of technology extraction. This paper introduces Retrieval Augmented Technology Extraction (RATE), a Large Language Model (LLM) based pipeline for automated technology extraction from scientific literature. RATE combines Retrieval Augmented Generation (RAG) with multi-definition LLM-based validation. This hybrid method results in high rec…

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

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.

Snap, Segment, Deploy: A Visual Data and Detection Pipeline for Wearable Industrial Assistants
Di Wen, Junwei Zheng, Ruiping Liu, Yi Xu, Kunyu Peng, Rainer Stiefelhagen
https://arxiv.org/abs/2507.21072

Snap, Segment, Deploy: A Visual Data and Detection Pipeline for Wearable Industrial Assistants
Industrial assembly tasks increasingly demand rapid adaptation to complex procedures and varied components, yet are often conducted in environments with limited computing, connectivity, and strict privacy requirements. These constraints make conventional cloud-based or fully autonomous solutions impractical for factory deployment. This paper introduces a mobile-device-based assistant system for industrial training and operational support, enabling real-time, semi-hands-free interaction through …

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

A pipeline to search for signatures of line-of-sight acceleration in gravitational wave signals produced by compact binary coalescences
Avinash Tiwari, Aditya Vijaykumar, Shasvath J. Kapadia, Shrobana Ghosh, Alex B. Nielsen
https://arxiv.org/abs/2506.22272

A pipeline to search for signatures of line-of-sight acceleration in gravitational wave signals produced by compact binary coalescences
Compact binary coalescences (CBCs), such as merging binary black holes (BBHs), binary neutron stars (BNSs), or neutron star black holes (NSBHs), hosted by dense stellar environments, could produce gravitational waves (GWs) that contain signatures of line-of-sight acceleration (LOSA) imparted by the environment's gravitational potential. We calculate the Post-Newtonian (PN) corrections to the $(2,\,2)$ mode GW phase due to a finite LOSA, starting from the leading order at -4 PN below the quadrup…

"But good DX doesn’t guarantee good UX. In fact, it’s often the opposite. Because the more comfortable we make things for developers, the more abstraction we add. And every abstraction creates distance between the thing being built and the people it’s for."
https://www.jon…

JavaScript broke the web (and called it progress)
We replaced simple websites with complex apps nobody asked for. Now it takes a complex build pipeline just to change a headline.

One-Loop Galaxy Bispectrum: Consistent Theory, Efficient Analysis with COBRA, and Implications for Cosmological Parameters
Thomas Bakx, Mikhail M. Ivanov, Oliver H. E. Philcox, Zvonimir Vlah
https://arxiv.org/abs/2507.22110

One-Loop Galaxy Bispectrum: Consistent Theory, Efficient Analysis with COBRA, and Implications for Cosmological Parameters
We present an efficient and accurate pipeline for the analysis of the redshift-space galaxy bispectrum multipoles at one-loop order in effective field theory (EFT). We provide a systematic theory derivation based on power counting, which features the first comprehensive treatment of stochastic EFT contributions -- these are found to significantly improve the match to data. Our computational pipeline utilizes the COBRA technique that expands the linear matter power spectrum over a basis of princ…

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

From Prompt to Pipeline: Large Language Models for Scientific Workflow Development in Bioinformatics
Khairul Alam, Banani Roy
https://arxiv.org/abs/2507.20122 https://

From Prompt to Pipeline: Large Language Models for Scientific Workflow Development in Bioinformatics
The increasing complexity of bioinformatics data analysis has made Scientific Workflow Systems (SWSs) like Galaxy and Nextflow essential for enabling scalable, reproducible, and automated workflows. However, creating and understanding these workflows remains challenging, particularly for domain experts without programming expertise. This study investigates whether modern Large Language Models (LLMs), GPT-4o, Gemini 2.5 Flash, and DeepSeek-V3, can support the generation of accurate, complete, an…

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 …

DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments
Yufei Jia, Guangyu Wang, Yuhang Dong, Junzhe Wu, Yupei Zeng, Haonan Lin, Zifan Wang, Haizhou Ge, Weibin Gu, Kairui Ding, Zike Yan, Yunjie Cheng, Yue Li, Ziming Wang, Chuxuan Li, Wei Sui, Lu Shi, Guanzhong Tian, Ruqi Huang, Guyue Zhou
https://arxiv.org/abs/2507.219…

DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments
We present the first unified, modular, open-source 3DGS-based simulation framework for Real2Sim2Real robot learning. It features a holistic Real2Sim pipeline that synthesizes hyper-realistic geometry and appearance of complex real-world scenarios, paving the way for analyzing and bridging the Sim2Real gap. Powered by Gaussian Splatting and MuJoCo, Discoverse enables massively parallel simulation of multiple sensor modalities and accurate physics, with inclusive supports for existing 3D assets, …

SleepWalk: Exploiting Context Switching and Residual Power for Physical Side-Channel Attacks
Sahan Sanjaya, Aruna Jayasena, Prabhat Mishra
https://arxiv.org/abs/2507.22306 https…

SleepWalk: Exploiting Context Switching and Residual Power for Physical Side-Channel Attacks
Context switching is utilized by operating systems to change the execution context between application programs. It involves saving and restoring the states of multiple registers and performing a pipeline flush to remove any pre-fetched instructions, leading to a higher instantaneous power consumption compared to typical program execution. In this paper, we introduce a physical power side-channel leakage source that exploits the power spike observed during a context switch, triggered by the inb…

MPipeMoE: Memory Efficient MoE for Pre-trained Models with Adaptive Pipeline Parallelism
Zheng Zhang, Donglin Yang, Yaqi Xia, Liang Ding, Dacheng Tao, Xiaobo Zhou, Dazhao Cheng
https://arxiv.org/abs/2506.22175

MPipeMoE: Memory Efficient MoE for Pre-trained Models with Adaptive Pipeline Parallelism
Recently, Mixture-of-Experts (MoE) has become one of the most popular techniques to scale pre-trained models to extraordinarily large sizes. Dynamic activation of experts allows for conditional computation, increasing the number of parameters of neural networks, which is critical for absorbing the vast amounts of knowledge available in many deep learning areas. However, despite the existing system and algorithm optimizations, there are significant challenges to be tackled when it comes to the i…

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

Intent-Aware Neural Query Reformulation for Behavior-Aligned Product Search
Jayanth Yetukuri, Ishita Khan
https://arxiv.org/abs/2507.22213 https://arxiv.or…

Intent-Aware Neural Query Reformulation for Behavior-Aligned Product Search
Understanding and modeling buyer intent is a foundational challenge in optimizing search query reformulation within the dynamic landscape of e-commerce search systems. This work introduces a robust data pipeline designed to mine and analyze large-scale buyer query logs, with a focus on extracting fine-grained intent signals from both explicit interactions and implicit behavioral cues. Leveraging advanced sequence mining techniques and supervised learning models, the pipeline systematically capt…

Speech Emotion Recognition via Entropy-Aware Score Selection
ChenYi Chua, JunKai Wong, Chengxin Chen, Xiaoxiao Miao
https://arxiv.org/abs/2508.20796 https://

Speech Emotion Recognition via Entropy-Aware Score Selection
In this paper, we propose a multimodal framework for speech emotion recognition that leverages entropy-aware score selection to combine speech and textual predictions. The proposed method integrates a primary pipeline that consists of an acoustic model based on wav2vec2.0 and a secondary pipeline that consists of a sentiment analysis model using RoBERTa-XLM, with transcriptions generated via Whisper-large-v3. We propose a late score fusion approach based on entropy and varentropy thresholds to …

Enabling Early Transient Discovery in LSST via Difference Imaging with DECam
Yize Dong, Kaylee de Soto, V. Ashley Villar, Anya Nugent, Alex Gagliano, K. Azalee Bostroem
https://arxiv.org/abs/2507.22156

Enabling Early Transient Discovery in LSST via Difference Imaging with DECam
We present SLIDE, a pipeline that enables transient discovery in data from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), using archival images from the Dark Energy Camera (DECam) as templates for difference imaging. We apply this pipeline to the recently released Data Preview 1 (DP1; the first public release of Rubin commissioning data) and search for transients in the resulting difference images. The image subtraction, photometry extraction, and transient detection ar…

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 […]

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…

Hungarian opposition politician speaks up in support for Ukrainian commander banned by Budapest over oil pipeline attack: https://benborges.xyz/2025/08/29/hungarian-opposition-politician-speaks-up.html

From ⁨⁨⁨⁨⁨#AnnafromUkraine⁩⁩⁩⁩⁩ @AnnafromUkraine@youtube.com
RYAZAN-MOSCOW OIL PIPELINE HUGE EXPLOSION & #VORONEZH #RADARS DESTROYED Vlog 1149: War in

A surrogate model for topology optimisation of elastic structures via parametric autoencoders
Matteo Giacomini, Antonio Huerta
https://arxiv.org/abs/2507.22539 https://

