Tootfinder

Multikernel architecture proposed for #Linux
https://www.osnews.com/story/143398/multikernel-architecture-proposed-for-linux/
Linux kernel updates with…

Decentralized AI-driven IoT Architecture for Privacy-Preserving and Latency-Optimized Healthcare in Pandemic and Critical Care Scenarios
Harsha Sammangi (Dakota State University), Aditya Jagatha (College of Business,Information Systems, Dakota State University), Giridhar Reddy Bojja (College of Business, Michigan Technological University), Jun Liu (College of Business,I.S, Dakota State University)

Decentralized AI-driven IoT Architecture for Privacy-Preserving and Latency-Optimized Healthcare in Pandemic and Critical Care Scenarios
AI Innovations in the IoT for Real-Time Patient Monitoring On one hand, the current traditional centralized healthcare architecture poses numerous issues, including data privacy, delay, and security. Here, we present an AI-enabled decentralized IoT architecture that can address such challenges during a pandemic and critical care settings. This work presents our architecture to enhance the effectiveness of the current available federated learning, blockchain, and edge computing approach, maximiz…

Emerging Trends in Software Architecture from the Practitioners Perspective: A Five Year Review
Ruoyu Su, Noman ahmad, Matteo Esposito, Andrea Janes, Davide Taibi, Valentina Lenarduzzi
https://arxiv.org/abs/2507.14554

Emerging Trends in Software Architecture from the Practitioners Perspective: A Five Year Review
Software architecture plays a central role in the design, development, and maintenance of software systems. With the rise of cloud computing, microservices, and containers, architectural practices have diversified. Understanding these shifts is vital. This study analyzes software architecture trends across eight leading industry conferences over five years. We investigate the evolution of software architecture by analyzing talks from top practitioner conferences, focusing on the motivations and…

from my link log —
Designing for the eye: optical corrections in architecture and typography.
https://www.nubero.ch/blog/015/
saved 2025-07-15 https://

Designing for the Eye – Optical Corrections in Architecture and Typography
The Nuberodesign Blog

Report Details Israel's 'Architecture of Genocidal Starvation' in Gaza | Common Dreams
https://www.commondreams.org/news/architecture-of-genocidal-starvation

AcceleratedKernels.jl: Cross-Architecture Parallel Algorithms from a Unified, Transpiled Codebase
Andrei-Leonard Nicusan, Dominik Werner, Simon Branford, Simon Hartley, Andrew J. Morris, Kit Windows-Yule
https://arxiv.org/abs/2507.16710

AcceleratedKernels.jl: Cross-Architecture Parallel Algorithms from a Unified, Transpiled Codebase
AcceleratedKernels.jl is introduced as a backend-agnostic library for parallel computing in Julia, natively targeting NVIDIA, AMD, Intel, and Apple accelerators via a unique transpilation architecture. Written in a unified, compact codebase, it enables productive parallel programming with minimised implementation and usage complexities. Benchmarks of arithmetic-heavy kernels show performance on par with C and OpenMP-multithreaded CPU implementations, with Julia sometimes offering more consisten…

Very nice article about LLM architecture, a bit too complicated for me but probably not for others..
https://magazine.sebastianraschka.com/p/the-big-llm-architecture-comparison

The Big LLM Architecture Comparison
From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture Design

Quantization-Aware Neuromorphic Architecture for Efficient Skin Disease Classification on Resource-Constrained Devices
Haitian Wang, Xinyu Wang, Yiren Wang, Karen Lee, Zichen Geng, Xian Zhang, Kehkashan Kiran, Yu Zhang, Bo Miao
https://arxiv.org/abs/2507.15958

Quantization-Aware Neuromorphic Architecture for Efficient Skin Disease Classification on Resource-Constrained Devices
Accurate and efficient skin lesion classification on edge devices is critical for accessible dermatological care but remains challenging due to computational, energy, and privacy constraints. We introduce QANA, a novel quantization-aware neuromorphic architecture for incremental skin lesion classification on resource-limited hardware. QANA effectively integrates ghost modules, efficient channel attention, and squeeze-and-excitation blocks for robust feature representation with low-latency and e…

