Tootfinder

Taylor Swift has surprise performance at Tight End University in Tennessee https://www.nfl.com/news/taylor-swift-has-surprise-performance-at-tight-end-university-in-tennessee

Carrot Cache: High-Performance, SSD-Friendly #Caching #Library for #Java

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems
Valerii Gakh, Hayretdin Bahsi
https://arxiv.org/abs/2506.19109 …

Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems
Prompt injection threatens novel applications that emerge from adapting LLMs for various user tasks. The newly developed LLM-based software applications become more ubiquitous and diverse. However, the threat of prompt injection attacks undermines the security of these systems as the mitigation and defenses against them, proposed so far, are insufficient. We investigated the capabilities of early prompt injection detection systems, focusing specifically on the detection performance of technique…

Adversarial Observability and Performance Tradeoffs in Optimal Control
Filippos Fotiadis, Ufuk Topcu
https://arxiv.org/abs/2506.19829 https://

Adversarial Observability and Performance Tradeoffs in Optimal Control
We develop a feedback controller that minimizes the observability of a set of adversarial sensors of a linear system, while adhering to strict closed-loop performance constraints. We quantify the effectiveness of adversarial sensors using the trace of their observability Gramian and its inverse, capturing both average observability and the least observable state directions of the system. We derive theoretical lower bounds on these metrics under performance constraints, characterizing the fundam…

Taylor Swift joins Kane Brown in surprise performance at Tight End University https://www.espn.com/nfl/story/_/id/45583576/taylor-swift-travis-kelce-tight-end-university-nashville-live-performance-re…

Steam bekommt ein neues Overlay für Performance-Messungen
Bislang konnte man sich über Steam lediglich die fps anzeigen lassen – nicht immer die beste Metrik. Im Beta-Client gibt es nun einen neuen Performance-Monitor.

Fractality of Wireless Mesh Networks: Dimensional Effects on Network Performance
Marat Zaidyn, Sayat Akhtanov, Dana Turlykozhayeva, Symbat Temesheva, Almat Akhmetali, Alisher Skabylov, Nurzhan Ussipov
https://arxiv.org/abs/2506.19366

Fractality of Wireless Mesh Networks: Dimensional Effects on Network Performance
Wireless mesh networks (WMNs) depend on the spatial distribution of nodes, which directly influences connectivity, routing efficiency, and overall network performance. Conventional models typically assume uniform or random node placement, which inadequately represent the complex, hierarchical spatial patterns observed in practical deployments. In this study, we present a novel algorithm that constructs WMN topologies with tunable fractal dimensions, allowing precise control over spatial self-si…

Examination of Eye-Tracking, Head-Gaze, and Controller-Based Ray-casting in TMT-VR: Performance and Usability Across Adulthood
Panagiotis Kourtesis, Evgenia Giatzoglou, Panagiotis Vorias, Katerina Alkisti Gounari, Eleni Orfanidou, Chrysanthi Nega
https://arxiv.org/abs/2506.19519

Examination of Eye-Tracking, Head-Gaze, and Controller-Based Ray-casting in TMT-VR: Performance and Usability Across Adulthood
Virtual reality (VR) can enrich neuropsychological testing, yet the ergonomic trade-offs of its input modes remain under-examined. Seventy-seven healthy volunteers-young (19-29 y) and middle-aged (27-56 y)-completed a VR Trail-Making Test with three pointing methods: eye-tracking, head-gaze, and a six-degree-of-freedom hand controller. Completion time, spatial accuracy, and error counts for the simple (Trail A) and alternating (Trail B) sequences were analysed in 3 x 2 x 2 mixed-model ANOVAs; p…

Performance Analysis of OAMP Detection for ODDM Modulation in Satellite Communications
Yu Liu, Cunhua Pan, Tantao Gong, Yinlu Wang, Ming Chen
https://arxiv.org/abs/2506.19688

Performance Analysis of OAMP Detection for ODDM Modulation in Satellite Communications
Towards future 6G wireless networks, low earth orbit (LEO) satellites have been widely considered as a promising component to enhance the terrestrial communications. To ensure the link reliability of high-mobility satellite communication scenarios, the emerging orthogonal delay-Doppler division multiplexing (ODDM) modulation has attracted significant research attention. In this paper, we study the diversity gain achieved by ODDM modulation along with the mathematical analysis and numerical simu…

Practical insights on the effect of different encodings, ans\"atze and measurements in quantum and hybrid convolutional neural networks
Jes\'us Lozano-Cruz, Albert Nieto-Morales, Oriol Ball\'o-Gimbernat, Adan Garriga, Ant\'on Rodr\'iguez-Otero, Alejandro Borrallo-Rentero
https://arxiv.org/abs/2506.20355

Practical insights on the effect of different encodings, ansätze and measurements in quantum and hybrid convolutional neural networks
This study investigates the design choices of parameterized quantum circuits (PQCs) within quantum and hybrid convolutional neural network (HQNN and QCNN) architectures, applied to the task of satellite image classification using the EuroSAT dataset. We systematically evaluate the performance implications of data encoding techniques, variational ansätze, and measurement in approx. 500 distinct model configurations. Our analysis reveals a clear hierarchy of influence on model performance. For h…

Evaluating the impact of the L3 cache size of AMD EPYC CPUs on the performance of CFD applications
Marcin Lawenda, {\L}ukasz Szustak, L\'aszl\'o K\"ornyei, Flavio Cesar Cunha Galeazzo, Pawe{\l} Bratek
https://arxiv.org/abs/2505.17934

Evaluating the impact of the L3 cache size of AMD EPYC CPUs on the performance of CFD applications
In this work, the authors focus on assessing the impact of the AMD EPYC processor architecture on the performance of CFD applications. Several generations of architectures were analyzed, such as Rome, Milan, Milan X, Genoa, Genoa X and Bergamo, characterized by a different number of cores (64-128), L3 cache size (256 - 1152 MB) and RAM type (8-channel DDR4 or 12-channel DDR5). The research was conducted based on the OpenFOAM application using two memory-bound models: motorBike and Urban Air Pol…

Did you miss the live Broadway performance of Good Night & Good Luck on CNN?
Here's all 1hr 34min of it, starring George Clooney.
▶️ Good Night and Good Luck Broadway 2025 - CNN
https://youtube.com/watch?v=_3S2ymUw_Hs&si=4bT5GWzoaLnBmfnU

Metallicities from High-Resolution TRES Spectra with The Payne and uberMS: Performance Benchmarks and Literature Comparison
Emily K. Pass, Phillip A. Cargile, Victoria DiTomasso, Romy Rodr\'iguez Mart\'inez, David Charbonneau, David W. Latham, Andrew Vanderburg, Allyson Bieryla, Samuel N. Quinn, Lars A. Buchhave
https://…

Metallicities from High-Resolution TRES Spectra with The Payne and uberMS: Performance Benchmarks and Literature Comparison
As the field of exoplanetary astronomy has matured, there has been growing demand for precise stellar abundances to probe subtle correlations between stellar compositions and planetary demographics. However, drawing population-level conclusions from the disparate measurements in the literature is challenging, with various groups measuring metallicities using bespoke codes with differing line lists, radiative transfer models, and other assumptions. Here we use the neural-net framework uberMS to …

Tight End University: History, purpose of program and this year's surprise Taylor Swift performance

https://www.cbssports.com/nfl/news/tight-e…

The Effects of Air Pollution on Teenagers' Cognitive Performance: Evidence from School Leaving Examination in Poland
Agata Galkiewicz
https://arxiv.org/abs/2506.19801

The Effects of Air Pollution on Teenagers' Cognitive Performance: Evidence from School Leaving Examination in Poland
Random disturbances such as air pollution may affect cognitive performance, which, particularly in high-stakes settings, may have severe consequences for an individual's productivity and well-being. This paper examines the short-term effects of air pollution on school leaving exam results in Poland. I exploit random variation in air pollution between the days on which exams are held across three consecutive school years. I aim to capture this random variation by including school and time fixed …

"Indian researchers develop fast-charging sodium-ion battery, promising over 3,000 cycles"
#India #Batteries #Energy

Indian researchers develop fast-charging sodium-ion battery, promising over 3,000 cycles
NAtrium Super Ionic CONductor (NASICON) materials continue to show their potential for sodium-ion anodes in high-performance batteries, with fresh research out of India delving more into the materials.

