Tootfinder

Mazi Smith gets brutally honest on poor performance in Cowboys' preseason opener https://www.si.com/nfl/cowboys/news/mazi-smith-gets-brutally-honest-poor-performance-dallas-cowboys-preseason-opener

When a radical performance artist has command of an army (The Economist)
https://www.economist.com/leaders/2025/06/12/when-a-radical-performance-artist-has-command-of-an-army
http://www.memeorandum.com/250613/p150#a250613p150

AMD unveils its new MI350X and MI355X GPUs for AI workloads, claiming up to 4x AI compute performance and 35x inference gains over the prior-gen MI300X (Paul Alcorn/Tom's Hardware)
https://www.to…

AMD announces MI350X and MI355X AI GPUs, claims up to 4X generational performance gain, 35X faster inference
More power consumption equals lower TCO.

"Oblivion Remastered": Erster Patch für Performance-Probleme ist da
Über zwei Monate nach dem Release von "Oblivion Remastered" gibt es nun einen Patch, der die mangelhafte Performance verbessern soll.

Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning
Zezhen Xiang, Jingzhi Gong, Tao Chen
https://arxiv.org/abs/2507.08730 ht…

Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning
Modern configurable software systems need to learn models that correlate configuration and performance. However, when the system operates in dynamic environments, the workload variations, hardware changes, and system updates will inevitably introduce concept drifts at different levels - global drifts, which reshape the performance landscape of the entire configuration space; and local drifts, which only affect certain sub-regions of that space. As such, existing offline and transfer learning ap…

Transient performance of MPC for tracking without terminal constraints
Nadine Ehmann, Matthias K\"ohler, Frank Allg\"ower
https://arxiv.org/abs/2506.10589

Transient performance of MPC for tracking without terminal constraints
Model predictive control (MPC) for tracking is a recently introduced approach, which extends standard MPC formulations by incorporating an artificial reference as an additional optimization variable, in order to track external and potentially time-varying references. In this work, we analyze the performance of such an MPC for tracking scheme without a terminal cost and terminal constraints. We derive a transient performance estimate, i.e. a bound on the closed-loop performance over an arbitrary…

Performance of GPT-5 Frontier Models in Ophthalmology Question Answering
Fares Antaki, David Mikhail, Daniel Milad, Danny A Mammo, Sumit Sharma, Sunil K Srivastava, Bing Yu Chen, Samir Touma, Mertcan Sevgi, Jonathan El-Khoury, Pearse A Keane, Qingyu Chen, Yih Chung Tham, Renaud Duval
https://arxiv.org/abs/2508.09956

Performance of GPT-5 Frontier Models in Ophthalmology Question Answering
Large language models (LLMs) such as GPT-5 integrate advanced reasoning capabilities that may improve performance on complex medical question-answering tasks. For this latest generation of reasoning models, the configurations that maximize both accuracy and cost-efficiency have yet to be established. We evaluated 12 configurations of OpenAI's GPT-5 series (three model tiers across four reasoning effort settings) alongside o1-high, o3-high, and GPT-4o, using 260 closed-access multiple-choice que…

DS@GT at LongEval: Evaluating Temporal Performance in Web Search Systems and Topics with Two-Stage Retrieval
Anthony Miyaguchi, Imran Afrulbasha, Aleksandar Pramov
https://arxiv.org/abs/2507.08360

DS@GT at LongEval: Evaluating Temporal Performance in Web Search Systems and Topics with Two-Stage Retrieval
Information Retrieval (IR) models are often trained on static datasets, making them vulnerable to performance degradation as web content evolves. The DS@GT competition team participated in the Longitudinal Evaluation of Model Performance (LongEval) lab at CLEF 2025, which evaluates IR systems across temporally distributed web snapshots. Our analysis of the Qwant web dataset includes exploratory data analysis with topic modeling over time. The two-phase retrieval system employs sparse keyword se…

Revealing the Role of Audio Channels in ASR Performance Degradation
Kuan-Tang Huang, Li-Wei Chen, Hung-Shin Lee, Berlin Chen, Hsin-Min Wang
https://arxiv.org/abs/2508.08967 http…

Revealing the Role of Audio Channels in ASR Performance Degradation
Pre-trained automatic speech recognition (ASR) models have demonstrated strong performance on a variety of tasks. However, their performance can degrade substantially when the input audio comes from different recording channels. While previous studies have demonstrated this phenomenon, it is often attributed to the mismatch between training and testing corpora. This study argues that variations in speech characteristics caused by different recording channels can fundamentally harm ASR performan…

Enhancing Decoding Performance using Efficient Error Learning
Pavithran Iyer, Aditya Jain, Stephen D. Bartlett, Joseph Emerson
https://arxiv.org/abs/2507.08536

Enhancing Decoding Performance using Efficient Error Learning
Lowering the resource overhead needed to achieve fault-tolerant quantum computation is crucial to building scalable quantum computers. We show that adapting conventional maximum likelihood (ML) decoders to a small subset of efficiently learnable physical error characteristics can significantly improve the logical performance of a quantum error-correcting code. Specifically, we leverage error information obtained from efficient characterization methods based on Cycle Error Reconstruction (CER), …

Automating Multi-Tenancy Performance Evaluation on Edge Compute Nodes
Joanna Georgiou, Moysis Symeonides, George Pallis, Marios D. Dikaiakos
https://arxiv.org/abs/2506.10461

Automating Multi-Tenancy Performance Evaluation on Edge Compute Nodes
Edge Computing emerges as a promising alternative of Cloud Computing, with scalable compute resources and services deployed in the path between IoT devices and Cloud. Since virtualization techniques can be applied on Edge compute nodes, administrators can share their Edge infrastructures among multiple users, providing the so-called multi-tenancy. Even though multi-tenancy is unavoidable, it raises concerns about security and performance degradation due to resource contention in Edge Computing.…

Cybernetic Marionette: Channeling Collective Agency Through a Wearable Robot in a Live Dancer-Robot Duet
Anup Sathya, Jiasheng Li, Zeyu Yan, Adriane Fang, Bill Kules, Jonathan David Martin, Huaishu Peng
https://arxiv.org/abs/2506.10079

Cybernetic Marionette: Channeling Collective Agency Through a Wearable Robot in a Live Dancer-Robot Duet
We describe DANCE^2, an interactive dance performance in which audience members channel their collective agency into a dancer-robot duet by voting on the behavior of a wearable robot affixed to the dancer's body. At key moments during the performance, the audience is invited to either continue the choreography or override it, shaping the unfolding interaction through real-time collective input. While post-performance surveys revealed that participants felt their choices meaningfully influenced …

Evaluating the Performance of Reconfigurable Intelligent Base Stations through Ray Tracing
Sina Beyraghi, Giovanni Interdonato, Giovanni Geraci, Stefano Buzzi, Angel Lozano
https://arxiv.org/abs/2507.08611

Evaluating the Performance of Reconfigurable Intelligent Base Stations through Ray Tracing
Massive multiple-input multiple-output (mMIMO) is a key capacity-boosting technology in 5G wireless systems. To reduce the number of radio frequency (RF) chains needed in such systems, a novel approach has recently been introduced involving an antenna array supported by a reconfigurable intelligent surface. This arrangement, known as a reconfigurable intelligent base station (RIBS), offers performance comparable to that of a traditional mMIMO array, but with significantly fewer RF chains. Given…