A surrogate model for topology optimisation of elastic structures via parametric autoencoders
A surrogate-based topology optimisation algorithm for linear elastic structures under parametric loads and boundary conditions is proposed. Instead of learning the parametric solution of the state (and adjoint) problems or the optimisation trajectory as a function of the iterations, the proposed approach devises a surrogate version of the entire optimisation pipeline. First, the method predicts a quasi-optimal topology for a given problem configuration as a surrogate model of high-fidelity topo…

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 …

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…

HOLISMOKES XVIII: Detecting strongly lensed SNe Ia from time series of multi-band LSST-like imaging data
Satadru Bag, Raoul Canameras, Sherry H. Suyu, Stefan Schuldt, Stefan Taubenberger, Irham Taufik Andika, Alejandra Melo
https://arxiv.org/abs/2506.22076

HOLISMOKES XVIII: Detecting strongly lensed SNe Ia from time series of multi-band LSST-like imaging data
Strong gravitationally lensed supernovae (LSNe), though rare, are exceptionally valuable probes for cosmology and astrophysics. Upcoming time-domain surveys like the Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) offer a major opportunity to discover them in large numbers. Early identification is crucial for timely follow-up observations. We develop a deep learning pipeline to detect LSNe using multi-band, multi-epoch image cutouts. Our model is based on a 2D convolutional long…

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

Differentiating a HEP Analysis Pipeline within the Scikit-HEP Software Ecosystem
Mohamed Aly, Lino Gerlach
https://arxiv.org/abs/2508.17802 https://arxiv.o…

Differentiating a HEP Analysis Pipeline within the Scikit-HEP Software Ecosystem
A first differentiable analysis pipeline is presented for an example high-energy physics (HEP) use case with publicly available collision data from the Compact Muon Solenoid detector at the Large Hadron Collider. The pipeline combines tools from the Scikit-HEP ecosystem with \texttt{JAX}. The study is based on an existing search for a hypothetical particle, the $Z^{\prime}$ boson, and uses a realistic, yet simplified, statistical model. The gradient-based optimization techniques employed in thi…

Refining Czech GEC: Insights from a Multi-Experiment Approach
Petr Pechman, Milan Straka, Jana Strakov\'a, Jakub N\'aplava
https://arxiv.org/abs/2506.22402

Refining Czech GEC: Insights from a Multi-Experiment Approach
We present a grammar error correction (GEC) system that achieves state of the art for the Czech language. Our system is based on a neural network translation approach with the Transformer architecture, and its key feature is its real-time synthetic generation pipeline, which dynamically augments sentences with artificial errors by introducing both language-agnostic and Czech-specific errors. We conduct a comprehensive series of experiments, investigating the Czech GEC corpora as bases for synth…

UiS-IAI@LiveRAG: Retrieval-Augmented Information Nugget-Based Generation of Responses
Weronika {\L}ajewska, Ivica Kostric, Gabriel Iturra-Bocaz, Mariam Arustashvili, Krisztian Balog
https://arxiv.org/abs/2506.22210

UiS-IAI@LiveRAG: Retrieval-Augmented Information Nugget-Based Generation of Responses
Retrieval-augmented generation (RAG) faces challenges related to factual correctness, source attribution, and response completeness. The LiveRAG Challenge hosted at SIGIR'25 aims to advance RAG research using a fixed corpus and a shared, open-source LLM. We propose a modular pipeline that operates on information nuggets-minimal, atomic units of relevant information extracted from retrieved documents. This multistage pipeline encompasses query rewriting, passage retrieval and reranking, nugget d…

🇺🇦 Auf radioeins läuft...
Vivien Goldman:
🎵 Launderette
#NowPlaying #VivienGoldman
https://kutmah.bandcamp.com/track/vivien-goldman-launderette-solid-space-destination-moon-die-letzten-ecken-ich-depeche-mode-pipeline-delia-derbyshire-ziwzih-ziwzih-bruce-haack-word-game-dirty-beaches-death-valley-the-residents-f
https://open.spotify.com/track/4g8X2H8Ve4s3aEGU9drjgh

Multi-robot LiDAR SLAM: a practical case study in underground tunnel environments
Federica Di Lauro, Domenico G. Sorrenti, Miguel Angel Sotelo
https://arxiv.org/abs/2507.21553 h…

Multi-robot LiDAR SLAM: a practical case study in underground tunnel environments
Multi-robot SLAM aims at localizing and building a map with multiple robots, interacting with each other. In the work described in this article, we analyze the pipeline of a decentralized LiDAR SLAM system to study the current limitations of the state of the art, and we discover a significant source of failures, i.e., that the loop detection is the source of too many false positives. We therefore develop and propose a new heuristic to overcome these limitations. The environment taken as referen…