Ihr wisst doch immer alles: ich suche ein Laptop für Linux. Office, Terminal, Internet, Container, um „an der Technologie dran zu bleiben“. 13 Zoll, bevorzugt AMD-CPU, Akkulaufzeit sticht Performance, gerne gebraucht, so 2-3 Jahre alt. Mein tägliches Arbeitstier wird weiterhin macOS bleiben. Irgendwelche Vorschläge oder Empfehlungen?

Large Language Models for Spreadsheets: Benchmarking Progress and Evaluating Performance with FLARE
Simon Thorne
https://arxiv.org/abs/2506.17330 https://

Large Language Models for Spreadsheets: Benchmarking Progress and Evaluating Performance with FLARE
Large Language Models (LLMs) have demonstrated some significant capabilities across various domains; however, their effectiveness in spreadsheet related tasks remains underexplored. This study introduces a foundation for a comprehensive benchmark framework to evaluate the performance of leading LLMs in executing spreadsheet functions, formula generation and data manipulation tasks. The benchmark encompasses tasks ranging from basic formula creation to complex, real world spreadsheet scenarios. …

How I Approach Performance Investigations
https://ricomariani.medium.com/how-i-approach-performance-investigations-69f4191c553f?source=rss-e6e91dab0708------2

General-domain FC-based shock-dynamics solver II: Non-smooth domains, accuracy and parallel performance
Daniel V. Leibovici, Oscar P. Bruno
https://arxiv.org/abs/2506.19370

General-domain FC-based shock-dynamics solver II: Non-smooth domains, accuracy and parallel performance
This contribution Part II of a two-part series, extends the general-domain FC-SDNN (Fourier Continuation Shock-Detecting Neural Network) introduces in Part I to enable treatment of non-smooth domains, it introduces a parallel implementation of the scheme with high-quality weak and strong scalability properties, and it illustrates the overall methodology for a variety of tests for the 2D Euler equations--including supersonic and hypersonic flows and shocks past obstacles with corners. The result…

On Error Rate Approximations for FSO Systems with Weak Turbulence and Pointing Errors
Carmen \'Alvarez Roa, Yunus Can G\"ultekin, Kaiquan Wu, Cornelis Willem Korevaar, Alex Alvarado
https://arxiv.org/abs/2506.19627

On Error Rate Approximations for FSO Systems with Weak Turbulence and Pointing Errors
Atmospheric attenuation, atmospheric turbulence, geometric spread, and pointing errors, degrade the performance of free-space optical transmission. In the weak turbulence regime, the probability density function describing the distribution of the channel fading coefficient that models these four effects is known in the literature. This function is an integral equation, which makes it difficult to find simple analytical expressions of important performance metrics such as the bit error rate (BER…

Mixed-Mode In-Memory Computing: Towards High-Performance Logic Processing In A Memristive Crossbar Array
Nan Du, Ilia Polian, Christopher Bengel, Kefeng Li, Ziang Chen, Xianyue Zhao, Uwe Huebner, Li-Wei Chen, Feng Liu, Massimiliano Di Ventra, Stephan Menzel, Heidemarie Krueger
https://arxiv.org/abs/2506.19063

Mixed-Mode In-Memory Computing: Towards High-Performance Logic Processing In A Memristive Crossbar Array
In-memory computing is a promising alternative to traditional computer designs, as it helps overcome performance limits caused by the separation of memory and processing units. However, many current approaches struggle with unreliable device behavior, which affects data accuracy and efficiency. In this work, the authors present a new computing method that combines two types of operations,those based on electrical resistance and those based on voltage, within each memory cell. This design improv…

Evaluating the Impact of Lean and Green Practices on Operational Performance: A Real Data-Driven Simulation Case Study
Farah Altarazi
https://arxiv.org/abs/2506.17354

Evaluating the Impact of Lean and Green Practices on Operational Performance: A Real Data-Driven Simulation Case Study
Global market-driven forces and customer needs are continuously changing. In the past, profitability and efficiency were the primary objectives of most companies. However, in recent decades, sustainable performance has emerged as a new competitive advantage. Companies have been compelled to adopt a concept that combines these evolving global interests with traditional goals resulting in the innovation of the lean and green approach. In this study, a research methodology that includes system a…

Say hello to The Fairphone (Gen.6)
Get ready for a new smartphone experience—modular, long-lasting, high-performance, distraction-free, and fairer than ever.
Launch video: https://www.youtube.com/watch?v=GpUkYw4i6Kk&t=2s
Guide tour video:

HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation
R. Alfaro, C. Alvarez, A. Andr\'es, E. Anita-Rangel, M. Araya, J. C. Arteaga-Vel\'azquez, D. Avila Rojas, H. A. Ayala Solares, R. Babu, P. Bangale, E. Belmont-Moreno, A. Bernal, T. Capistr\'an, A. Carrami\~nana, F. Carre\'on, S. Casanova, U. Cotti, E. De la Fuente, D. Depaoli, P. Desiati, N. Di Lalla, R. Diaz Hernandez, M. A. DuVernois, J. C. D\'iaz-V\'elez, K. Engel, T. Ergin, C. Espinoza,…

HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation
Improving gamma-hadron separation is one of the most effective ways to enhance the performance of ground-based gamma-ray observatories. With over a decade of continuous operation, the High-Altitude Water Cherenkov (HAWC) Observatory has contributed significantly to high-energy astrophysics. To further leverage its rich dataset, we introduce a machine learning approach for gamma-hadron separation. A Multilayer Perceptron shows the best performance, surpassing traditional and other Machine Learni…

Dive into semantic reranking at Berlin Buzzwords 2025! Athanasios Papaoikonomou will explore how different models and reranking depths impact search performance, revealing important patterns and the real-world efficiency vs. effectiveness trade-off.
Learn more: https://

Exploring reranking depth in modern search pipelines
The use of semantic reranking on top of a ‘cheaper’ retrieval step becomes more and more common in modern search applications. It offers a different cost quality profile to semantic retrieval trading indexing time compute for retrieval time compute. The depth represents the number of documents that we select to retrieve and feed into the reranking model in order to optimise their ordering. Intuitively, there is a “natural” trade-off between the uplift we can achieve by operating on an i…

i'm finna hike up to the used record store and see if i can sell some records
Machine-generated ALT: three photos showcase a collection of 12 diverse vinyl records, spanning genres such as rock, soul, jazz, Christmas music, and contemporary pop, featuring artists like Lucinda Williams, Frank Sinatra, Chick Corea, Ramsey Lewis, Lana Del Rey, Mavis Staples, and Hugh Masekela.