Preprocessing Algorithm Leveraging Geometric Modeling for Scale Correction in Hyperspectral Images for Improved Unmixing Performance
Praveen Sumanasekara, Athulya Ratnayake, Buddhi Wijenayake, Keshawa Ratnayake, Roshan Godaliyadda, Parakrama Ekanayake, Vijitha Herath
https://arxiv.org/abs/2508.08431

Preprocessing Algorithm Leveraging Geometric Modeling for Scale Correction in Hyperspectral Images for Improved Unmixing Performance
Spectral variability significantly impacts the accuracy and convergence of hyperspectral unmixing algorithms. While many methods address complex spectral variability, large-scale variations in spectral signature scale caused by factors such as topography, illumination, and shadowing remain a major challenge. These variations often degrade unmixing performance and complicate model fitting. In this paper, we propose a novel preprocessing algorithm that corrects scale-induced spectral variability …

Evaluating Task Execution Performance Under Energy Measurement Overhead
Mateen Ashraf, Shahab Jahanbazi, Onel L. A. L\'opez
https://arxiv.org/abs/2508.08757 https://

Evaluating Task Execution Performance Under Energy Measurement Overhead
Energy-awareness for adapting task execution behavior can bring several benefits in terms of performance improvement in energy harvesting (EH) Internet of Things (IoT) devices. However, the energy measurement cost of acquiring energy information, which is traditionally ignored, can potentially neutralize or even reverse the potential benefits. This paper highlights operational parameters, such as energy measurement frequency and task execution frequency, which can be tuned to improve the task e…

That WWDC talk on using performance with Instruments is just the excuse I needed.
It is incredible.

Multi-Level Service Performance Forecasting via Spatiotemporal Graph Neural Networks
Zhihao Xue, Yun Zi, Nia Qi, Ming Gong, Yujun Zou
https://arxiv.org/abs/2508.07122 https://…

Multi-Level Service Performance Forecasting via Spatiotemporal Graph Neural Networks
This paper proposes a spatiotemporal graph neural network-based performance prediction algorithm to address the challenge of forecasting performance fluctuations in distributed backend systems with multi-level service call structures. The method abstracts system states at different time slices into a sequence of graph structures. It integrates the runtime features of service nodes with the invocation relationships among services to construct a unified spatiotemporal modeling framework. The mode…

Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method
Masoumeh Sharafi, Soufiane Belharbi, Houssem Ben Salem, Ali Etemad, Alessandro Lameiras Koerich, Marco Pedersoli, Simon Bacon, Eric Granger
https://arxiv.org/abs/2508.09202

Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method
Facial expression recognition (FER) models are employed in many video-based affective computing applications, such as human-computer interaction and healthcare monitoring. However, deep FER models often struggle with subtle expressions and high inter-subject variability, limiting their performance in real-world applications. To improve their performance, source-free domain adaptation (SFDA) methods have been proposed to personalize a pretrained source model using only unlabeled target domain da…

lmao @ this aliexpress vendor complaining about supply chains
we've all been there mate

Performance Benchmarking of Machine Learning Models for Terahertz Metamaterial Absorber Prediction
Nafisa Anjum, Robiul Hasan
https://arxiv.org/abs/2508.08611 https://

Performance Benchmarking of Machine Learning Models for Terahertz Metamaterial Absorber Prediction
This study presents a polarization-insensitive ultra-broadband terahertz metamaterial absorber based on vanadium dioxide (VO2) and evaluates machine learning methods for predicting its absorption performance. The structure consists of a VO2 metasurface, a MF2 dielectric spacer, and a gold ground plane. It achieves more than 90% absorption between 5.72 and 11.11 THz, covering a 5.38 THz bandwidth with an average absorptance of 98.15%. A dataset of 9,018 samples was generated from full-wave simul…

baseball: Baseball steroid use (2008)
Two networks representing steroid use among baseball players. First, a bipartite network of players and their steroid providers (of illegal performance-enhancing substances). Second, a one-mode projection of players, which are linked if they have a common supplier.
This network has 72 nodes and 1089 edges.
Tags: Social, Offline, Weighted, Projection

Can We Trust AI to Govern AI? Benchmarking LLM Performance on Privacy and AI Governance Exams
Zane Witherspoon, Thet Mon Aye, YingYing Hao
https://arxiv.org/abs/2508.09036 https…

Can We Trust AI to Govern AI? Benchmarking LLM Performance on Privacy and AI Governance Exams
The rapid emergence of large language models (LLMs) has raised urgent questions across the modern workforce about this new technology's strengths, weaknesses, and capabilities. For privacy professionals, the question is whether these AI systems can provide reliable support on regulatory compliance, privacy program management, and AI governance. In this study, we evaluate ten leading open and closed LLMs, including models from OpenAI, Anthropic, Google DeepMind, Meta, and DeepSeek, by benchmarki…

Just before the Kennedy Center's opening night performance of the musical "Les Miserables,"
Donald and Melania Trump stood to be recognized.
They were met with a loud chorus of boos.
Video of the moment shows the boos mixed with some cheering,
Some Trump supporters in the audience shouted "USA" chants in an attempt to drown out the booing.
In addition to the First Couple, both JD Vance and Usha Vance were also in attendance

'Embarrassing': Trump 'booed mercilessly' by Kennedy Center at 'Les Miserables' opening
Just before the Kennedy Center's opening night performance of the musical "Les Miserables," President Donald Trump and First Lady Melania Trump stood to be recognized. They were met with a loud chorus of boos.Video of the moment shows the boos mixed with some cheering, with Reuters White House corre...

After turning on both users and advertisers, tech companies only had one more place to go: employees. Individual productivity (AI-powered or otherwise) will not save you, because the issue is not performance.
In return, employees rightly stopped caring.
But unless you care, you can't do good design.
https://

Fear vs Play
Fear is choking out thoughtfulness in tech, and it can't be replaced with "more productivity" — because productivity is not what motivates corporate layoffs.

from my link log —
Performance of memory reclamation for lockless synchronization.
http://csng.cs.toronto.edu/publication_files/0000/0159/jpdc07.pdf
saved 2022-12-26

JSPIM: A Skew-Aware PIM Accelerator for High-Performance Databases Join and Select Operations
Sabiha Tajdari, Anastasia Ailamaki, Sandhya Dwarkadas
https://arxiv.org/abs/2508.08503

JSPIM: A Skew-Aware PIM Accelerator for High-Performance Databases Join and Select Operations
Database applications are increasingly bottlenecked by memory bandwidth and latency due to the memory wall and the limited scalability of DRAM. Join queries, central to analytical workloads, require intensive memory access and are particularly vulnerable to inefficiencies in data movement. While Processing-in-Memory (PIM) offers a promising solution, existing designs typically reuse CPU-oriented join algorithms, limiting parallelism and incurring costly inter-chip communication. Additionally, d…

Simulation and Performance Studies for the Tau Air-Shower Mountain-Based Observatory
Carlos A. Arg\"uelles (for the TAMBO collaboration), Jeffrey Lazar (for the TAMBO collaboration), William Thompson (for the TAMBO collaboration), Pavel Zhelnin (for the TAMBO collaboration)
https://arxiv.org/abs/2507.08524