On the Feasibility of Poisoning Text-to-Image AI Models via Adversarial Mislabeling
Stanley Wu, Ronik Bhaskar, Anna Yoo Jeong Ha, Shawn Shan, Haitao Zheng, Ben Y. Zhao
https://arxiv.org/abs/2506.21874

On the Feasibility of Poisoning Text-to-Image AI Models via Adversarial Mislabeling
Today's text-to-image generative models are trained on millions of images sourced from the Internet, each paired with a detailed caption produced by Vision-Language Models (VLMs). This part of the training pipeline is critical for supplying the models with large volumes of high-quality image-caption pairs during training. However, recent work suggests that VLMs are vulnerable to stealthy adversarial attacks, where adversarial perturbations are added to images to mislead the VLMs into producing …

Quantum Verifiable Rewards for Post-Training Qiskit Code Assistant
Nicolas Dupuis, Adarsh Tiwari, Youssef Mroueh, David Kremer, Ismael Faro, Juan Cruz-Benito
https://arxiv.org/abs/2508.20907

Quantum Verifiable Rewards for Post-Training Qiskit Code Assistant
Qiskit is an open-source quantum computing framework that allows users to design, simulate, and run quantum circuits on real quantum hardware. We explore post-training techniques for LLMs to assist in writing Qiskit code. We introduce quantum verification as an effective method for ensuring code quality and executability on quantum hardware. To support this, we developed a synthetic data pipeline that generates quantum problem-unit test pairs and used it to create preference data for aligning L…

FD-Bench: A Full-Duplex Benchmarking Pipeline Designed for Full Duplex Spoken Dialogue Systems
Yizhou Peng, Yi-Wen Chao, Dianwen Ng, Yukun Ma, Chongjia Ni, Bin Ma, Eng Siong Chng
https://arxiv.org/abs/2507.19040

FD-Bench: A Full-Duplex Benchmarking Pipeline Designed for Full Duplex Spoken Dialogue Systems
Full-duplex spoken dialogue systems (FDSDS) enable more natural human-machine interactions by allowing real-time user interruptions and backchanneling, compared to traditional SDS that rely on turn-taking. However, existing benchmarks lack metrics for FD scenes, e.g., evaluating model performance during user interruptions. In this paper, we present a comprehensive FD benchmarking pipeline utilizing LLMs, TTS, and ASR to address this gap. It assesses FDSDS's ability to handle user interruptions,…

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.

ViStruct: Simulating Expert-Like Reasoning Through Task Decomposition and Visual Attention Cues
Oliver Huang, Carolina Nobre
https://arxiv.org/abs/2506.21762

ViStruct: Simulating Expert-Like Reasoning Through Task Decomposition and Visual Attention Cues
Data visualization tasks often require multi-step reasoning, and the interpretive strategies experts use, such as decomposing complex goals into smaller subtasks and selectively attending to key chart regions are rarely made explicit. ViStruct is an automated pipeline that simulates these expert behaviours by breaking high-level visual questions into structured analytic steps and highlighting semantically relevant chart areas. Leveraging large language and vision-language models, ViStruct ident…

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

I have no idea what past me wanted me to fix....

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.

Tansu Çiller, and the corruption of her neoliberal government, was one of the primary drivers of the neoliberalism-to-fascism pipeline* in Turkey that exacerbated inequality, alienated and marginalised huge swaths of the population, and eventually led to Erdoğan’s fascist/Islamist takeover.
Carve her name, indeed; right next to Margaret Thatcher’s.
* Sound familiar, Americans?
#tansuCiller

CarveHerName (@CarveHerName@mstdn.social)
Attached: 1 image #OnThisDay, 25 June 1993, Tansu Çiller wins a vote of confidence to become prime minister of Turkey. She was the first woman to hold the post. #WomenInHistory #OTD #History #WomensHistory

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

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

An OpenSource CI/CD Pipeline for Variant-Rich Software-Defined Vehicles
Matthias Wei{\ss}, Anish Navalgund, Johannes St\"umpfle, Falk Dettinger, Michael Weyrich
https://arxiv.org/abs/2507.19446

An OpenSource CI/CD Pipeline for Variant-Rich Software-Defined Vehicles
Software-defined vehicles (SDVs) offer a wide range of connected functionalities, including enhanced driving behavior and fleet management. These features are continuously updated via over-the-air (OTA) mechanisms, resulting in a growing number of software versions and variants due to the diversity of vehicles, cloud/edge environments, and stakeholders involved. The lack of a unified integration environment further complicates development, as connected mobility solutions are often built in isol…