Augmenting Von Neumann's Architecture for an Intelligent Future
Rajpreet Singh, Vidhi Kothari
https://arxiv.org/abs/2507.16628 https://

Augmenting Von Neumann's Architecture for an Intelligent Future
This work presents a novel computer architecture that extends the Von Neumann model with a dedicated Reasoning Unit (RU) to enable native artificial general intelligence capabilities. The RU functions as a specialized co-processor that executes symbolic inference, multi-agent coordination, and hybrid symbolic-neural computation as fundamental architectural primitives. This hardware-embedded approach allows autonomous agents to perform goal-directed planning, dynamic knowledge manipulation, and …

I like the wood board ceiling at the top of the stairwell near the elevator in the new Computer Science building
#photo #photography #architecture

Technical overview and architecture of the FastNet Machine Learning weather prediction model, version 1.0
Eric G. Daub, Tom Dunstan, Thusal Bennett, Matthew Burnand, James Chappell, Alejandro Coca-Castro, Noushin Eftekhari, J. Scott Hosking, Manvendra Janmaijaya, Jon Lillis, David Salvador-Jasin, Nathan Simpson, Oliver T Strickson, Ryan Sze-Yin Chan, Mohamad Elmasri, Lydia Allegranza France, Sam Madge, James Robinson, Adam A. Scaife, David Walters, Peter Yatsyshin, Theo McCaie, Levan B…

Technical overview and architecture of the FastNet Machine Learning weather prediction model, version 1.0
We present FastNet version 1.0, a data-driven medium range numerical weather prediction (NWP) model based on a Graph Neural Network architecture, developed jointly between the Alan Turing Institute and the Met Office. FastNet uses an encode-process-decode structure to produce deterministic global weather predictions out to 10 days. The architecture is independent of spatial resolution and we have trained models at 1$^{\circ}$ and 0.25$^{\circ}$ resolution, with a six hour time step. FastNet use…

Neural Architecture Search Algorithms for Quantum Autoencoders
Ankit Kulshrestha, Xiaoyuan Liu, Hayato Ushijima-Mwesigwa, Ilya Safro
https://arxiv.org/abs/2509.15451 https://

Neural Architecture Search Algorithms for Quantum Autoencoders
The design of quantum circuits is currently driven by the specific objectives of the quantum algorithm in question. This approach thus relies on a significant manual effort by the quantum algorithm designer to design an appropriate circuit for the task. However this approach cannot scale to more complex quantum algorithms in the future without exponentially increasing the circuit design effort and introducing unwanted inductive biases. Motivated by this observation, we propose to automate the p…

A Fault-Tolerant Architecture for Urban and Rural Digital Connectivity: Synergizing SDWMN, Direct-to-Mobile Broadcasting, and Hybrid Cloud Streaming
Pavel Malinovskiy
https://arxiv.org/abs/2507.14205

A Fault-Tolerant Architecture for Urban and Rural Digital Connectivity: Synergizing SDWMN, Direct-to-Mobile Broadcasting, and Hybrid Cloud Streaming
We propose an integrated architecture combining Software-Defined Wireless Mesh Networks (SDWMN), Direct-to-Mobile (D2M) broadcasting, and Kafka-based hybrid cloud streaming to improve wireless network performance in both urban and rural settings. The approach addresses urban congestion and rural digital exclusion through traffic offloading, enhanced fault tolerance, and equitable resource allocation. We model urban congestion $ρ_u = λ_t / μ_c$ and rural coverage deficit $δ_r = 1 - C_r / C_{…

APTx Neuron: A Unified Trainable Neuron Architecture Integrating Activation and Computation
Ravin Kumar
https://arxiv.org/abs/2507.14270 https://