Simulation and Performance Studies for the Tau Air-Shower Mountain-Based Observatory
While IceCube's detection of astrophysical neutrinos at energies up to a few PeV has opened a new window to our Universe, much remains to be discovered regarding these neutrinos' origin and nature. In particular, the difficulty of differentiating electron- and tau-neutrino charged-current (CC) events limits our ability to measure precisely the flavor ratio of this flux. The Tau Air-Shower Mountain-Based Observatory (TAMBO) is a next-generation neutrino observatory capable of producing a high-pu…

MIDI-VALLE: Improving Expressive Piano Performance Synthesis Through Neural Codec Language Modelling
Jingjing Tang, Xin Wang, Zhe Zhang, Junichi Yamagishi, Geraint Wiggins, George Fazekas
https://arxiv.org/abs/2507.08530

MIDI-VALLE: Improving Expressive Piano Performance Synthesis Through Neural Codec Language Modelling
Generating expressive audio performances from music scores requires models to capture both instrument acoustics and human interpretation. Traditional music performance synthesis pipelines follow a two-stage approach, first generating expressive performance MIDI from a score, then synthesising the MIDI into audio. However, the synthesis models often struggle to generalise across diverse MIDI sources, musical styles, and recording environments. To address these challenges, we propose MIDI-VALLE, …

What Do I Do After My Songs Are Recorded, Mixed, and Mastered? 🚀
#MusicianTips
https://www.

What Do I Do After My Songs Are Recorded, Mixed, and Mastered?
Your song is recorded, mixed, and mastered—now what? This guide walks you through the next steps: release strategy, live performance setup, marketing, and career growth, with a clear action plan to ensure your music gets heard.

On the Impact of Classical and Quantum Communication Networks Upon Modular Quantum Computing Architecture System Performance
Pau Escofet, Abhijit Das, Sahar Ben Rached, Santiago Rodrigo, Jordi Domingo, Fabio Sebastiano, Masoud Babaie, Batuhan Keskin, Edoardo Charbon, Peter Haring Bol\'ivar, Maurizio Palesi, Elena Blokhina, Bogdan Staszewski, Avishek Nag, Artur Garcia-S\'aez, Sergi Abadal, Eduard Alarc\'on, Carmen G. Almud\'ever

On the Impact of Classical and Quantum Communication Networks Upon Modular Quantum Computing Architecture System Performance
Modular architectures are a promising approach to scaling quantum computers beyond the limits of monolithic designs. However, non-local communications between different quantum processors might significantly impact overall system performance. In this work, we investigate the role of the network infrastructure in modular quantum computing architectures, focusing on coherence loss due to communication constraints. We analyze the impact of classical network latency on quantum teleportation and ide…

An Empirical Study on Method-Level Performance Evolution in Open-Source Java Projects
Kaveh Shahedi, Nana Gyambrah, Heng Li, Maxime Lamothe, Foutse Khomh
https://arxiv.org/abs/2508.07084

An Empirical Study on Method-Level Performance Evolution in Open-Source Java Projects
Performance is a critical quality attribute in software development, yet the impact of method-level code changes on performance evolution remains poorly understood. While developers often make intuitive assumptions about which types of modifications are likely to cause performance regressions or improvements, these beliefs lack empirical validation at a fine-grained level. We conducted a large-scale empirical study analyzing performance evolution in 15 mature open-source Java projects hosted on…

Isto é interessante. Relatório sobre educação nos países da OCDE.
https://www.oecd.org/en/publications/education-at-a-glance-2024_c00cad36-en.html

Calibration and Performance Validation of the SST-1M Telescopes Using Crab Nebula Observations
Thomas Tavernier, Jakub Jury\v{s}ek, Vladimir Novotny
https://arxiv.org/abs/2508.08861

Calibration and Performance Validation of the SST-1M Telescopes Using Crab Nebula Observations
SST-1M is a prototype single mirror Small Sized Cherenkov Telescope designed for very high energy (VHE) gamma-ray astronomy. With a 4 meter primary mirror and a 5.6 meter focal length, it provides a wide 9 degree optical field of view, optimized for detecting VHE gamma-rays from 1 TeV to several hundred TeV. Its focal plane is equipped with DigiCam, a fully digital trigger and readout camera made of 1296 silicon photomultiplier (SiPM) pixels. The use of SiPM sensors enables observation under hi…

Evaluating Robot Program Performance with Power Consumption Driven Metrics in Lightweight Industrial Robots
Juan Heredia, Emil Stubbe Kolvig-Raun, Sune Lundo Sorensen, Mikkel Baun Kjaergaard
https://arxiv.org/abs/2508.06295

Evaluating Robot Program Performance with Power Consumption Driven Metrics in Lightweight Industrial Robots
The code performance of industrial robots is typically analyzed through CPU metrics, which overlook the physical impact of code on robot behavior. This study introduces a novel framework for assessing robot program performance from an embodiment perspective by analyzing the robot's electrical power profile. Our approach diverges from conventional CPU based evaluations and instead leverages a suite of normalized metrics, namely, the energy utilization coefficient, the energy conversion metric, a…

Ultra Ethernet's Design Principles and Architectural Innovations
Torsten Hoefler, Karen Schramm, Eric Spada, Keith Underwood, Cedell Alexander, Bob Alverson, Paul Bottorff, Adrian Caulfield, Mark Handley, Cathy Huang, Costin Raiciu, Abdul Kabbani, Eugene Opsasnick, Rong Pan, Adee Ran, Rip Sohan
https://arxiv.org/abs/2508.08906

Ultra Ethernet's Design Principles and Architectural Innovations
The recently released Ultra Ethernet (UE) 1.0 specification defines a transformative High-Performance Ethernet standard for future Artificial Intelligence (AI) and High-Performance Computing (HPC) systems. This paper, written by the specification's authors, provides a high-level overview of UE's design, offering crucial motivations and scientific context to understand its innovations. While UE introduces advancements across the entire Ethernet stack, its standout contribution is the novel Ultra…

HP2C-DT: High-Precision High-Performance Computer-enabled Digital Twin
E. Iraola (Barcelona Supercomputing Center), M. Garc\'ia-Lorenzo (Barcelona Supercomputing Center), F. Lordan-Gomis (Barcelona Supercomputing Center), F. Rossi (CITCEA, Universitat Polit\`ecnica de Catalunya), E. Prieto-Araujo (CITCEA, Universitat Polit\`ecnica de Catalunya), R. M. Badia (Barcelona Supercomputing Center)

HP2C-DT: High-Precision High-Performance Computer-enabled Digital Twin
Digital twins are transforming the way we monitor, analyze, and control physical systems, but designing architectures that balance real-time responsiveness with heavy computational demands remains a challenge. Cloud-based solutions often struggle with latency and resource constraints, while edge-based approaches lack the processing power for complex simulations and data-driven optimizations. To address this problem, we propose the High-Precision High-Performance Computer-enabled Digital Twin …

Lastly, I was wondering about the hard realities of the recent tragedy in terms of personnel decisions. Do Chiesa and Nunez look differently now? The rumors are that Chiesa wants a move back to Serie A. Playing time would change that, I bet. I keep hoping for him to have a dominant performance, but he just hasn't. You can see the glimpses of what he's got, but if it's hard to reach?
Nunez looked mature & patient (not just b/c of the short haircut!). Important pre-season…