Multimodal large language models and physics visual tasks: comparative analysis of performance and costs
Giulia Polverini, Bor Gregorcic
https://arxiv.org/abs/2506.19662

Multimodal large language models and physics visual tasks: comparative analysis of performance and costs
Multimodal large language models (MLLMs) capable of processing both text and visual inputs are increasingly being explored for uses in physics education, such as tutoring, formative assessment, and grading. This study evaluates a range of publicly available MLLMs on a set of standardized, image-based physics research-based conceptual assessments (concept inventories). We benchmark 15 models from three major providers (Anthropic, Google, and OpenAI) across 102 physics items, focusing on two main…

AFGE | OPM’s New Performance Management Rules Are Illegal, Violate Labor Contracts
https://www.afge.org/article/opms-new-performance-management-rules-are-illegal-violate-labor-contracts/

When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs
Ammar Khairi, Daniel D'souza, Ye Shen, Julia Kreutzer, Sara Hooker
https://arxiv.org/abs/2506.20544

When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs
Recent advancements in large language models (LLMs) have shifted focus toward scaling inference-time compute, improving performance without retraining the model. A common approach is to sample multiple outputs in parallel, and select one of these as the final output. However, work to date has focused on English and a handful of domains such as math and code. In contrast, we are most interested in techniques that generalize across open-ended tasks, formally verifiable tasks, and across languages…

Disentangled representations of microscopy images
Jacopo Dapueto, Vito Paolo Pastore, Nicoletta Noceti, Francesca Odone
https://arxiv.org/abs/2506.20649 ht…

Disentangled representations of microscopy images
Microscopy image analysis is fundamental for different applications, from diagnosis to synthetic engineering and environmental monitoring. Modern acquisition systems have granted the possibility to acquire an escalating amount of images, requiring a consequent development of a large collection of deep learning-based automatic image analysis methods. Although deep neural networks have demonstrated great performance in this field, interpretability, an essential requirement for microscopy image an…

Performance of High-Intensity Electron Linacs as Drivers for Compact Neutron Sources
Javier Olivares Herrador, Laurence M. Wroe, Andrea Latina, Roberto Corsini, Walter Wuensch, Steinar Stapnes, Nuria Fuster-Mart{\i}nez, Benito Gimeno, Daniel Esperante
https://arxiv.org/abs/2506.20313

Performance of High-Intensity Electron Linacs as Drivers for Compact Neutron Sources
The demand for neutron production facilities is increasing, as the applications of neutron science are manifold. These applications drive the need for efficient and compact neutron sources. In this context, this paper explores the potential performance of normal-conducting compact electron linacs from 20 MeV to 500 MeV as drivers for neutron sources. Results from a G4BEAMLINE simulation study find optimum dimensions of a tungsten target and characterize the emitted neutron spectrum. Two electro…

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

Leveraging Correlation Across Test Platforms for Variance-Reduced Metric Estimation
Rachel Luo, Heng Yang, Michael Watson, Apoorva Sharma, Sushant Veer, Edward Schmerling, Marco Pavone
https://arxiv.org/abs/2506.20553

Leveraging Correlation Across Test Platforms for Variance-Reduced Metric Estimation
Learning-based robotic systems demand rigorous validation to assure reliable performance, but extensive real-world testing is often prohibitively expensive, and if conducted may still yield insufficient data for high-confidence guarantees. In this work, we introduce a general estimation framework that leverages paired data across test platforms, e.g., paired simulation and real-world observations, to achieve better estimates of real-world metrics via the method of control variates. By incorpora…

Survey of HPC in US Research Institutions
Peng Shu, Junhao Chen, Zhengliang Liu, Huaqin Zhao, Xinliang Li, Tianming Liu
https://arxiv.org/abs/2506.19019 ht…

Survey of HPC in US Research Institutions
The rapid growth of AI, data-intensive science, and digital twin technologies has driven an unprecedented demand for high-performance computing (HPC) across the research ecosystem. While national laboratories and industrial hyperscalers have invested heavily in exascale and GPU-centric architectures, university-operated HPC systems remain comparatively under-resourced. This survey presents a comprehensive assessment of the HPC landscape across U.S. universities, benchmarking their capabilities …

POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes
Ruijia Zhang, Zhengling Qi, Yue Wu, Xiangyu Zhang, Yanxun Xu
https://arxiv.org/abs/2506.20406

POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes
Dynamic treatment regimes (DTRs) provide a principled framework for optimizing sequential decision-making in domains where decisions must adapt over time in response to individual trajectories, such as healthcare, education, and digital interventions. However, existing statistical methods often rely on strong positivity assumptions and lack robustness under partial data coverage, while offline reinforcement learning approaches typically focus on average training performance, lack statistical gu…

Oha, hab ne Möglichkeit gefunden, alles #Performance-Kerne des #Computer​s auf 100 % zu bekommen. 😱 🤣

Deformable Medical Image Registration with Effective Anatomical Structure Representation and Divide-and-Conquer Network
Xinke Ma, Yongsheng Pan, Qingjie Zeng, Mengkang Lu, Bolysbek Murat Yerzhanuly, Bazargul Matkerim, Yong Xia
https://arxiv.org/abs/2506.19222

Deformable Medical Image Registration with Effective Anatomical Structure Representation and Divide-and-Conquer Network
Effective representation of Regions of Interest (ROI) and independent alignment of these ROIs can significantly enhance the performance of deformable medical image registration (DMIR). However, current learning-based DMIR methods have limitations. Unsupervised techniques disregard ROI representation and proceed directly with aligning pairs of images, while weakly-supervised methods heavily depend on label constraints to facilitate registration. To address these issues, we introduce a novel ROI-…

Efficient Channel Estimation for Rotatable Antenna-Enabled Wireless Communication
Xue Xiong, Beixiong Zheng, Wen Wu, Xiaodan Shao, Liang Dai, Ming-Min Zhao, Jie Tang
https://arxiv.org/abs/2506.20158

Efficient Channel Estimation for Rotatable Antenna-Enabled Wireless Communication
Rotatable antenna (RA) is a promising antenna architecture that exploits additional spatial degrees of freedom (DoFs) to enhance the communication performance. To fully obtain the performance gain provided by RAs, accurate channel state information (CSI) is essential for adjusting the orientation/boresight of each antenna. In this letter, we propose an efficient channel estimation scheme for RA communication systems, where the base station (BS) can sequentially and adaptively adjust the orienta…

Operation and performance of the CMS silicon strip tracker with proton-proton collisions at the CERN LHC
CMS Collaboration
https://arxiv.org/abs/2506.17195

Operation and performance of the CMS silicon strip tracker with proton-proton collisions at the CERN LHC
Salient aspects of the commissioning, calibration, and performance of the CMS silicon strip tracker are discussed, drawing on experience during operation with proton-proton collisions delivered by the CERN LHC. The data were obtained with a variety of luminosities. The operating temperature of the strip tracker was changed several times during this period and results are shown as a function of temperature in several cases. Details of the system performance are presented, including occupancy, si…

A Study on E2E Performance Improvement of Platooning Using Outdoor LiFi
Zhiyi Zhu, Eiji Takimoto, Patrick Finnerty, Chikara Ohta
https://arxiv.org/abs/2506.19304

A Study on E2E Performance Improvement of Platooning Using Outdoor LiFi
Platooning within autonomous vehicles has proven effective in addressing driver shortages and reducing fuel consumption. However, as platooning lengths increase, traditional C-V2X (cellular vehicle-to-everything) architectures are susceptible to end-to-end (E2E) latency increases. This is due to the necessity of relaying information through multiple hops from the leader vehicle to the last vehicle. To address this problem, this paper proposes a hybrid communication architecture based on a simul…