APTx Neuron: A Unified Trainable Neuron Architecture Integrating Activation and Computation
We propose the APTx Neuron, a novel, unified neural computation unit that integrates non-linear activation and linear transformation into a single trainable expression. The APTx Neuron is derived from the APTx activation function, thereby eliminating the need for separate activation layers and making the architecture both computationally efficient and elegant. The proposed neuron follows the functional form $y = \sum_{i=1}^{n} ((α_i + \tanh(β_i x_i)) \cdot γ_i x_i) + δ$, where all parameter…

It’s been a long wait, but @…’s book is finally out and I just got my copy. Architecture for Flow: Adaptive Systems with Domain-Driven Design, Wardley Mapping, and Team Topologies - I’ve seen her talks on this subject and it’s a powerful combination of tools for getting large teams organized and productive.

Interviews with Apple's Tim Millet, Arun Mathias, and Kaiann Drance on the new neural accelerators in A19 Pro GPU cores, C1X modem, N1 wireless chip, and more (Katie Tarasov/CNBC)
https://www.cnbc.com/2025/09/21/apple-now-controls-a…

Apple takes control of all core chips in iPhone Air with new architecture to prioritize AI
Apple is quietly taking control of all the core chips in iPhone with new architecture to prioritize AI workloads on its most advanced chip, the A19 Pro.

The New Architecture of the Online Observation Quality System for the ASTRI Mini-Array Project
N. Parmiggiani (for the ASTRI Project), A. Bulgarelli (for the ASTRI Project), L. Castaldini (for the ASTRI Project), V. Fioretti (for the ASTRI Project), I. Abu (for the ASTRI Project), M. Capalbi (for the ASTRI Project), O. Catalano (for the ASTRI Project), V. Conforti (for the ASTRI Project), M. Corpora (for the ASTRI Project), A. Di Piano (for the ASTRI Project), R. Falco (for the ASTRI P…

The New Architecture of the Online Observation Quality System for the ASTRI Mini-Array Project
The ASTRI Mini-Array is an international collaboration led by the Italian National Institute for Astrophysics. The project aims to construct and operate an array of nine Imaging Atmospheric Cherenkov Telescopes to study gamma-ray sources at very high energy (TeV) and perform stellar intensity interferometry observations. We describe the updated Online Observation Quality System (OOQS) software architecture. The OOQS is one of the subsystems of the Supervisory Control and Data Acquisition (SCADA…

Worker Pool Design Pattern Explanation
https://coffeebytes.dev/en/software-architecture/worker-pool-design-pattern-explanation/

On The Road - To Xi’An/ Departure 🔜
在路上 - 去西安/ 离 🔜
📷 Pentax MX
🎞️Kodak Double-X 5222
#filmphotography #Photography #blackandwhite

Tree-like Pairwise Interaction Networks
Ronald Richman, Salvatore Scognamiglio, Mario V. W\"uthrich
https://arxiv.org/abs/2508.15678 https://arxiv.org…

Tree-like Pairwise Interaction Networks
Modeling feature interactions in tabular data remains a key challenge in predictive modeling, for example, as used for insurance pricing. This paper proposes the Tree-like Pairwise Interaction Network (PIN), a novel neural network architecture that explicitly captures pairwise feature interactions through a shared feed-forward neural network architecture that mimics the structure of decision trees. PIN enables intrinsic interpretability by design, allowing for direct inspection of interaction e…

An Octave-based Multi-Resolution CQT Architecture for Diffusion-based Audio Generation
Maur\'icio do V. M. da Costa, Eloi Moliner
https://arxiv.org/abs/2509.16603 https://…

An Octave-based Multi-Resolution CQT Architecture for Diffusion-based Audio Generation
This paper introduces MR-CQTdiff, a novel neural-network architecture for diffusion-based audio generation that leverages a multi-resolution Constant-$Q$ Transform (C$Q$T). The proposed architecture employs an efficient, invertible CQT framework that adjusts the time-frequency resolution on an octave-by-octave basis. This design addresses the issue of low temporal resolution at lower frequencies, enabling more flexible and expressive audio generation. We conduct an evaluation using the Fréchet…