Efficient Lines Detection for Robot Soccer
Jo\~ao G. Melo, Jo\~ao P. Mafaldo, Edna Barros
https://arxiv.org/abs/2507.19469 https://arxiv.org/pdf/2507.19469…

Efficient Lines Detection for Robot Soccer
Self-localization is essential in robot soccer, where accurate detection of visual field features, such as lines and boundaries, is critical for reliable pose estimation. This paper presents a lightweight and efficient method for detecting soccer field lines using the ELSED algorithm, extended with a classification step that analyzes RGB color transitions to identify lines belonging to the field. We introduce a pipeline based on Particle Swarm Optimization (PSO) for threshold calibration to opt…

Requirements on bandpass resolution and measurement precision for LiteBIRD
S. Giardiello, A. Carones, T. Ghigna, L. Pagano, F. Piacentini, L. Montier, R. Takaku, E. Calabrese, D. Adak, E. Allys, A. Anand, J. Aumont, M. Ballardini, A. J. Banday, R. B. Barreiro, N. Bartolo, S. Basak, M. Bersanelli, A. Besnard, M. Bortolami, T. Brinckmann, F. J. Casas, K. Cheung, M. Citran, L. Clermont, F. Columbro, A. Coppolecchia, F. Cuttaia, P. de Bernardis, E. de la Hoz, M. De Lucia, S. Della Torre, E…

Requirements on bandpass resolution and measurement precision for LiteBIRD
In this work, we study the impact of an imperfect knowledge of the instrument bandpasses on the estimate of the tensor-to-scalar ratio $r$ in the context of the next-generation LiteBIRD satellite. We develop a pipeline to include bandpass integration in both the time-ordered data (TOD) and the map-making processing steps. We introduce the systematic effect by having a mismatch between the ``real'', high resolution bandpass $τ$, entering the TOD, and the estimated one $τ_s$, used in the map-ma…

The Ongoing Decline in Activity of Comet 103P/Hartley 2
Ariel Graykowski, Guillaume Langin, David Chiron, Bruno Guillet, Franck Marchis, Nicolas Biver, G\'erard Arlic, Bernard Baudouin, Etienne Bertrand, Randall Blake, Cyrille Bosquet, John K. Bradley, Isabelle Brocard, Christophe Cac, Alain Cagna, Nicolas Castel, Eric Chariot, Olivier Clerget, Tom Coarrase, Lucas Cogniaux, Julien Collot, Christophe Cot\'e, Michel Deconinck, Jean-Paul Desgrees, Josselin Desmars, Giuseppe Di Tom…

The Ongoing Decline in Activity of Comet 103P/Hartley 2
We report photometric observations of Comet 103P/Hartley 2 during its 2023 apparition. Our campaign, conducted from August through December 2023, combined data from a global network of citizen astronomers coordinated by Unistellar and the Association Française d'Astronomie. Photometry was derived using an automated pipeline for eVscope observations in partnership with the SETI Institute and aperture photometry via AstroLab Stellar. We find that the comet's peak reduced brightness, measured at …

A MILP-Based Solution to Multi-Agent Motion Planning and Collision Avoidance in Constrained Environments
Akshay Jaitly, Jack Cline, Siavash Farzan
https://arxiv.org/abs/2506.21982

A MILP-Based Solution to Multi-Agent Motion Planning and Collision Avoidance in Constrained Environments
We propose a mixed-integer linear program (MILP) for multi-agent motion planning that embeds Polytopic Action-based Motion Planning (PAAMP) into a sequence-then-solve pipeline. Region sequences confine each agent to adjacent convex polytopes, while a big-M hyperplane model enforces inter-agent separation. Collision constraints are applied only to agents sharing or neighboring a region, which reduces binary variables exponentially compared with naive formulations. An L1 path-length-plus-accelera…

PPipe: Efficient Video Analytics Serving on Heterogeneous GPU Clusters via Pool-Based Pipeline Parallelism
Z. Jonny Kong, Qiang Xu, Y. Charlie Hu
https://arxiv.org/abs/2507.18748

PPipe: Efficient Video Analytics Serving on Heterogeneous GPU Clusters via Pool-Based Pipeline Parallelism
With the rapid innovation of GPUs, heterogeneous GPU clusters in both public clouds and on-premise data centers have become increasingly commonplace. In this paper, we demonstrate how pipeline parallelism, a technique wellstudied for throughput-oriented deep learning model training, can be used effectively for serving latency-bound model inference, e.g., in video analytics systems, on heterogeneous GPU clusters. Our work exploits the synergy between diversity in model layers and diversity in GP…