The unusual spectrum of the X-ray transient source XRISM J174610.8-290021 near the Galactic center
A. Yoshimoto, S. Yamauchi, M. Nobukawa, H. Uchiyama, K. K. Nobukawa, Y. Aoki, M. Ishida, Y. Kanemaru, M. Shidatsu, T. Hayashi, Y. Maeda, H. Matsumoto, Y. Tsuboi, H. Suzuki, H. Nakajima, Q. D. Wang, S. Eguchi, T. Yoneyama, T. Dotani, E. Behar, Y. Terada, N. Suzuki, M. Yoshimoto

The unusual spectrum of the X-ray transient source XRISM J174610.8-290021 near the Galactic center
The Galactic center region was observed with the XRISM X-ray observatory during the performance verification phase in 2024 and a point-like X-ray source was detected with the X-ray imager Xtend at a position of (RA, Dec)=(17h46m10.8s, -29°00'21''), which is thus named XRISM J174610.8-290021. This source was bright in February to March and showed time variations in count rate by more than one order of magnitude in one week. The 2-10 keV X-ray luminosity was ~$10^{35}$ erg/s for the assumed dist…

Atomic layer etching of InGaAs using sequential exposures of atomic hydrogen and oxygen gas
Mete M. Bayrak, Anthony J. Ardizzi, Sadhvikas Addamane, Kieran Cleary, Austin J. Minnich
https://arxiv.org/abs/2506.19749

Atomic layer etching of InGaAs using sequential exposures of atomic hydrogen and oxygen gas
The high frequency performance and yield of III-V semiconductor devices such as InP HEMTs is negatively impacted by subsurface etch damage and non-uniform etch depth over the wafer. Atomic layer etching (ALE) has the potential to overcome this challenge because of its ability to etch with Angstrom-scale precision, low damage, and intrinsic wafer-scale uniformity. Here, we report an ALE process for InGaAs based on sequential atomic hydrogen and oxygen gas exposures. An etch rate of 0.095 Å/cycl…

MEDEA: A Design-Time Multi-Objective Manager for Energy-Efficient DNN Inference on Heterogeneous Ultra-Low Power Platforms
Hossein Taji, Jos\'e Miranda, Miguel Pe\'on-Quir\'os, David Atienza
https://arxiv.org/abs/2506.19067

MEDEA: A Design-Time Multi-Objective Manager for Energy-Efficient DNN Inference on Heterogeneous Ultra-Low Power Platforms
The growing demand for on-device AI necessitates energy-efficient execution of DNN based applications on resource-constrained ultra-low power (ULP) platforms. Heterogeneous architectures, combining specialized processing elements (PEs), have emerged as a key solution for achieving the required performance and energy efficiency. However, optimizing energy while executing applications on these platforms requires efficiently managing platform resources like PEs, power features, and memory footprin…

Posterior Cram\'er-Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM
Benjamin J. B. Deutschmann, Xuhong Li, Florian Meyer, Erik Leitinger
https://arxiv.org/abs/2506.19957

Posterior Cramér-Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM
Radio-frequency simultaneous localization and mapping (RF-SLAM) methods jointly infer the position of mobile transmitters and receivers in wireless networks, together with a geometric map of the propagation environment. An inferred map of specular surfaces can be used to exploit non-line-of-sight components of the multipath channel to increase robustness, bypass obstructions, and improve overall communication and positioning performance. While performance bounds for user location are well estab…

Deposition-Dependent Coverage and Performance of Phosphonic Acid Interface Modifiers in Halide Perovskite Optoelectronics
Hannah Contreras, Aidan O'Brien, Margherita Taddei, Yangwei Shi, Fangyuan Jiang, Robert J. E. Westbrook, Yadong Zhang, Rajiv Giridharagopal, Paul A. Lee, Stephen Barlow, Seth R. Marder, Neal R. Armstrong, David S. Ginger
https…

Deposition-Dependent Coverage and Performance of Phosphonic Acid Interface Modifiers in Halide Perovskite Optoelectronics
In this work, we study the effect of various deposition methods for phosphonic acid interface modifiers commonly pursued as self-assembled monolayers in high-performance metal halide perovskite photovoltaics and light-emitting diodes. We compare the deposition of (2-(3,6-diiodo-9H-carbazol-9-yl)ethyl)phosphonic acid onto indium tin oxide (ITO) bottom contacts by varying three parameters: the method of deposition, specifically spin coating or prolonged dip coating, ITO surface treatment via HCl/…

gcor: A Python Implementation of Categorical Gini Correlation and Its Inference
Sameera Hewage
https://arxiv.org/abs/2506.19230 https://

gcor: A Python Implementation of Categorical Gini Correlation and Its Inference
Categorical Gini Correlation (CGC), introduced by Dang et al. (2020), is a novel dependence measure designed to quantify the association between a numerical variable and a categorical variable. It has appealing properties compared to existing dependence measures, such as zero correlation mutually implying independence between the variables. It has also shown superior performance over existing methods when applied to feature screening for classification. This article presents a Python implementa…

Assessing the Influence of Pavement Performance on Road Safety Through Crash Frequency and Severity Analysis
Prathyush Kumar Reddy Lebaku, Lu Gao, Jingran Sun, Xingju Wang, Xuejian Kang
https://arxiv.org/abs/2506.16485

Assessing the Influence of Pavement Performance on Road Safety Through Crash Frequency and Severity Analysis
Road safety is impacted by a range of factors that can be categorized into human, vehicle, and roadway/environmental elements. This research explores the connection between pavement performance and road safety, particularly in relation to crash frequency and severity, using data from the Iowa Department of Transportation (DOT) for 2022. By merging crash data with pavement inventory data, we conduct a spatial analysis that incorporates the geographical coordinates of crash sites with the conditi…

Salesforce launches Agentforce 3 with an observability tool called Command Center and MCP support, and says 8,000 customers have signed up to deploy Agentforce (Larry Dignan/Constellation Research)
https://www.constellationr.com/blog-news/i

Salesforce launches Agentforce 3, Command Center for visibility
Salesforce launched Agentforce 3, which features Agentforce Command Center, support for Model Context Protocol (MCP), and updated Atlas architecture to speed up reasoning and performance. The rollout maintains Salesforce's Agentforce cadence, which includes updates every few months as the company learns from enterprise use cases. In addition, Salesforce has added more than 30 partners to AgentExchange including AWS, Box, Cisco, Google Cloud, IBM and payments players such as PayPal and Stripe.

TFW you build a site with @… and web platform standards (== semantic HTML, modern CSS, a little bit of JavaScript, and a dash of ARIA) and you look at Lighthouse for the first time …
Looks like industry best practices actually work. (Sorry for using my platform to ruin anyone’s day. 🥰) 🍋

Aligning visual imagery to the operator improves geospatial situation awareness in a single-display 360-degree periscope concept https://cognitiveresearchjournal.springeropen.com/articles/10.1186/s41235-025-00646-1

Aligning visual imagery to the operator improves geospatial situation awareness in a single-display 360-degree periscope concept - Cognitive Research: Principles and Implications
Technological advances mean that it is now possible to represent the entire 360° view of the horizon to a submarine periscope operator simultaneously, in strips on a single display, as opposed to the restricted view offered through a conventional periscope aperture. Initial research showing performance improvements for such panoramic displays is promising. However, that research has yet to consider the importance of alignment between the visual representation of the environment on the periscop…

Enhancing Generalization of Spiking Neural Networks Through Temporal Regularization
Boxuan Zhang, Zhen Xu, Kuan Tao
https://arxiv.org/abs/2506.19256 https:…