Köln old town

A Novel Domain-Aware CNN Architecture for Faster-than-Nyquist Signaling Detection
Osman Tokluoglu, Enver Cavus, Ebrahim Bedeer, Halim Yanikomeroglu
https://arxiv.org/abs/2507.15291

A Novel Domain-Aware CNN Architecture for Faster-than-Nyquist Signaling Detection
This paper proposes a convolutional neural network (CNN)-based detector for faster-than-Nyquist (FTN) signaling that employs structured fixed kernel layers with domain-informed masking to mitigate intersymbol interference (ISI). Unlike standard CNNs with sliding kernels, the proposed method utilizes fixed-position kernels to directly capture ISI effects at varying distances from the central symbol. A hierarchical filter allocation strategy is also introduced, assigning more filters to earlier l…

Predict, Reposition, and Allocate: A Greedy and Flow-Based Architecture for Sustainable Urban Food Delivery
Aqsa Ashraf Makhdomi, Iqra Altaf Gillani
https://arxiv.org/abs/2507.15282

Predict, Reposition, and Allocate: A Greedy and Flow-Based Architecture for Sustainable Urban Food Delivery
The rapid proliferation of food delivery platforms has reshaped urban mobility but has also contributed significantly to environmental degradation through increased greenhouse gas emissions. Existing optimization mechanisms produce sub-optimal outcomes as they do not consider environmental sustainability their optimization objective. This study proposes a novel eco-friendly food delivery optimization framework that integrates demand prediction, delivery person routing, and order allocation to m…

Data-Driven Adaptive Gradient Recovery for Unstructured Finite Volume Computations
G. de Rom\'emont, F. Renac, F. Chinesta, J. Nunez, D. Gueyffier
https://arxiv.org/abs/2507.16571

Data-Driven Adaptive Gradient Recovery for Unstructured Finite Volume Computations
We present a novel data-driven approach for enhancing gradient reconstruction in unstructured finite volume methods for hyperbolic conservation laws, specifically for the 2D Euler equations. Our approach extends previous structured-grid methodologies to unstructured meshes through a modified DeepONet architecture that incorporates local geometry in the neural network. The architecture employs local mesh topology to ensure rotation invariance, while also ensuring first-order constraint on the le…

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

Exploration of Evolving Quantum Key Distribution Network Architecture Using Model-Based Systems Engineering
Hayato Ishida, Amal Elsokary, Maria Aslam, Catherine White, Michael J. de C. Henshaw, Siyuan Ji
https://arxiv.org/abs/2508.15733

Exploration of Evolving Quantum Key Distribution Network Architecture Using Model-Based Systems Engineering
Realisation of significant advances in capabilities of sensors, computing, timing, and communication enabled by quantum technologies is dependent on engineering highly complex systems that integrate quantum devices into existing classical infrastructure. A systems engineering approach is considered to address the growing need for quantum-secure telecommunications that overcome the threat to encryption caused by maturing quantum computation. This work explores a range of existing and future quan…

Dextr: Zero-Shot Neural Architecture Search with Singular Value Decomposition and Extrinsic Curvature
Rohan Asthana, Joschua Conrad, Maurits Ortmanns, Vasileios Belagiannis
https://arxiv.org/abs/2508.12977

Dextr: Zero-Shot Neural Architecture Search with Singular Value Decomposition and Extrinsic Curvature
Zero-shot Neural Architecture Search (NAS) typically optimises the architecture search process by exploiting the network or gradient properties at initialisation through zero-cost proxies. The existing proxies often rely on labelled data, which is usually unavailable in real-world settings. Furthermore, the majority of the current methods focus either on optimising the convergence and generalisation attributes or solely on the expressivity of the network architectures. To address both limitatio…

I consider it a failure of the GOV.UK information architecture that organisations retrospectively edit "news stories" years later to bring them into line with current policy.
(They are marked as changed, this isn't subterfuge. It just sets weird expectations about how "news" works.)