💻 Coding Performance:
• LiveCodeBench v6: 53.7 (beats Claude Opus 4's 47.4) 🏆 • SWE-bench Verified: 65.8% single-attempt (competitive with top models) • SWE-bench Multilingual: 47.3% (leading multilingual coding) • EvalPlus: 80.3 (state-of-the-art performance) 📈 • Pass@1 LiveCodeBench: 26.3

Simulation of irradiated hybrid planar pixels modules at fluences expected at HL-LHC
Marco Bomben
https://arxiv.org/abs/2506.10823 https://

Simulation of irradiated hybrid planar pixels modules at fluences expected at HL-LHC
Signal loss is the main limitation on tracking/vertexing performance due to radiation damage effect to hybrid pixel detectors when irradiated at fluences expected at High Luminosity LHC (HL-LHC). It is important to have reliable predictions on the charge collection performance after irradiation in order to predict operational voltage values and test tracking algorithms robustness. In this paper the validation of combined TCAD and Monte Carlo simulations of hybrid silicon planar pixels sensors w…

This session has a plot twist at the end that qualifies as a cinematic masterpiece of the year:
https://developer.apple.com/videos/play/wwdc2025/308

Optimize CPU performance with Instruments - WWDC25 - Videos - Apple Developer
Learn how to optimize your app for Apple silicon with two new hardware-assisted tools in Instruments. We'll start by covering how to...

🔊 #NowPlaying on #BBCRadio3:
#ThroughTheNight
- Schoenberg, Gershwin and Rachmaninov
Elim Chan conducts the WDR Symphony Orchestra in Schoenberg's Piano Concerto and Gershwin's Rhapsody in Blue. Kirill Gerstein is the soloist in this sparkling performance.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002fk18

'Beyoncé Bowl' Christmas Ravens-Texans halftime show wins Emmy https://www.espn.com/nfl/story/_/id/45959699/beyonce-bowl-emmy-award-win-christmas-day-ravens-texans

Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study
Mahdi Dhaini, Juraj Vladika, Ege Erdogan, Zineb Attaoui, Gjergji Kasneci
https://arxiv.org/abs/2508.09776

Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study
In the rapidly evolving field of Explainable Natural Language Processing (NLP), textual explanations, i.e., human-like rationales, are pivotal for explaining model predictions and enriching datasets with interpretable labels. Traditional approaches rely on human annotation, which is costly, labor-intensive, and impedes scalability. In this work, we present an automated framework that leverages multiple state-of-the-art large language models (LLMs) to generate high-quality textual explanations. …

Oracle stock jumped 24% this week, its best performance since 2001, and is up 29% in 2025, vs. Nasdaq's 1%, after the company reported cloud momentum (Jordan Novet/CNBC)
https://www.cnbc.com/2025/06/13/oracles-stock-on-pace…

Oracle's stock closes out best week since 2001 on cloud momentum
Oracle investors haven't had a week this good since April 2001, after the stock had collapsed in the dot-com crash.

still boggled by Don Rickles in a Rare Dramatic Role.
I mean, he's still a ways off from unseating Mary Tyler Moore doing "Ordinary People",
but, man, that was a creditable performance.
#Monsterdon

U.S. Multifamily Market Snapshot — May 2025
Arbor - May 14, 2025
“Examining key benchmarks for the sector, the multifamily market’s consistent performance provides a solid foundation for continued expansion despite broader economic uncertainties, while well-positioned multifamily investors consider new opportunities.”
#ArborRealtyTrust

U.S. Multifamily Market Snapshot — May 2025
The U.S. multifamily market continued to settle into a normalized cycle, despite ongoing uncertainties surrounding the global economy and labor market. There is a rising tide of optimism for the sector ahead and investors are continuing to take advantage of market opportunities.

Vidyut3d: a GPU accelerated fluid solver for non-equilibrium plasmas on adaptive grids
Hariswaran Sitaraman, Nicholas Deak, Taaresh Taneja
https://arxiv.org/abs/2507.08200

Vidyut3d: a GPU accelerated fluid solver for non-equilibrium plasmas on adaptive grids
We present the numerical methods, programming methodology, verification, and performance assessment of a non-equilibrium plasma fluid solver that can effectively utilize current and upcoming central processing and graphics processing unit (CPU+GPU) architectures, in this work. Our plasma fluid model solves the coupled conservation equations for species transport, electrostatic Poisson and electron temperature on adaptive Cartesian grids. Our solver is written using performance portable adaptive…

OnlineBEV: Recurrent Temporal Fusion in Bird's Eye View Representations for Multi-Camera 3D Perception
Junho Koh, Youngwoo Lee, Jungho Kim, Dongyoung Lee, Jun Won Choi
https://arxiv.org/abs/2507.08644

OnlineBEV: Recurrent Temporal Fusion in Bird's Eye View Representations for Multi-Camera 3D Perception
Multi-view camera-based 3D perception can be conducted using bird's eye view (BEV) features obtained through perspective view-to-BEV transformations. Several studies have shown that the performance of these 3D perception methods can be further enhanced by combining sequential BEV features obtained from multiple camera frames. However, even after compensating for the ego-motion of an autonomous agent, the performance gain from temporal aggregation is limited when combining a large number of imag…

Mastery Learning Improves Performance on Complex Tasks on PCP Literacy Test
Chandana Srinivas, Elif E. Firat, Robert S. Laramee, Alark Joshi
https://arxiv.org/abs/2506.10164

Mastery Learning Improves Performance on Complex Tasks on PCP Literacy Test
Developing literacy with unfamiliar data visualization techniques such as Parallel Coordinate Plots (PCPs) can be a significant challenge for students. We adopted the Revised Bloom's taxonomy to instruct students on Parallel Coordinate Plots (PCPs) using Mastery Learning in the classroom. To evaluate Mastery Learning's impact, we conducted an intervention in a Data Visualization course to teach students about PCPs using the Revised Bloom's taxonomy with and without Mastery Learning. Based on ou…

Ground-Based Verification Method for Pointing and Acquisition Performance of Space Optical Communication System with Sub-Second Acquisition Time
Jianmin Wang, Zhiqian Su, Bin Li, Weiran Zheng, Haochun Gao
https://arxiv.org/abs/2508.08950

Ground-Based Verification Method for Pointing and Acquisition Performance of Space Optical Communication System with Sub-Second Acquisition Time
To meet the urgent need for sub-second link establishment in inter-satellite and satellite-to-ground free space optical communication, this paper presents a periscope-type optical communication terminal and a ground-based verification scheme for its pointing accuracy and acquisition performance, thereby avoiding costly in-orbit tests. This ground-based measurement method takes the positions of stellar constellations in inertial space as its reference. By establishing a theoretical attitude dete…

Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approaches
Andrea Moglia (Politecnico di Milano), Matteo Leccardi (Politecnico di Milano), Matteo Cavicchioli (Politecnico di Milano), Alice Maccarini (Universit\`a di Pavia), Marco Marcon (Politecnico di Milano), Luca Mainardi (Politecnico di Milano), Pietro Cerveri (Politecnico di Milano, Universit\`a di Pavia)

Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approaches
Following the successful paradigm shift of large language models, leveraging pre-training on a massive corpus of data and fine-tuning on different downstream tasks, generalist models have made their foray into computer vision. The introduction of Segment Anything Model (SAM) set a milestone on segmentation of natural images, inspiring the design of a multitude of architectures for medical image segmentation. In this survey we offer a comprehensive and in-depth investigation on generalist models…

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration
Jiaqi Lv, Xufeng He, Yanchen Liu, Xu Dai, Yang Hu, Shouyi Yin
https://arxiv.org/abs/2506.10401

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration
The rapid growth of deep learning has driven exponential increases in model parameters and computational demands. NVIDIA GPUs and their CUDA-based software ecosystem provide robust support for parallel computing, significantly alleviating computational bottlenecks. Meanwhile, due to the cultivation of user programming habits and the high performance of GPUs, the CUDA ecosystem has established a dominant position in the field of parallel software. This dominance requires other hardware platforms…

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

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

Quasi-Random Physics-informed Neural Networks
Tianchi Yu, Ivan Oseledets
https://arxiv.org/abs/2507.08121 https://arxiv.org/pdf/2507.08121 https://arxiv.org/html/2507.08121
arXiv:2507.08121v1 Announce Type: new
Abstract: Physics-informed neural networks have shown promise in solving partial differential equations (PDEs) by integrating physical constraints into neural network training, but their performance is sensitive to the sampling of points. Based on the impressive performance of quasi Monte-Carlo methods in high dimensional problems, this paper proposes Quasi-Random Physics-Informed Neural Networks (QRPINNs), which use low-discrepancy sequences for sampling instead of random points directly from the domain. Theoretically, QRPINNs have been proven to have a better convergence rate than PINNs. Empirically, experiments demonstrate that QRPINNs significantly outperform PINNs and some representative adaptive sampling methods, especially in high-dimensional PDEs. Furthermore, combining QRPINNs with adaptive sampling can further improve the performance.
toXiv_bot_toot

Analyzing the performance of a V2X-enhanced braking system in real-world crash situations
Jan Zimmermann, J\"org M\"onnich, Michael Scherl, Ignacio Llatser, Florian Wildsch\"utte, Frank Hofmann
https://arxiv.org/abs/2506.10535

Analyzing the performance of a V2X-enhanced braking system in real-world crash situations
By using an automated braking system, such as the Automatic Emergency Brake (AEB), crashes can be avoided in situations where the driver is unaware of an imminent collision. However, conventional AEB systems detect potential collision adversaries with onboard sensor systems, such as radars and cameras, that may fail in non-line-of-sight situations. By leveraging vehicle-to-everything (V2X) communication, information regarding an approaching vehicle can be received by the ego vehicle at an early…

DeepPolar : Breaking the BER-BLER Trade-off with Self-Attention and SMART (SNR-MAtched Redundancy Technique) decoding
Shubham Srivastava, Adrish Banerjee
https://arxiv.org/abs/2506.10166

DeepPolar+: Breaking the BER-BLER Trade-off with Self-Attention and SMART (SNR-MAtched Redundancy Technique) decoding
DeepPolar codes have recently emerged as a promising approach for channel coding, demonstrating superior bit error rate (BER) performance compared to conventional polar codes. Despite their excellent BER characteristics, these codes exhibit suboptimal block error rate (BLER) performance, creating a fundamental BER-BLER trade-off that severely limits their practical deployment in communication systems. This paper introduces DeepPolar+, an enhanced neural polar coding framework that systematicall…

Re-thinking Memory-Bound Limitations in CGRAs
Xiangfeng Liu, Zhe Jiang, Anzhen Zhu, Xiaomeng Han, Mingsong Lyu, Qingxu Deng, Nan Guan
https://arxiv.org/abs/2508.09570 https://…

Re-thinking Memory-Bound Limitations in CGRAs
Coarse-Grained Reconfigurable Arrays (CGRAs) are specialized accelerators commonly employed to boost performance in workloads with iterative structures. Existing research typically focuses on compiler or architecture optimizations aimed at improving CGRA performance, energy efficiency, flexibility, and area utilization, under the idealistic assumption that kernels can access all data from Scratchpad Memory (SPM). However, certain complex workloads-particularly in fields like graph analytics, ir…

Improving Dense Passage Retrieval with Multiple Positive Passages
Shuai Chang
https://arxiv.org/abs/2508.09534 https://arxiv.org/pdf/2508.09534

Improving Dense Passage Retrieval with Multiple Positive Passages
By leveraging a dual encoder architecture, Dense Passage Retrieval (DPR) has outperformed traditional sparse retrieval algorithms such as BM25 in terms of passage retrieval accuracy. Recently proposed methods have further enhanced DPR's performance. However, these models typically pair each question with only one positive passage during training, and the effect of associating multiple positive passages has not been examined. In this paper, we explore the performance of DPR when additional posit…

Improved SINR Approximation for Downlink SDMA-based Networks with Outdated Channel State Information
Maria Cecilia Fern\'andez Montefiore, Gustavo Gonz\'alez, F. Javier L\'opez-Mart\'inez, Fernando Gregorio
https://arxiv.org/abs/2508.09020

Improved SINR Approximation for Downlink SDMA-based Networks with Outdated Channel State Information
Understanding the performance of multi-user multiple-input multiple-output (MU-MIMO) systems under imperfect channel state information at the transmitter (CSIT) remains a critical challenge in next-generation wireless networks. In this context, accurate statistical modeling of the signal-tointerference- plus-noise ratio (SINR) is essential for enabling tractable performance analysis of multi-user systems. This paper presents an improved statistical approximation of the SINR for downlink (DL) MU…

Nintendo Switch 2 review: great build quality, better haptics, and improved performance for Switch 1 games, but weak battery life and only a few launch titles (The Shortcut)
https://www.theshortcut.com/p/nintendo-switch-2-review

Nintendo Switch 2 review: bigger, faster, and the best handheld since Game Boy
Our full review and 30+ photos of The Shortcut team testing the Nintendo Switch 2 for a week

Performance and extensions of the central data acquisition system for Phase II of the Pierre Auger Observatory
Paul Filip (for the Pierre Auger Collaboration)
https://arxiv.org/abs/2507.08747

Performance and extensions of the central data acquisition system for Phase II of the Pierre Auger Observatory
The Pierre Auger Observatory is a hybrid detector designed to observe and study ultra-high-energy particles of extraterrestrial origin. With its 27 fluorescence telescopes and over 1600 autonomously operating water-Cherenkov detectors spread over an area of 3000 km$^2$, it is world-leading in terms of exposure to cosmic rays and offers an unparalleled window into the physical processes that happen at energy scales unattainable by particle accelerators on Earth. Measurement information of candid…

Stetson Bennett sets career-high in dominant performance https://ugawire.usatoday.com/story/sports/college/bulldogs/football/2025/08/10/rams-stetson-bennett-career-high-performance-georgia-foo…

What Factors Affect LLMs and RLLMs in Financial Question Answering?
Peng Wang, Xuesi Hu, Jiageng Wu, Yuntao Zou, Qiancheng Zhang, Dagang Li
https://arxiv.org/abs/2507.08339