Enhancing Generalization of Spiking Neural Networks Through Temporal Regularization
Spiking Neural Networks (SNNs) have received widespread attention due to their event-driven and low-power characteristics, making them particularly effective for processing event-based neuromorphic data. Recent studies have shown that directly trained SNNs suffer from severe overfitting issues due to the limited scale of neuromorphic datasets and the gradient mismatching problem, which fundamentally constrain their generalization performance. In this paper, we propose a temporal regularization …

Uncovering Conceptual Blindspots in Generative Image Models Using Sparse Autoencoders
Matyas Bohacek, Thomas Fel, Maneesh Agrawala, Ekdeep Singh Lubana
https://arxiv.org/abs/2506.19708

Uncovering Conceptual Blindspots in Generative Image Models Using Sparse Autoencoders
Despite their impressive performance, generative image models trained on large-scale datasets frequently fail to produce images with seemingly simple concepts -- e.g., human hands or objects appearing in groups of four -- that are reasonably expected to appear in the training data. These failure modes have largely been documented anecdotally, leaving open the question of whether they reflect idiosyncratic anomalies or more structural limitations of these models. To address this, we introduce a …

Rethinking Scientific Rankings with Breakthrough and Disruption Metrics: A Complex Network-Based Approach
Adarsh Raghuvanshi, Hrishidev Unni, Vinayak, Anirban Chakraborti
https://arxiv.org/abs/2506.18804

Rethinking Scientific Rankings with Breakthrough and Disruption Metrics: A Complex Network-Based Approach
Scientific progress is often driven by groundbreaking and disruptive research, yet traditional metrics for evaluating contributions to science often emphasize quantity over quality. In this study, we consider metrics that focus on the quality of scientific performance of countries and propose a novel framework that facilitates a competitive ranking of these countries based on their performance, highlighting the impact of their contributions. Using a bipartite network and centrality measures com…

PocketVina Enables Scalable and Highly Accurate Physically Valid Docking through Multi-Pocket Conditioning
Ahmet Sarigun, Bora Uyar, Vedran Franke, Altuna Akalin
https://arxiv.org/abs/2506.20043

PocketVina Enables Scalable and Highly Accurate Physically Valid Docking through Multi-Pocket Conditioning
Sampling physically valid ligand-binding poses remains a major challenge in molecular docking, particularly for unseen or structurally diverse targets. We introduce PocketVina, a fast and memory-efficient, search-based docking framework that combines pocket prediction with systematic multi-pocket exploration. We evaluate PocketVina across four established benchmarks--PDBbind2020 (timesplit and unseen), DockGen, Astex, and PoseBusters--and observe consistently strong performance in sampling phys…

Replaced article(s) found for hep-ex. https://arxiv.org/list/hep-ex/new
[1/1]:
- The performance of missing transverse momentum reconstruction and its significance with the ATLAS...
ATLAS Collaboration

Deep Dive into #uv #Dockerfiles by #Astral: Image Size, Performance & Best Practices

A digital twin of atomic ensemble quantum memories
Elizabeth Robertson, Benjamin Maa{\ss}, Konrad Tschernig, Janik Wolters
https://arxiv.org/abs/2506.20403

A digital twin of atomic ensemble quantum memories
Accurate performance estimation of experimentally demonstrated quantum memories is key to understand the nuances in their deployment in photonic quantum networks. While several software packages allow for accessible quantum simulation, they often do not account for the loss and noise in physical devices. We present a framework for modeling ensemble-based atomic quantum memories using the quantum channel formalism. We provide a Kraus matrix representation of several experimentally implemented st…

PPTP: Performance-Guided Physiological Signal-Based Trust Prediction in Human-Robot Collaboration
Hao Guo, Wei Fan, Shaohui Liu, Feng Jiang, Chunzhi Yi
https://arxiv.org/abs/2506.16677

PPTP: Performance-Guided Physiological Signal-Based Trust Prediction in Human-Robot Collaboration
Trust prediction is a key issue in human-robot collaboration, especially in construction scenarios where maintaining appropriate trust calibration is critical for safety and efficiency. This paper introduces the Performance-guided Physiological signal-based Trust Prediction (PPTP), a novel framework designed to improve trust assessment. We designed a human-robot construction scenario with three difficulty levels to induce different trust states. Our approach integrates synchronized multimodal p…

Exploring Developer Experience Factors in Software Ecosystems
Rodrigo Oliveira Zacarias, L\'eo Carvalho Ramos Antunes, M\'arcio de Oliveira Barros, Rodrigo Pereira dos Santos, Patricia Lago
https://arxiv.org/abs/2506.19757

Exploring Developer Experience Factors in Software Ecosystems
Context: Developer experience (DX) plays a key role in developers' performance and their continued involvement in a software ecosystem (SECO) platform. While researchers and practitioners have recognized several factors affecting DX in SECO platforms, a clear roadmap of the most influential factors is still missing. This is particularly important given the direct impact on developers' interest in SECO and their ongoing engagement with the common technological platform. Goal: This work aims to i…

Evolutionary Gait Reconfiguration in Damaged Legged Robots
Sahand Farghdani, Robin Chhabra
https://arxiv.org/abs/2506.19968 https://a…

Evolutionary Gait Reconfiguration in Damaged Legged Robots
Multi-legged robots deployed in complex missions are susceptible to physical damage in their legs, impairing task performance and potentially compromising mission success. This letter presents a rapid, training-free damage recovery algorithm for legged robots subject to partial or complete loss of functional legs. The proposed method first stabilizes locomotion by generating a new gait sequence and subsequently optimally reconfigures leg gaits via a developed differential evolution algorithm to…

KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs
Xin Fan Guo, Albert Merono Penuela, Sergio Maffeis, Fabio Pierazzi
https://arxiv.org/abs/2506.19802

KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs
Despite extensive research on Machine Learning-based Network Intrusion Detection Systems (ML-NIDS), their capability to detect diverse attack variants remains uncertain. Prior studies have largely relied on homogeneous datasets, which artificially inflate performance scores and offer a false sense of security. Designing systems that can effectively detect a wide range of attack variants remains a significant challenge. The progress of ML-NIDS continues to depend heavily on human expertise, whic…

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- AI-coupled HPC Workflow Applications, Middleware and Performance
Wes Brewer, Ana Gainaru, Fr\'ed\'eric Suter, Feiyi Wang, Murali Emani, Shantenu Jha

Replaced article(s) found for cs.PF. https://arxiv.org/list/cs.PF/new
[1/1]:
- Power-Capping Metric Evaluation for Improving Energy Efficiency in HPC Applications
Patrou, Wang, Elwasif, Eisenbach, Miller, Godoy, Hernandez

Non-Intrusive MLOps-Driven Performance Intelligence in Software Data Planes
Qiong Liu, Jianke Lin, Tianzhu Zhang, Leonardo Linguaglossa
https://arxiv.org/abs/2506.17658

Non-Intrusive MLOps-Driven Performance Intelligence in Software Data Planes
The last decade has witnessed the proliferation of network function virtualization (NFV) in the telco industry, thanks to its unparalleled flexibility, scalability, and cost-effectiveness. However, as the NFV infrastructure is shared by virtual network functions (VNFs), sporadic resource contentions are inevitable. Such contention makes it extremely challenging to guarantee the performance of the provisioned network services, especially in high-speed regimes (e.g., Gigabit Ethernet). Existing s…

Implementation and Analysis of Different Geomagnetic Field Models for Attitude Determination and Control System (ADCS) of a Satellite
Hoor Bano, Tatiana Podladchikova, Bisma Sajid, Dmitry Ris
https://arxiv.org/abs/2506.19588