Language-Guided Tuning: Enhancing Numeric Optimization with Textual Feedback
Yuxing Lu, Yucheng Hu, Nan Sun, Xukai Zhao
https://arxiv.org/abs/2508.15757 https://

Language-Guided Tuning: Enhancing Numeric Optimization with Textual Feedback
Configuration optimization remains a critical bottleneck in machine learning, requiring coordinated tuning across model architecture, training strategy, feature engineering, and hyperparameters. Traditional approaches treat these dimensions independently and lack interpretability, while recent automated methods struggle with dynamic adaptability and semantic reasoning about optimization decisions. We introduce Language-Guided Tuning (LGT), a novel framework that employs multi-agent Large Langua…

Analysis of an Architecture for Integrated Sensing and Communication in 5G OpenRAN
Daniel Lindenschmitt, Tobias Jung, Prudhvi Kumar Kakani, Torsten Reissland, Norman Franchi, Hans D. Schotten
https://arxiv.org/abs/2509.16917

Analysis of an Architecture for Integrated Sensing and Communication in 5G OpenRAN
This paper analyzes the functional requirements and architectural considerations for Integrated Sensing and Communication ( ISAC) in a 5G Open Radio Access Network (OpenRAN) environment, with emphasis on secure and modular deployment. Focusing on a mono-static, half-duplex sensing approach, it evaluates radar setup options, signal types, and processing placement within the Radio Access Network ( RAN), considering performance and security implications. The proposed architecture minimizes hardwar…

This has got to be the most welcoming and beautiful public toilet facility I've ever seen. Look at the way it's lit - gorgeous!
https://www.archpaper.com/2025/08/toronto-to-the-loo-competition-public-restrooms/

Toronto’s To the Loo competition proves public restrooms can be beautiful too
the Toronto Public Space Committee, together with Cyan Station Architecture, have launched the To the Loo design competition to highlight the city’s shortage of public restrooms and to show that bathrooms can be an inspiring part of the public realm.

Time-adaptive SympNets for separable Hamiltonian systems
Konrad Janik, Peter Benner
https://arxiv.org/abs/2509.16026 https://arxiv.org/pdf/2509.16026

Time-adaptive SympNets for separable Hamiltonian systems
Measurement data is often sampled irregularly i.e. not on equidistant time grids. This is also true for Hamiltonian systems. However, existing machine learning methods, which learn symplectic integrators, such as SympNets [20] and HénonNets [4] still require training data generated by fixed step sizes. To learn time-adaptive symplectic integrators, an extension to SympNets, which we call TSympNets, was introduced in [20]. We adapt the architecture of TSympNets and extend them to non-autonomous…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[10/10]:
- TSPC: A Two-Stage Phoneme-Centric Architecture for code-switching Vietnamese-English Speech Recog...
Minh N. H. Nguyen, Anh Nguyen Tran, Dung Truong Dinh, Nam Van Vo

Hear Your Code Fail, Voice-Assisted Debugging for Python
Sayed Mahbub Hasan Amiri, Md. Mainul Islam, Mohammad Shakhawat Hossen, Sayed Majhab Hasan Amiri, Mohammad Shawkat Ali Mamun, Sk. Humaun Kabir, Naznin Akter
https://arxiv.org/abs/2507.15007

Hear Your Code Fail, Voice-Assisted Debugging for Python
This research introduces an innovative voice-assisted debugging plugin for Python that transforms silent runtime errors into actionable audible diagnostics. By implementing a global exception hook architecture with pyttsx3 text-to-speech conversion and Tkinter-based GUI visualization, the solution delivers multimodal error feedback through parallel auditory and visual channels. Empirical evaluation demonstrates 37% reduced cognitive load (p<0.01, n=50) compared to traditional stack-trace debugg…