What Factors Affect LLMs and RLLMs in Financial Question Answering?
Recently, the development of large language models (LLMs) and reasoning large language models (RLLMs) have gained considerable attention from many researchers. RLLMs enhance the reasoning capabilities of LLMs through Long Chain-of-Thought (Long CoT) processes, significantly improving the performance of LLMs in addressing complex problems. However, there are few works that systematically explore what methods can fully unlock the performance of LLMs and RLLMs within the financial domain. To inves…

Knowledge Graph-Based approach for Sustainable 6G End-to-End System Design
Akshay Jain, Sylvaine Kerboeuf, Sokratis Barmpounakis, Crist\'obal Vinagre Z., Stefan Wendt, Dinh Thai Bui, Pol Alemany, Riccardo Nicolicchia, Jos\'e Mar\'ia Jorquera Valero, Dani Korpi, Mohammad Hossein Moghaddam, Mikko A. Uusitalo, Patrik Rugeland, Abdelkader Outtagarts, Karthik Upadhya, Panagiotis Demestichas, Raul Mu\~noz, Manuel Gil P\'erez, Daniel Adanza, Ricard Vilalta

Knowledge Graph-Based approach for Sustainable 6G End-to-End System Design
Previous generations of cellular communication, such as 5G, have been designed with the objective of improving key performance indicators (KPIs) such as throughput, latency, etc. However, to meet the evolving KPI demands as well as the ambitious sustainability targets for the ICT industry, 6G will need to be designed differently. Concretely, 6G will need to consider both the performance and sustainability targets for the various use cases it will serve. Moreover, like previous generations, 6G w…

🧠 General Intelligence:
• MMLU: 89.5 (near frontier performance) • MATH-500: 97.4 (exceptional mathematical reasoning) • AIME 2024: 69.6 (top-tier competition math) • Tool Use Benchmarks: Leading performance across multiple domains 🛠️

As for the youth, of which I tend to think of these games as sales presentations for loans, they were good.
At 16, Ngumoha really looks like an up and comer. He's patient mostly, but quite direct as well.
I liked Nyoni last year and I liked him yesterday. He wasn't flashy, but he always made the right decision at the right time.
Stephenson was good in the middle. I think he looked a bit nervous on the ball, but made no real mistakes. Good performance.
4/n

Beyond Predictions: A Study of AI Strength and Weakness Transparency Communication on Human-AI Collaboration
Tina Behzad, Nikolos Gurney, Ning Wang, David V. Pynadath
https://arxiv.org/abs/2508.09033

Beyond Predictions: A Study of AI Strength and Weakness Transparency Communication on Human-AI Collaboration
The promise of human-AI teaming lies in humans and AI working together to achieve performance levels neither could accomplish alone. Effective communication between AI and humans is crucial for teamwork, enabling users to efficiently benefit from AI assistance. This paper investigates how AI communication impacts human-AI team performance. We examine AI explanations that convey an awareness of its strengths and limitations. To achieve this, we train a decision tree on the model's mistakes, allo…

AdaptiveLLM: A Framework for Selecting Optimal Cost-Efficient LLM for Code-Generation Based on CoT Length
Junhang Cheng, Fang Liu, Chengru Wu, Li Zhang
https://arxiv.org/abs/2506.10525

AdaptiveLLM: A Framework for Selecting Optimal Cost-Efficient LLM for Code-Generation Based on CoT Length
While Large Language Models (LLMs) have significantly advanced code generation efficiency, they face inherent challenges in balancing performance and inference costs across diverse programming tasks. Dynamically selecting the optimal LLM based on task difficulty and resource constraints offers a promising approach to achieve an optimal balance between efficiency and performance. However, existing model selection methods are resource-intensive and often neglect cost efficiency. Moreover, these a…

Beyoncé Wins First Emmy for N.F.L. Halftime Show https://www.nytimes.com/2025/08/13/arts/television/beyonce-emmy-win-nfl-halftime-netflix-christmas.html

Tencent reports Q2 revenue up 15% YoY to ~$25.7B, vs. ~$24.8B est., gaming revenue up 17% YoY to ~$5.6B, global revenue up 35% YoY to ~$2.6B, a $7.7B net profit (Reuters)
https://www.reuters.com/world/china/chinas-tencent-…

A Neural Network-aided Low Complexity Chase Decoder for URLLC
Enrico Testi, Enrico Paolini
https://arxiv.org/abs/2506.10513 https://a…

A Neural Network-aided Low Complexity Chase Decoder for URLLC
Ultra-reliable low-latency communications (URLLC) demand decoding algorithms that simultaneously offer high reliability and low complexity under stringent latency constraints. While iterative decoding schemes for LDPC and Polar codes offer a good compromise between performance and complexity, they fall short in approaching the theoretical performance limits in the typical URLLC short block length regime. Conversely, quasi-ML decoding schemes for algebraic codes, like Chase-II decoding, exhibit …

Monitoring and performance of AugerPrime
Bel\'en Andrada (for the Pierre Auger Collaboration)
https://arxiv.org/abs/2507.08723 https://

Monitoring and performance of AugerPrime
The upgrade of Pierre Auger Observatory, AugerPrime, is a multi-hybrid system designed to improve the sensitivity and precision of ultra-high-energy cosmic ray measurements. It includes scintillator detectors positioned both atop the enhanced Water-Cherenkov detectors and buried nearby for direct muon measurements, along with radio and fluorescence detectors. In this contribution, we present an overview of the monitoring tools developed for all the components of AugerPrime, focusing on real-tim…

Deep Reinforcement Learning in Applied Control: Challenges, Analysis, and Insights
Klinsmann Agyei, Pouria Sarhadi, Daniel Polani
https://arxiv.org/abs/2507.08196

Deep Reinforcement Learning in Applied Control: Challenges, Analysis, and Insights
Over the past decade, remarkable progress has been made in adopting deep neural networks to enhance the performance of conventional reinforcement learning. A notable milestone was the development of Deep Q-Networks (DQN), which achieved human-level performance across a range of Atari games, demonstrating the potential of deep learning to stabilise and scale reinforcement learning. Subsequently, extensions to continuous control algorithms paved the way for a new paradigm in control, one that has…

OmniLLP: Enhancing LLM-based Log Level Prediction with Context-Aware Retrieval
Youssef Esseddiq Ouatiti, Mohammed Sayagh, Bram Adams, Ahmed E. Hassan
https://arxiv.org/abs/2508.08545

OmniLLP: Enhancing LLM-based Log Level Prediction with Context-Aware Retrieval
Developers insert logging statements in source code to capture relevant runtime information essential for maintenance and debugging activities. Log level choice is an integral, yet tricky part of the logging activity as it controls log verbosity and therefore influences systems' observability and performance. Recent advances in ML-based log level prediction have leveraged large language models (LLMs) to propose log level predictors (LLPs) that demonstrated promising performance improvements (AU…

Is Sparse Matrix Reordering Effective for Sparse Matrix-Vector Multiplication?
Omid Asudeh, Sina Mahdipour Saravani, Gerald Sabin, Fabrice Rastello, P Sadayappan
https://arxiv.org/abs/2506.10356

Is Sparse Matrix Reordering Effective for Sparse Matrix-Vector Multiplication?
This work evaluates the impact of sparse matrix reordering on the performance of sparse matrix-vector multiplication across different multicore CPU platforms. Reordering can significantly enhance performance by optimizing the non-zero element patterns to reduce total data movement and improve the load-balancing. We examine how these gains vary over different CPUs for different reordering strategies, focusing on both sequential and parallel execution. We address multiple aspects, including appro…