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

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Heuristics for AI-driven Graphical Asset Generation Tools in Game Design and Development Pipeline...
Kaisei Fukaya, Damon Daylamani-Zad, Harry Agius

Automate *all* the things!
Just set up a build pipeline on mit gitlab to automatically compile a #godot project
I love #gitlab

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…

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments
Lukas P\'eron, Paolo Calafiura, Xiangyang Ju, Jay Chan
https://arxiv.org/abs/2508.16518

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments
We have developed an Uncertainty Quantification process for multistep pipelines and applied it to the ACORN particle tracking pipeline. All our experiments are made using the TrackML open dataset. Using the Monte Carlo Dropout method, we measure the data and model uncertainties of the pipeline steps, study how they propagate down the pipeline, and how they are impacted by the training dataset's size, the input data's geometry and physical properties. We will show that for our case study, as the…

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

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…

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

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.

Building Telemetry Pipelines with the #OpenTelemetry #Collector
https://www.dash0.com/guides/opentelemetry

Field-level constraints on cosmic birefringence from hybrid ILC maps combining $E$- and $B$-mode channels
Mathieu Remazeilles
https://arxiv.org/abs/2507.22109 https://

Field-level constraints on cosmic birefringence from hybrid ILC maps combining $E$- and $B$-mode channels
Cosmic birefringence, arising from a potential parity-violating interaction between cosmic microwave background (CMB) photons and evolving pseudo-scalar fields such as axion-like particles, can rotate the CMB polarization plane and induce an effective correlation between the CMB $E$- and $B$-mode polarization. In this work, we introduce a hybrid internal linear combination (ILC) method that combines both $E$- and $B$-mode frequency maps into the component separation pipeline, enabling the disen…

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

Mind the Language Gap in Digital Humanities: LLM-Aided Translation of SKOS Thesauri
Felix Kraus, Nicolas Blumenr\"ohr, Danah Tonne, Achim Streit
https://arxiv.org/abs/2507.19537

Mind the Language Gap in Digital Humanities: LLM-Aided Translation of SKOS Thesauri
We introduce WOKIE, an open-source, modular, and ready-to-use pipeline for the automated translation of SKOS thesauri. This work addresses a critical need in the Digital Humanities (DH), where language diversity can limit access, reuse, and semantic interoperability of knowledge resources. WOKIE combines external translation services with targeted refinement using Large Language Models (LLMs), balancing translation quality, scalability, and cost. Designed to run on everyday hardware and be easi…

OLMoASR: Open Models and Data for Training Robust Speech Recognition Models
Huong Ngo, Matt Deitke, Martijn Bartelds, Sarah Pratt, Josh Gardner, Matt Jordan, Ludwig Schmidt
https://arxiv.org/abs/2508.20869

OLMoASR: Open Models and Data for Training Robust Speech Recognition Models
Improvements in training data scale and quality have led to significant advances, yet its influence in speech recognition remains underexplored. In this paper, we present a large-scale dataset, OLMoASR-Pool, and series of models, OLMoASR, to study and develop robust zero-shot speech recognition models. Beginning from OLMoASR-Pool, a collection of 3M hours of English audio and 17M transcripts, we design text heuristic filters to remove low-quality or mistranscribed data. Our curation pipeline pr…

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, 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.

SimShear: Sim-to-Real Shear-based Tactile Servoing
Kipp McAdam Freud, Yijiong Lin, Nathan F. Lepora
https://arxiv.org/abs/2508.20561 https://arxiv.org/pdf/…

SimShear: Sim-to-Real Shear-based Tactile Servoing
We present SimShear, a sim-to-real pipeline for tactile control that enables the use of shear information without explicitly modeling shear dynamics in simulation. Shear, arising from lateral movements across contact surfaces, is critical for tasks involving dynamic object interactions but remains challenging to simulate. To address this, we introduce shPix2pix, a shear-conditioned U-Net GAN that transforms simulated tactile images absent of shear, together with a vector encoding shear informat…

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…

MSRS: Evaluating Multi-Source Retrieval-Augmented Generation
Rohan Phanse, Yijie Zhou, Kejian Shi, Wencai Zhang, Yixin Liu, Yilun Zhao, Arman Cohan
https://arxiv.org/abs/2508.20867