StackTrans: From Large Language Model to Large Pushdown Automata Model
Kechi Zhang, Ge Li, Jia Li, Huangzhao Zhang, Yihong Dong, Jia Li, Jingjing Xu, Zhi Jin
https://arxiv.org/abs/2507.15343

StackTrans: From Large Language Model to Large Pushdown Automata Model
The Transformer architecture has emerged as a landmark advancement within the broad field of artificial intelligence, effectively catalyzing the advent of large language models (LLMs). However, despite its remarkable capabilities and the substantial progress it has facilitated, the Transformer architecture still has some limitations. One such intrinsic limitation is its inability to effectively capture the Chomsky hierarchy, such as regular expressions or deterministic context-free grammars. Dr…

FOGNITE: Federated Learning-Enhanced Fog-Cloud Architecture
Somayeh Sobati-M
https://arxiv.org/abs/2507.16668 https://arxiv.org/pdf/2…

FOGNITE: Federated Learning-Enhanced Fog-Cloud Architecture
Modern smart grids demand fast, intelligent, and energy-aware computing at the edge to manage real time fluctuations and ensure reliable operation. This paper introduces FOGNITE Fog-based Grid In intelligence with Neural Integration and Twin based Execution a next-generation fog cloud framework designed to enhance autonomy, resilience, and efficiency in distributed energy systems. FOGNITE combines three core components: federated learning, reinforcement learning, and digital twin validation. Ea…

On The Road - To Xi’An/Objects 🔶
在路上 - 去西安/物 🔶
📷 Pentax MX
🎞️Kodak Double-X 5222
#filmphotography #Photography #blackandwhite

The Digital Life of Parisian Parks: Multifunctionality and Urban Context Uncovered by Mobile Application Traffic
Andr\'e Felipe Zanella, Linus W. Dietz, Sanja \v{S}\'cepanovi\'c, Ke Zhou, Zbigniew Smoreda, Daniele Quercia
https://arxiv.org/abs/2508.15516

The Digital Life of Parisian Parks: Multifunctionality and Urban Context Uncovered by Mobile Application Traffic
Landscape architecture typically considers urban parks through the lens of form and function. While past research on equitable access has focused mainly on form, studies of functions have been constrained by limited scale and coarse measurement. Existing efforts have partially quantified functions through small-scale surveys and movement data (e.g., GPS) or general usage records (e.g., CDR), but have not captured the activities and motivations underlying park visits. As a result, our understand…

Quantum teleportation of an elemental silicon nanophotonic CNOT gate
Kai-Chi Chang, Xiang Cheng, Felix Ribuot-Hirsch, Murat Can Sarihan, Yujie Chen, Jaime Gonzalo Flor Flores, Mingbin Yu, Patrick Guo-Qiang Lo, Dim-Lee Kwong, Chee Wei Wong
https://arxiv.org/abs/2507.16783

Quantum teleportation of an elemental silicon nanophotonic CNOT gate
Large-scale quantum computers possess the capacity to effectively tackle practical problems that can be insurmountable for classical computers. The main challenge in building these quantum computers is to realize scalable modules for remote qubits and entanglement. By assembling small, specialized parts into a larger architecture, the modular approach mitigates complexity and uncertainty. Such a distributed architecture requires non-local quantum gate operations between remote qubits. An essent…

DSPv2: Improved Dense Policy for Effective and Generalizable Whole-body Mobile Manipulation
Yue Su, Chubin Zhang, Sijin Chen, Liufan Tan, Yansong Tang, Jianan Wang, Xihui Liu
https://arxiv.org/abs/2509.16063

DSPv2: Improved Dense Policy for Effective and Generalizable Whole-body Mobile Manipulation
Learning whole-body mobile manipulation via imitation is essential for generalizing robotic skills to diverse environments and complex tasks. However, this goal is hindered by significant challenges, particularly in effectively processing complex observation, achieving robust generalization, and generating coherent actions. To address these issues, we propose DSPv2, a novel policy architecture. DSPv2 introduces an effective encoding scheme that aligns 3D spatial features with multi-view 2D sema…

from my link log —
μFork: a pure actor-based concurrent machine architecture with memory-safety and object-capability security.
https://ufork.org/
saved 2025-03-22 h…