Implementation and Analysis of Different Geomagnetic Field Models for Attitude Determination and Control System (ADCS) of a Satellite
An Attitude Determination and Control System is essential for orientation stability and performance of slew maneuvers on the satellite. This research focuses on comparing two different geomagnetic field models, Direct Dipole Model and International Geomagnetic Reference Field Model, for modeling of magnetometer and magnetorquers. Both these magnetic field models are compared and analyzed for two satellite attitude cases: orientation stability and unloading of reaction wheels. Magnetometer model…

Reflection-less filter for superconducting quantum circuits
Jessica Kedziora, Eric Q. Bui, Alec Yen, Andres E. Lombo, Kaidong Peng, Terence J. Weir, Kevin P. O'Brien
https://arxiv.org/abs/2506.20645

Reflection-less filter for superconducting quantum circuits
Protecting superconducting quantum circuits from non-ideal return loss, including out-of-band circulator behavior and enhancing the performance of broadband quantum-limited amplifiers can be accomplished using a superconducting version of a special class of microwave filters known as reflection-less filters. These filters can simultaneously permit low pass band loss to preserve quantum efficiency and broad band reflection-less characteristics in the stop and pass bands. The filter also suppress…

A novel plate-type phononic crystal for efficient vibration and noise attenuation performance
Hao Zhu, Kai Zhao
https://arxiv.org/abs/2506.20266 https://…

A novel plate-type phononic crystal for efficient vibration and noise attenuation performance
Mitigating low-frequency vibration or noise is of vital importance to both human health and mechanical engineering. Two-dimensional phononic crystal (PC) structures were proposed by attaching rubber and metallic cylinders on one or both sides of a thin plate to attenuate low-frequency vibration via the local resonance mechanism. The finite element method was employed to evaluate the band structure and associated vibration modes of the proposed PC structures. It was found that the bandgap of the…

Robust Embodied Self-Identification of Morphology in Damaged Multi-Legged Robots
Sahand Farghdani, Mili Patel, Robin Chhabra
https://arxiv.org/abs/2506.19984

Robust Embodied Self-Identification of Morphology in Damaged Multi-Legged Robots
Multi-legged robots (MLRs) are vulnerable to leg damage during complex missions, which can impair their performance. This paper presents a self-modeling and damage identification algorithm that enables autonomous adaptation to partial or complete leg loss using only data from a low-cost IMU. A novel FFT-based filter is introduced to address time-inconsistent signals, improving damage detection by comparing body orientation between the robot and its model. The proposed method identifies damaged …

Detecting LLM-Generated Short Answers and Effects on Learner Performance
Shambhavi Bhushan, Danielle R Thomas, Conrad Borchers, Isha Raghuvanshi, Ralph Abboud, Erin Gatz, Shivang Gupta, Kenneth Koedinger
https://arxiv.org/abs/2506.17196

Detecting LLM-Generated Short Answers and Effects on Learner Performance
The increasing availability of large language models (LLMs) has raised concerns about their potential misuse in online learning. While tools for detecting LLM-generated text exist and are widely used by researchers and educators, their reliability varies. Few studies have compared the accuracy of detection methods, defined criteria to identify content generated by LLM, or evaluated the effect on learner performance from LLM misuse within learning. In this study, we define LLM-generated text wit…

A parametric tensor ROM for the shallow water dam break problem
Md Rezwan Bin Mizan, Maxim Olshanskii, Ilya Timofeyev
https://arxiv.org/abs/2506.20007 http…

A parametric tensor ROM for the shallow water dam break problem
We develop a variant of a tensor reduced-order model (tROM) for the parameterized shallow-water dam-break problem. This hyperbolic system presents multiple challenges for model reduction, including a slow decay of the Kolmogorov $N$-width of the solution manifold, shock formation, and the loss of smooth solution dependence on parameters. These issues limit the performance of traditional Proper Orthogonal Decomposition based ROMs. Our tROM approach, based on a low-rank tensor decomposition, buil…

Diffusion-based Task-oriented Semantic Communications with Model Inversion Attack
Xuesong Wang, Mo Li, Xingyan Shi, Zhaoqian Liu, Shenghao Yang
https://arxiv.org/abs/2506.19886

Diffusion-based Task-oriented Semantic Communications with Model Inversion Attack
Semantic communication has emerged as a promising neural network-based system design for 6G networks. Task-oriented semantic communication is a novel paradigm whose core goal is to efficiently complete specific tasks by transmitting semantic information, optimizing communication efficiency and task performance. The key challenge lies in preserving privacy while maintaining task accuracy, as this scenario is susceptible to model inversion attacks. In such attacks, adversaries can restore or even…

On the Age of Information in Single-Server Queues with Aged Updates
Fernando Miguelez, Urtzi Ayesta, Josu Doncel, Maria Dolores Ugarte
https://arxiv.org/abs/2506.19648

On the Age of Information in Single-Server Queues with Aged Updates
The Age of Information (AoI) is a performance metric that quantifies the freshness of data in systems where timely updates are critical. Most state-of-the-art methods typically assume that packets enter the monitored system with zero age, neglecting situations, such as those prevalent in multi-hop networks or distributed sensing, where packets experience prior delays. In this paper, the AoI is investigated when packets have a non-zero initial age. We derive an expression for the average AoI in …

Replaced article(s) found for cs.PF. https://arxiv.org/list/cs.PF/new
[1/1]:
- The Gittins Index: A Design Principle for Decision-Making Under Uncertainty
Ziv Scully, Alexander Terenin

Shelby: Decentralized Storage Designed to Serve
Guy Goren, Andrew Hariri, Timothy D. R. Hartley, Ravi Kappiyoor, Alexander Spiegelman, David Zmick
https://arxiv.org/abs/2506.19233

Shelby: Decentralized Storage Designed to Serve
Existing decentralized storage protocols fall short of the service required by real-world applications. Their throughput, latency, cost-effectiveness, and availability are insufficient for demanding workloads such as video streaming, large-scale data analytics, or AI training. As a result, Web3 data-intensive applications are predominantly dependent on centralized infrastructure. Shelby is a high-performance decentralized storage protocol designed to meet demanding needs. It achieves fast, re…

Neural Collapse based Deep Supervised Federated Learning for Signal Detection in OFDM Systems
Kaidi Xu, Shenglong Zhou, Geoffrey Ye Li
https://arxiv.org/abs/2506.19476

Neural Collapse based Deep Supervised Federated Learning for Signal Detection in OFDM Systems
Future wireless networks are expected to be AI-empowered, making their performance highly dependent on the quality of training datasets. However, physical-layer entities often observe only partial wireless environments characterized by different power delay profiles. Federated learning is capable of addressing this limited observability, but often struggles with data heterogeneity. To tackle this challenge, we propose a neural collapse (NC) inspired deep supervised federated learning (NCDSFL) a…

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning
Mehmet Fatih Ozkan, Samuel Filgueira da Silva, Faissal El Idrissi, Prashanth Ramesh, Marcello Canova
https://arxiv.org/abs/2506.19146

Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning
Accurate parameter estimation in electrochemical battery models is essential for monitoring and assessing the performance of lithium-ion batteries (LiBs). This paper presents a novel approach that combines deep reinforcement learning (DRL) with an optimal experimental design (OED) framework to identify key electrochemical parameters of LiB cell models. The proposed method utilizes the twin delayed deep deterministic policy gradient (TD3) algorithm to optimize input excitation, thereby increasin…

Scalable entangling gates on ion qubits via structured light addressing
Xueying Mai, Liyun Zhang, Qinyang Yu, Junhua Zhang, Yao Lu
https://arxiv.org/abs/2506.19535