Cowboys' head coach impressed by 'gritty' performance from Marist Liufau https://www.si.com/nfl/cowboys/news/dallas-cowboys-head-coach-impressed-gritty-performance-marist-liufau

Compress Any Segment Anything Model (SAM)
Juntong Fan, Zhiwei Hao, Jianqiang Shen, Shang-Ling Jui, Yi Zhang, Jing-Xiao Liao, Feng-Lei Fan
https://arxiv.org/abs/2507.08765

Compress Any Segment Anything Model (SAM)
Due to the excellent performance in yielding high-quality, zero-shot segmentation, Segment Anything Model (SAM) and its variants have been widely applied in diverse scenarios such as healthcare and intelligent manufacturing. Therefore, effectively compressing SAMs has become an increasingly pressing practical need. In this study, we propose Birkhoff, a novel data-free compression algorithm for SAM and its variants. Unlike quantization, pruning, distillation, and other compression methods, Birkh…

Shedeur Sanders shines in preseason debut: Deion Sanders, LeBron James react to Browns rookie's performance

https://www.cbssports.com/nfl/news/shedeur…

When Is Prior Knowledge Helpful? Exploring the Evaluation and Selection of Unsupervised Pretext Tasks from a Neuro-Symbolic Perspective
Lin-Han Jia, Si-Yu Han, Wen-Chao Hu, Jie-Jing Shao, Wen-Da Wei, Zhi Zhou, Lan-Zhe Guo, Yu-Feng Li
https://arxiv.org/abs/2508.07299

When Is Prior Knowledge Helpful? Exploring the Evaluation and Selection of Unsupervised Pretext Tasks from a Neuro-Symbolic Perspective
Neuro-symbolic (Nesy) learning improves the target task performance of models by enabling them to satisfy knowledge, while semi/self-supervised learning (SSL) improves the target task performance by designing unsupervised pretext tasks for unlabeled data to make models satisfy corresponding assumptions. We extend the Nesy theory based on reliable knowledge to the scenario of unreliable knowledge (i.e., assumptions), thereby unifying the theoretical frameworks of SSL and Nesy. Through rigorous t…

Towards Scalable Training for Handwritten Mathematical Expression Recognition
Haoyang Li, Jiaqing Li, Jialun Cao, Zongyuan Yang, Yongping Xiong
https://arxiv.org/abs/2508.09220 …

Towards Scalable Training for Handwritten Mathematical Expression Recognition
Large foundation models have achieved significant performance gains through scalable training on massive datasets. However, the field of \textbf{H}andwritten \textbf{M}athematical \textbf{E}xpression \textbf{R}ecognition (HMER) has been impeded by the scarcity of data, primarily due to the arduous and costly process of manual annotation. To bridge this gap, we propose a novel method integrating limited handwritten formulas with large-scale LaTeX-rendered formulas by developing a scalable data e…

Performance and Practical Considerations of Large and Small Language Models in Clinical Decision Support in Rheumatology
Sabine Felde, R\"udiger Buchkremer, Gamal Chehab, Christian Thielscher, J\"org HW Distler, Matthias Schneider, Jutta G. Richter
https://arxiv.org/abs/2507.07983

Performance and Practical Considerations of Large and Small Language Models in Clinical Decision Support in Rheumatology
Large language models (LLMs) show promise for supporting clinical decision-making in complex fields such as rheumatology. Our evaluation shows that smaller language models (SLMs), combined with retrieval-augmented generation (RAG), achieve higher diagnostic and therapeutic performance than larger models, while requiring substantially less energy and enabling cost-efficient, local deployment. These features are attractive for resource-limited healthcare. However, expert oversight remains essenti…

Performance Evaluation of Brokerless Messaging Libraries
Lorenzo La Corte, Syed Aftab Rashid, Andrei-Marian Dan
https://arxiv.org/abs/2508.07934 https://ar…

Performance Evaluation of Brokerless Messaging Libraries
Messaging systems are essential for efficiently transferring large volumes of data, ensuring rapid response times and high-throughput communication. The state-of-the-art on messaging systems mainly focuses on the performance evaluation of brokered messaging systems, which use an intermediate broker to guarantee reliability and quality of service. However, over the past decade, brokerless messaging systems have emerged, eliminating the single point of failure and trading off reliability guarante…

Joe Burrow downplays preseason performance: 'Today was average' https://www.nfl.com/news/joe-burrow-downplays-preseason-performance-today-was-average

Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation
Junyan Ye, Dongzhi Jiang, Zihao Wang, Leqi Zhu, Zhenghao Hu, Zilong Huang, Jun He, Zhiyuan Yan, Jinghua Yu, Hongsheng Li, Conghui He, Weijia Li
https://arxiv.org/abs/2508.09987

Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation
Recently, GPT-4o has garnered significant attention for its strong performance in image generation, yet open-source models still lag behind. Several studies have explored distilling image data from GPT-4o to enhance open-source models, achieving notable progress. However, a key question remains: given that real-world image datasets already constitute a natural source of high-quality data, why should we use GPT-4o-generated synthetic data? In this work, we identify two key advantages of syntheti…

FermŠt, which uses AI to analyze e-commerce store performance and audience behavior for personalized campaigns, raised a $45M Series B led by VMG Partners (Chris Metinko/Axios)
https://www.axios.com/pro/enterprise-software-deals/2…

Exclusive: Commerce platform Fermàt raises $45M Series B
VMG Partners led the Series B.

Reveal-Bangla: A Dataset for Cross-Lingual Multi-Step Reasoning Evaluation
Khondoker Ittehadul Islam, Gabriele Sarti
https://arxiv.org/abs/2508.08933 https://

Reveal-Bangla: A Dataset for Cross-Lingual Multi-Step Reasoning Evaluation
Language models have demonstrated remarkable performance on complex multi-step reasoning tasks. However, their evaluation has been predominantly confined to high-resource languages such as English. In this paper, we introduce a manually translated Bangla multi-step reasoning dataset derived from the English Reveal dataset, featuring both binary and non-binary question types. We conduct a controlled evaluation of English-centric and Bangla-centric multilingual small language models on the origin…

Coordinated Power Management on Heterogeneous Systems
Zhong Zheng, Michael E. Papka, Zhiling Lan
https://arxiv.org/abs/2508.07605 https://arxiv.org/pdf/250…

Coordinated Power Management on Heterogeneous Systems
Performance prediction is essential for energy-efficient computing in heterogeneous computing systems that integrate CPUs and GPUs. However, traditional performance modeling methods often rely on exhaustive offline profiling, which becomes impractical due to the large setting space and the high cost of profiling large-scale applications. In this paper, we present OPEN, a framework consists of offline and online phases. The offline phase involves building a performance predictor and constructing…

Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification
Fabian C. Pe\~na, Steffen Herbold
https://arxiv.org/abs/2506.08581

Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification
This work evaluates Sentence-BERT for a multi-label code comment classification task seeking to maximize the classification performance while controlling efficiency constraints during inference. Using a dataset of 13,216 labeled comment sentences, Sentence-BERT models are fine-tuned and combined with different classification heads to recognize comment types. While larger models outperform smaller ones in terms of F1, the latter offer outstanding efficiency, both in runtime and GFLOPS. As result…

ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way
Rajarshi Roy, Devleena Das, Ankesh Banerjee, Arjya Bhattacharjee, Kousik Dasgupta, Subarna Tripathi
https://arxiv.org/abs/2507.08679

ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way
We introduce ByDeWay, a training-free framework designed to enhance the performance of Multimodal Large Language Models (MLLMs). ByDeWay uses a novel prompting strategy called Layered-Depth-Based Prompting (LDP), which improves spatial reasoning and grounding without modifying any model parameters. It segments the scene into closest, mid-range, and farthest layers using monocular depth estimation, then generates region-specific captions with a grounded vision-language model. These structured, d…

Utilizing Multilingual Encoders to Improve Large Language Models for Low-Resource Languages
Imalsha Puranegedara, Themira Chathumina, Nisal Ranathunga, Nisansa de Silva, Surangika Ranathunga, Mokanarangan Thayaparan
https://arxiv.org/abs/2508.09091

Utilizing Multilingual Encoders to Improve Large Language Models for Low-Resource Languages
Large Language Models (LLMs) excel in English, but their performance degrades significantly on low-resource languages (LRLs) due to English-centric training. While methods like LangBridge align LLMs with multilingual encoders such as the Massively Multilingual Text-to-Text Transfer Transformer (mT5), they typically use only the final encoder layer. We propose a novel architecture that fuses all intermediate layers, enriching the linguistic information passed to the LLM. Our approach features tw…

SAG-AFTRA ends the 300 day video game actors strike after reaching a tentative deal for a new Interactive Media Agreement with the major video game companies (Erik Hayden/The Hollywood Reporter)
https://www.hollywoodreporter.com/business

SAG-AFTRA Suspends Strike Against Video Game Companies, Paving Way for Return to Work
The union had taken a stand on artificial intelligence, among other concerns, as it looked to advocate for voice and performance capture workers.

A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation
Shuting He, Peilin Ji, Yitong Yang, Changshuo Wang, Jiayi Ji, Yinglin Wang, Henghui Ding
https://arxiv.org/abs/2508.09977

A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation
3D Gaussian Splatting (3DGS) has recently emerged as a powerful alternative to Neural Radiance Fields (NeRF) for 3D scene representation, offering high-fidelity photorealistic rendering with real-time performance. Beyond novel view synthesis, the explicit and compact nature of 3DGS enables a wide range of downstream applications that require geometric and semantic understanding. This survey provides a comprehensive overview of recent progress in 3DGS applications. It first introduces 2D foundat…

Diagnosing Failures in Large Language Models' Answers: Integrating Error Attribution into Evaluation Framework
Zishan Xu, Shuyi Xie, Qingsong Lv, Shupei Xiao, Linlin Song, Sui Wenjuan, Fan Lin
https://arxiv.org/abs/2507.08459

Diagnosing Failures in Large Language Models' Answers: Integrating Error Attribution into Evaluation Framework
With the widespread application of Large Language Models (LLMs) in various tasks, the mainstream LLM platforms generate massive user-model interactions daily. In order to efficiently analyze the performance of models and diagnose failures in their answers, it is essential to develop an automated framework to systematically categorize and attribute errors. However, existing evaluation models lack error attribution capability. In this work, we establish a comprehensive Misattribution Framework wi…

Harnessing Input-Adaptive Inference for Efficient VLN
Dongwoo Kang, Akhil Perincherry, Zachary Coalson, Aiden Gabriel, Stefan Lee, Sanghyun Hong
https://arxiv.org/abs/2508.09262

Harnessing Input-Adaptive Inference for Efficient VLN
An emerging paradigm in vision-and-language navigation (VLN) is the use of history-aware multi-modal transformer models. Given a language instruction, these models process observation and navigation history to predict the most appropriate action for an agent. While they have significantly improved performance, the scale of these models can be a bottleneck in practical settings with limited computational resources. In this work, we propose a novel input-adaptive navigation method to enhance VLN …

UtterTune: LoRA-Based Target-Language Pronunciation Edit and Control in Multilingual Text-to-Speech
Shuhei Kato
https://arxiv.org/abs/2508.09767 https://ar…

UtterTune: LoRA-Based Target-Language Pronunciation Edit and Control in Multilingual Text-to-Speech
We propose UtterTune, a lightweight adaptation method that fine-tunes a multilingual text-to-speech (TTS) system based on a large language model (LLM) architecture, designed to enhance the controllability of pronunciation in a target language while preserving performance in others. While LLM architectures have enabled TTS models to achieve remarkable naturalness, accurately modeling grapheme-to-phoneme (G2P) mapping and prosody remains challenging, especially when the model omits an explicit G2…

IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection
Yanhui Li, Yunkang Cao, Chengliang Liu, Yuan Xiong, Xinghui Dong, Chao Huang
https://arxiv.org/abs/2508.09178

IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection
Industrial anomaly detection is a critical component of modern manufacturing, yet the scarcity of defective samples restricts traditional detection methods to scenario-specific applications. Although Vision-Language Models (VLMs) demonstrate significant advantages in generalization capabilities, their performance in industrial anomaly detection remains limited. To address this challenge, we propose IAD-R1, a universal post-training framework applicable to VLMs of different architectures and par…

The AI Language Proficiency Monitor -- Tracking the Progress of LLMs on Multilingual Benchmarks
David Pomerenke, Jonas Nothnagel, Simon Ostermann
https://arxiv.org/abs/2507.08538 …

The AI Language Proficiency Monitor -- Tracking the Progress of LLMs on Multilingual Benchmarks
To ensure equitable access to the benefits of large language models (LLMs), it is essential to evaluate their capabilities across the world's languages. We introduce the AI Language Proficiency Monitor, a comprehensive multilingual benchmark that systematically assesses LLM performance across up to 200 languages, with a particular focus on low-resource languages. Our benchmark aggregates diverse tasks including translation, question answering, math, and reasoning, using datasets such as FLORES+…

Scaling Learned Image Compression Models up to 1 Billion
Yuqi Li, Haotian Zhang, Li Li, Dong Liu, Feng Wu
https://arxiv.org/abs/2508.09075 https://arxiv.or…

Scaling Learned Image Compression Models up to 1 Billion
Recent advances in large language models (LLMs) highlight a strong connection between intelligence and compression. Learned image compression, a fundamental task in modern data compression, has made significant progress in recent years. However, current models remain limited in scale, restricting their representation capacity, and how scaling model size influences compression performance remains unexplored. In this work, we present a pioneering study on scaling up learned image compression mode…

SGPMIL: Sparse Gaussian Process Multiple Instance Learning
Andreas Lolos, Stergios Christodoulidis, Maria Vakalopoulou, Jose Dolz, Aris Moustakas
https://arxiv.org/abs/2507.08711 …

SGPMIL: Sparse Gaussian Process Multiple Instance Learning
Multiple Instance Learning (MIL) offers a natural solution for settings where only coarse, bag-level labels are available, without having access to instance-level annotations. This is usually the case in digital pathology, which consists of gigapixel sized images. While deterministic attention-based MIL approaches achieve strong bag-level performance, they often overlook the uncertainty inherent in instance relevance. In this paper, we address the lack of uncertainty quantification in instance-…