MSRS: Evaluating Multi-Source Retrieval-Augmented Generation
Retrieval-augmented systems are typically evaluated in settings where information required to answer the query can be found within a single source or the answer is short-form or factoid-based. However, many real-world applications demand the ability to integrate and summarize information scattered across multiple sources, where no single source is sufficient to respond to the user's question. In such settings, the retrieval component of a RAG pipeline must recognize a variety of relevance signa…

Unsupervised Classification of Gamma-ray Bursts from Blazars (GRBBLs) with Machine Learning
Matteo Cerruti
https://arxiv.org/abs/2508.20927 https://arxiv.o…

Unsupervised Classification of Gamma-ray Bursts from Blazars (GRBBLs) with Machine Learning
Blazars dominate the extragalactic $γ$-ray sky and show pronounced flares. Using public Fermi-LAT light curves for 732 blazars with secure redshifts, I implement an automated pipeline to identify and characterize $γ$-ray bursts from blazars (GRBBLs). Each event is modeled with an exponential rise/decay profile, and spectral variability is quantified via a constant fit. From 679 high-quality GRBBLs, I apply extreme deconvolution for unsupervised classification. The GRBBL population is remarkab…

Type-Compliant Adaptation Cascades: Adapting Programmatic LM Workflows to Data
Chu-Cheng Lin, Daiyi Peng, Yifeng Lu, Ming Zhang, Eugene Ie
https://arxiv.org/abs/2508.18244 https…

Type-Compliant Adaptation Cascades: Adapting Programmatic LM Workflows to Data
Reliably composing Large Language Models (LLMs) for complex, multi-step workflows remains a significant challenge. The dominant paradigm-optimizing discrete prompts in a pipeline-is notoriously brittle and struggles to enforce the formal compliance required for structured tasks. We introduce Type-Compliant Adaptation Cascades (TACs), a framework that recasts workflow adaptation as learning typed probabilistic programs. TACs treats the entire workflow, which is composed of parameter-efficiently …

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…
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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.

MegatronApp: Efficient and Comprehensive Management on Distributed LLM Training
Bohan Zhao, Guang Yang, Shuo Chen, Ruitao Liu, Tingrui Zhang, Yongchao He, Wei Xu
https://arxiv.org/abs/2507.19845

MegatronApp: Efficient and Comprehensive Management on Distributed LLM Training
The rapid escalation in the parameter count of large language models (LLMs) has transformed model training from a single-node endeavor into a highly intricate, cross-node activity. While frameworks such as Megatron-LM successfully integrate tensor (TP), pipeline (PP), and data (DP) parallelism to enable trillion-parameter training, they simultaneously expose practitioners to unprecedented systems-level challenges in performance optimization, diagnosis, and interpretability. MegatronApp is an op…

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…

Prompt-to-Product: Generative Assembly via Bimanual Manipulation
Ruixuan Liu, Philip Huang, Ava Pun, Kangle Deng, Shobhit Aggarwal, Kevin Tang, Michelle Liu, Deva Ramanan, Jun-Yan Zhu, Jiaoyang Li, Changliu Liu
https://arxiv.org/abs/2508.21063

Prompt-to-Product: Generative Assembly via Bimanual Manipulation
Creating assembly products demands significant manual effort and expert knowledge in 1) designing the assembly and 2) constructing the product. This paper introduces Prompt-to-Product, an automated pipeline that generates real-world assembly products from natural language prompts. Specifically, we leverage LEGO bricks as the assembly platform and automate the process of creating brick assembly structures. Given the user design requirements, Prompt-to-Product generates physically buildable brick…

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

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting
Alexandre Andre, Gauthier Roy, Eva Dyer, Kai Wang
https://arxiv.org/abs/2508.20401 https://…

Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting
Large Language Models (LLMs) are increasingly used for recommendation tasks due to their general-purpose capabilities. While LLMs perform well in rich-context settings, their behavior in cold-start scenarios, where only limited signals such as age, gender, or language are available, raises fairness concerns because they may rely on societal biases encoded during pretraining. We introduce a benchmark specifically designed to evaluate fairness in zero-context recommendation. Our modular pipeline …

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 …

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

SpeechIQ: Speech Intelligence Quotient Across Cognitive Levels in Voice Understanding Large Language Models
Zhen Wan, Chao-Han Huck Yang, Yahan Yu, Jinchuan Tian, Sheng Li, Ke Hu, Zhehuai Chen, Shinji Watanabe, Fei Cheng, Chenhui Chu, Sadao Kurohashi
https://arxiv.org/abs/2507.19361