Scalable entangling gates on ion qubits via structured light addressing
A central challenge in developing practical quantum processors is maintaining low control complexity while scaling to large numbers of qubits. Trapped-ion systems excel in small-scale operations and support rapid qubit scaling via long-chain architectures. However, their performance in larger systems is hindered by spectral crowding in radial motional modes, a problem that forces reliance on intricate pulse-shaping techniques to maintain gate fidelities. Here, we overcome this challenge by deve…

Learn to Position -- A Novel Meta Method for Robotic Positioning
Dongkun Wang, Junkai Zhao, Yunfei Teng, Jieyang Peng, Wenjing Xue, Xiaoming Tao
https://arxiv.org/abs/2506.20445

Learn to Position -- A Novel Meta Method for Robotic Positioning
Absolute positioning accuracy is a vital specification for robots. Achieving high position precision can be challenging due to the presence of various sources of errors. Meanwhile, accurately depicting these errors is difficult due to their stochastic nature. Vision-based methods are commonly integrated to guide robotic positioning, but their performance can be highly impacted by inevitable occlusions or adverse lighting conditions. Drawing on the aforementioned considerations, a vision-free, m…

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network
Yining Pang, Chenghan Li
https://arxiv.org/abs/2506.19871

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network
Insurance fraud detection represents a pivotal advancement in modern insurance service, providing intelligent and digitalized monitoring to enhance management and prevent fraud. It is crucial for ensuring the security and efficiency of insurance systems. Although AI and machine learning algorithms have demonstrated strong performance in detecting fraudulent claims, the absence of standardized defense mechanisms renders current systems vulnerable to emerging adversarial threats. In this paper, w…

PAT: a new algorithm for all-gather and reduce-scatter operations at scale
Sylvain Jeaugey
https://arxiv.org/abs/2506.20252 https://a…

PAT: a new algorithm for all-gather and reduce-scatter operations at scale
This paper describes a new algorithm called PAT, for Parallel Aggregated Trees, and which can be used to implement all-gather and reduce-scatter operations. This algorithm works on any number of ranks, has a logarithmic number of network transfers for small size operations, minimizes long-distance communication, and requires a logarithmic amount of internal buffers, independently from the total operation size. It is aimed at improving the performance of the NCCL library in cases where the ring …

A Unified Framework for UAV-Based Free-Space Quantum Links: Beam Shaping and Adaptive Field-of-View Control
Mohammad Taghi Dabiri, Mazen Hasna, Saif Al-Kuwari, Khalid Qaraqe
https://arxiv.org/abs/2506.20336

A Unified Framework for UAV-Based Free-Space Quantum Links: Beam Shaping and Adaptive Field-of-View Control
This paper develops a comprehensive analytical framework for modeling and performance evaluation of unmanned aerial vehicles (UAVs)-to-ground quantum communication links, incorporating key physical impairments such as beam divergence, pointing errors at both transmitter and receiver, atmospheric attenuation, turbulence-induced fading, narrow field-of-view (FoV) filtering, and background photon noise. To overcome the limitations of conventional wide-beam assumptions, we introduce a grid-based ap…

Robust and Resilient Networks with Integrated Sensing, Communication and Computation
Ming-Chun Lee, Christian Eckrich, Vahid Jamali, Yu-Chih Huang, Arash Asadi, Li-Chun Wang
https://arxiv.org/abs/2506.19518

Robust and Resilient Networks with Integrated Sensing, Communication and Computation
Emerging applications such as networked robotics, intelligent transportation, smart factories, and virtual and augmented reality demand integrated perception and connectivity enabled by wireless communication. This has driven growing interests in integrated sensing, communication, and computation (ISCC) systems, with a primary focus on their efficient co-designs. However, as ISCC systems increasingly support critical applications, they must not only deliver high performance but also demonstrate…

Enhanced Image Recognition Using Gaussian Boson Sampling
Si-Qiu Gong, Ming-Cheng Chen, Hua-Liang Liu, Hao Su, Yi-Chao Gu, Hao-Yang Tang, Meng-Hao Jia, Yu-Hao Deng, Qian Wei, Hui Wang, Han-Sen Zhong, Xiao Jiang, Li Li, Nai-Le Liu, Chao-Yang Lu, Jian-Wei Pan
https://arxiv.org/abs/2506.19707

Enhanced Image Recognition Using Gaussian Boson Sampling
Gaussian boson sampling (GBS) has emerged as a promising quantum computing paradigm, demonstrating its potential in various applications. However, most existing works focus on theoretical aspects or simple tasks, with limited exploration of its capabilities in solving real-world practical problems. In this work, we propose a novel GBS-based image recognition scheme inspired by extreme learning machine (ELM) to enhance the performance of perceptron and implement it using our latest GBS device, J…

HRIBench: Benchmarking Vision-Language Models for Real-Time Human Perception in Human-Robot Interaction
Zhonghao Shi, Enyu Zhao, Nathaniel Dennler, Jingzhen Wang, Xinyang Xu, Kaleen Shrestha, Mengxue Fu, Daniel Seita, Maja Matari\'c
https://arxiv.org/abs/2506.20566

HRIBench: Benchmarking Vision-Language Models for Real-Time Human Perception in Human-Robot Interaction
Real-time human perception is crucial for effective human-robot interaction (HRI). Large vision-language models (VLMs) offer promising generalizable perceptual capabilities but often suffer from high latency, which negatively impacts user experience and limits VLM applicability in real-world scenarios. To systematically study VLM capabilities in human perception for HRI and performance-latency trade-offs, we introduce HRIBench, a visual question-answering (VQA) benchmark designed to evaluate VL…

RepuNet: A Reputation System for Mitigating Malicious Clients in DFL
Isaac Marroqui Penalva, Enrique Tom\'as Mart\'inez Beltr\'an, Manuel Gil P\'erez, Alberto Huertas Celdr\'an
https://arxiv.org/abs/2506.19892

RepuNet: A Reputation System for Mitigating Malicious Clients in DFL
Decentralized Federated Learning (DFL) enables nodes to collaboratively train models without a central server, introducing new vulnerabilities since each node independently selects peers for model aggregation. Malicious nodes may exploit this autonomy by sending corrupted models (model poisoning), delaying model submissions (delay attack), or flooding the network with excessive messages, negatively affecting system performance. Existing solutions often depend on rigid configurations or addition…

Terahertz channel performance under dynamic water surface reflections
Yapeng Ge, Jiacheng Liu, Jiayuan Cui, Mingxia Zhang, Wenbo Liu, Peian Li, Houjun Sun, Jianjun Ma
https://arxiv.org/abs/2506.18358

Terahertz channel performance under dynamic water surface reflections
As the terahertz (THz) band emerges as a pivotal technology for next-generation wireless communications, accurate channel modeling in dynamic environments becomes increasingly critical, particularly for scenarios involving reflective interactions with water surfaces. This article presents comprehensive experimental and theoretical investigations into THz channel (120-320 GHz) performance under dynamic water surface reflections. By developing and validating a modified dual-scale scattering model…

Time-Constrained Interception of Seeker-Equipped Interceptors with Bounded Input
Ashok Samrat R, Swati Singh, Shashi Ranjan Kumar
https://arxiv.org/abs/2506.19522

Time-Constrained Interception of Seeker-Equipped Interceptors with Bounded Input
This paper presents a nonlinear guidance scheme designed to achieve precise interception of stationary targets at a pre-specified impact time. The proposed strategy essentially accounts for the constraints imposed by the interceptor's seeker field-of-view (FOV) and actuator limitations, which, if ignored, can degrade guidance performance. To address these challenges, the guidance law incorporates known actuator bounds directly into its design, thereby improving overall interceptor effectiveness…

LoRaIN: A Constructive Interference-Assisted Reliable and Energy-Efficient LoRa Indoor Network
Mahbubur Rahman, Abusayeed Saifullah
https://arxiv.org/abs/2506.16409

LoRaIN: A Constructive Interference-Assisted Reliable and Energy-Efficient LoRa Indoor Network
LoRa is a promising communication technology for enabling the next-generation indoor Internet of Things applications. Very few studies, however, have analyzed its performance indoors. Besides, these indoor studies investigate mostly the RSSI and SNR of the received packets at the gateway, which, as we show, may not unfold the poor performance of LoRa and its MAC protocol, LoRaWAN, indoors in terms of reliability and energy-efficiency. In this paper, we extensively evaluate the performance of Lo…

[2025-05-26 Mon (UTC), 1 new article found for cs.PF Performance]
#toXiv_bot_toot

From High-SNR Radar Signal to ECG: A Transfer Learning Model with Cardio-Focusing Algorithm for Scenarios with Limited Data
Yuanyuan Zhang, Haocheng Zhao, Sijie Xiong, Rui Yang, Eng Gee Lim, Yutao Yue
https://arxiv.org/abs/2506.19358

From High-SNR Radar Signal to ECG: A Transfer Learning Model with Cardio-Focusing Algorithm for Scenarios with Limited Data
Electrocardiogram (ECG), as a crucial find-grained cardiac feature, has been successfully recovered from radar signals in the literature, but the performance heavily relies on the high-quality radar signal and numerous radar-ECG pairs for training, restricting the applications in new scenarios due to data scarcity. Therefore, this work will focus on radar-based ECG recovery in new scenarios with limited data and propose a cardio-focusing and -tracking (CFT) algorithm to precisely track the card…

Robust Robotic Exploration and Mapping Using Generative Occupancy Map Synthesis
Lorin Achey, Alec Reed, Brendan Crowe, Bradley Hayes, Christoffer Heckman
https://arxiv.org/abs/2506.20049

Robust Robotic Exploration and Mapping Using Generative Occupancy Map Synthesis
We present a novel approach for enhancing robotic exploration by using generative occupancy mapping. We introduce SceneSense, a diffusion model designed and trained for predicting 3D occupancy maps given partial observations. Our proposed approach probabilistically fuses these predictions into a running occupancy map in real-time, resulting in significant improvements in map quality and traversability. We implement SceneSense onboard a quadruped robot and validate its performance with real-worl…

An ETSI GS QKD compliant TLS implementation
Thomas Pr\'evost (I3S), Bruno Martin (I3S), Olivier Alibart (INPHYNI)
https://arxiv.org/abs/2506.19409 http…

An ETSI GS QKD compliant TLS implementation
A modification of the TLS protocol is presented, using our implementation of the Quantum Key Distribution (QKD) standard ETSI GS QKD 014 v1.1.1. We rely on the Rustls library for this. The TLS protocol is modified while maintaining backward compatibility on the client and server side. We thus wish to participate in the effort to generalize the use of QKD on the Internet. We used our protocol for a video conference call encrypted by QKD. Finally, we analyze the performance of our protocol, compa…

AgenticControl: An Automated Control Design Framework Using Large Language Models
Mohammad Narimani, Seyyed Ali Emami
https://arxiv.org/abs/2506.19160 http…

AgenticControl: An Automated Control Design Framework Using Large Language Models
Traditional control system design, reliant on expert knowledge and precise models, struggles with complex, nonlinear, or uncertain dynamics. This paper introduces AgenticControl, a novel multi-agent framework that automates controller design using coordinated Large Language Model (LLM) agents. Through structured JSON communication, these agents handle tasks including controller selection, scenario design, parameter optimization, performance evaluation, and decision-making. Through an actor-crit…

Retrieval-Confused Generation is a Good Defender for Privacy Violation Attack of Large Language Models
Wanli Peng, Xin Chen, Hang Fu, XinYu He, Xue Yiming, Juan Wen
https://arxiv.org/abs/2506.19889

Retrieval-Confused Generation is a Good Defender for Privacy Violation Attack of Large Language Models
Recent advances in large language models (LLMs) have made a profound impact on our society and also raised new security concerns. Particularly, due to the remarkable inference ability of LLMs, the privacy violation attack (PVA), revealed by Staab et al., introduces serious personal privacy issues. Existing defense methods mainly leverage LLMs to anonymize the input query, which requires costly inference time and cannot gain satisfactory defense performance. Moreover, directly rejecting the PVA …

Experimental Assessment of A Framework for In-body RF-backscattering Localization
Noa Jie Vives Zaguirre, Oscar Lasierra, Filip Lemic, Gerard Calvo Bartra, Pablo Jos\'e Galv\'an Calder\'on, Gines Garcia-Aviles, Sergi Abadal, Xavier Costa-P\'erez
https://arxiv.org/abs/2506.19499

Experimental Assessment of A Framework for In-body RF-backscattering Localization
Localization of in-body devices is beneficial for Gastrointestinal (GI) diagnosis and targeted treatment. Traditional methods such as imaging and endoscopy are invasive and limited in resolution, highlighting the need for innovative alternatives. This study presents an experimental framework for Radio Frequency (RF)-backscatter-based in-body localization, inspired by the ReMix approach, and evaluates its performance in real-world conditions. The experimental setup includes an in-body backscatte…

Comparison of spin-qubit architectures for quantum error-correcting codes
Mauricio Guti\'errez, Juan S. Rojas-Arias, David Obando, Chien-Yuan Chang
https://arxiv.org/abs/2506.17190

Comparison of spin-qubit architectures for quantum error-correcting codes
We investigate the performance of two quantum error-correcting codes, the surface code and the Bacon-Shor code, for implementation with spin qubits in silicon. In each case, we construct a logical qubit using a planar array of quantum dots, exploring two encoding schemes: one based solely on single-electron Zeeman qubits (Loss-DiVincenzo qubits), and a hybrid approach combining Zeeman and singlet-triplet qubits. For both codes, we evaluate key performance metrics, including logical state prepar…

Sensing-Aware Transmit Waveform/Receive Filter Design for OFDM-MBS Systems
Xinghe Li, Kainan Cheng, Huiyong Li, Huiyong Li, Ziyang Cheng
https://arxiv.org/abs/2506.20231

Sensing-Aware Transmit Waveform/Receive Filter Design for OFDM-MBS Systems
In this letter, we study the problem of cooperative sensing design for an orthogonal frequency division multiplexing (OFDM) multiple base stations (MBS) system. We consider a practical scenario where the base stations (BSs) exploit certain subcarriers to realize a sensing function. Since the high sidelobe level (SLL) of OFDM waveforms degrades radar detection for weak targets, and the cross-correlation generated by other BSs further exacerbates detection performance, we devise a joint design sc…

Coherent and Noncoherent Detection in Dense Arrays: Can We Ignore Mutual Coupling?
Aniol Mart\'i, Luca Sanguinetti, Jaume Riba, Meritxell Lamarca
https://arxiv.org/abs/2506.19470

Coherent and Noncoherent Detection in Dense Arrays: Can We Ignore Mutual Coupling?
This paper investigates the impact of mutual coupling on MIMO systems with densely deployed antennas. Leveraging multiport communication theory, we analyze both coherent and noncoherent detection approaches in a single-user uplink scenario where the receiver ignores mutual coupling effects. Simulation results indicate that while coherent detection is generally more accurate, it is highly sensitive to mismatches in the coupling model, leading to severe performance degradation when antennas are c…