SpeechIQ: Speech Intelligence Quotient Across Cognitive Levels in Voice Understanding Large Language Models
We introduce Speech-based Intelligence Quotient (SIQ) as a new form of human cognition-inspired evaluation pipeline for voice understanding large language models, LLM Voice, designed to assess their voice understanding ability. Moving beyond popular voice understanding metrics such as word error rate (WER), SIQ examines LLM Voice across three cognitive levels motivated by Bloom's Taxonomy: (1) Remembering (i.e., WER for verbatim accuracy); (2) Understanding (i.e., similarity of LLM's interpreta…

Attention is also needed for form design
B. Sankar, Dibakar Sen
https://arxiv.org/abs/2508.19708 https://arxiv.org/pdf/2508.19708

Attention is also needed for form design
Conventional product design is a cognitively demanding process, limited by its time-consuming nature, reliance on subjective expertise, and the opaque translation of inspiration into tangible concepts. This research introduces a novel, attention-aware framework that integrates two synergistic systems: EUPHORIA, an immersive Virtual Reality environment using eye-tracking to implicitly capture a designer's aesthetic preferences, and RETINA, an agentic AI pipeline that translates these implicit pr…

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

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…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/3]:
- Comparison of pipeline, sequence-to-sequence, and GPT models for end-to-end relation extraction: ...
Shashank Gupta, Xuguang Ai, Ramakanth Kavuluru

The infrared jet of M87 observed with JWST
Jan R\"oder, Maciek Wielgus, Joseph B. Jensen, Gagandeep S. Anand, R. Brent Tully
https://arxiv.org/abs/2507.18716 https://

The infrared jet of M87 observed with JWST
We present the first JWST+NIRCam images of the giant elliptical active galaxy M87 and its jet at 0.90, 1.50, 2.77 and 3.56 $μ$m. We analysed the large-scale jet structure, identifying prominent components, and determined the near-infrared spectral index. The data were calibrated using the standard JWST pipeline. We subtracted a constant background level and a smooth model of the galaxy surface brightness to isolate the jet. The total image fluxes measured in the NIRCam filters follow the infra…

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

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…

Towards a Holistic and Automated Evaluation Framework for Multi-Level Comprehension of LLMs in Book-Length Contexts
Jiaqi Deng, Yuho Lee, Nicole Hee-Yeon Kim, Hyangsuk Min, Taewon Yun, Minjeong Ban, Kim Yul, Hwanjun Song
https://arxiv.org/abs/2508.19578

Towards a Holistic and Automated Evaluation Framework for Multi-Level Comprehension of LLMs in Book-Length Contexts
We introduce HAMLET, a holistic and automated framework for evaluating the long-context comprehension of large language models (LLMs). HAMLET structures source texts into a three-level key-fact hierarchy at root-, branch-, and leaf-levels, and employs query-focused summarization to evaluate how well models recall and faithfully represent information at each level. To validate the reliability of our fully automated pipeline, we conduct a systematic human study, showing that our automatic evaluat…

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…

Enhancing Speech Emotion Recognition Leveraging Aligning Timestamps of ASR Transcripts and Speaker Diarization
Hsuan-Yu Wang, Pei-Ying Lee, Berlin Chen
https://arxiv.org/abs/2507.19356

Enhancing Speech Emotion Recognition Leveraging Aligning Timestamps of ASR Transcripts and Speaker Diarization
In this paper, we investigate the impact of incorporating timestamp-based alignment between Automatic Speech Recognition (ASR) transcripts and Speaker Diarization (SD) outputs on Speech Emotion Recognition (SER) accuracy. Misalignment between these two modalities often reduces the reliability of multimodal emotion recognition systems, particularly in conversational contexts. To address this issue, we introduce an alignment pipeline utilizing pre-trained ASR and speaker diarization models, syste…

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

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…

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

Bangla-Bayanno: A 52K-Pair Bengali Visual Question Answering Dataset with LLM-Assisted Translation Refinement
Mohammed Rakibul Hasan, Rafi Majid, Ahanaf Tahmid
https://arxiv.org/abs/2508.19887

Bangla-Bayanno: A 52K-Pair Bengali Visual Question Answering Dataset with LLM-Assisted Translation Refinement
In this paper, we introduce Bangla-Bayanno, an open-ended Visual Question Answering (VQA) Dataset in Bangla, a widely used, low-resource language in multimodal AI research. The majority of existing datasets are either manually annotated with an emphasis on a specific domain, query type, or answer type or are constrained by niche answer formats. In order to mitigate human-induced errors and guarantee lucidity, we implemented a multilingual LLM-assisted translation refinement pipeline. This datas…

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, (…

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