GCC: A 3DGS Inference Architecture with Gaussian-Wise and Cross-Stage Conditional Processing
Minnan Pei, Gang Li, Junwen Si, Zeyu Zhu, Zitao Mo, Peisong Wang, Zhuoran Song, Xiaoyao Liang, Jian Cheng
https://arxiv.org/abs/2507.15300

GCC: A 3DGS Inference Architecture with Gaussian-Wise and Cross-Stage Conditional Processing
3D Gaussian Splatting (3DGS) has emerged as a leading neural rendering technique for high-fidelity view synthesis, prompting the development of dedicated 3DGS accelerators for mobile applications. Through in-depth analysis, we identify two major limitations in the conventional decoupled preprocessing-rendering dataflow adopted by existing accelerators: 1) a significant portion of preprocessed Gaussians are not used in rendering, and 2) the same Gaussian gets repeatedly loaded across different t…

Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art
Dayu Fan, Rui Meng, Xiaodong Xu, Yiming Liu, Guoshun Nan, Chenyuan Feng, Shujun Han, Song Gao, Bingxuan Xu, Dusit Niyato, Tony Q. S. Quek, Ping Zhang
https://arxiv.org/abs/2507.16733

Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art
With the rapid development of Generative Artificial Intelligence (GAI) technology, Generative Diffusion Models (GDMs) have shown significant empowerment potential in the field of wireless networks due to advantages, such as noise resistance, training stability, controllability, and multimodal generation. Although there have been multiple studies focusing on GDMs for wireless networks, there is still a lack of comprehensive reviews on their technological evolution. Motivated by this, we systemat…

Laguerre geometry for optimization of gridshell with specified force distribution
Kohei Kabaki, Kentaro Hayakawa, Makoto Ohsaki
https://arxiv.org/abs/2508.15179 https://

Laguerre geometry for optimization of gridshell with specified force distribution
For the design of gridshells consisting of continuous beams in two directions and quadrilateral faces to cover a large space of architecture, it is important to arrange each row and column of beams along a planar curve and ensure planar faces for constructability and cross-sectional compatibility at joints. It is also important that the gridshell is in equilibrium mainly with axial forces against the design loads; i.e., bending deformation should be avoided. In this study, we first find a conti…

Approximate Fiber Products of Schemes and Their \'Etale Homotopical Invariants
Dongfang Zhao
https://arxiv.org/abs/2509.15616 https://arxiv.org/pdf/250…

Approximate Fiber Products of Schemes and Their Étale Homotopical Invariants
The classical fiber product in algebraic geometry provides a powerful tool for studying loci where two morphisms to a base scheme, $ϕ: X \to S$ and $ψ: Y \to S$, coincide exactly. This condition of strict equality, however, is insufficient for describing the geometric structure of semantic spaces in modern large language models, whose foundational architecture is the Transformer. The token spaces of these models are fundamentally arithmetic, and recent work has revealed complex geometric sing…

The new architecture design of the Science Alert Generation pipeline of the Cherenkov Telescope Array Observatory
ACADA Collaboration, Luca Castaldini (INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, Bologna, Italy), Andrea Bulgarelli (INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, Bologna, Italy), Vincent Pollet (Univ. Savoie Mont Blanc, CNRS, Laboratoire d'Annecy de Physique des Particules - IN2P3, Annecy, France), Gabriele Panebianco …

The new architecture design of the Science Alert Generation pipeline of the Cherenkov Telescope Array Observatory
The Cherenkov Telescope Array Observatory (CTAO) represents the next-generation gamma-ray observatory and will operate for several decades. It will be particularly suited to analyse transients and variable phenomena, which will trigger real-time scientific alerts. To support this, the Science Alert Generation (SAG) pipeline within the Array Control and Data Acquisition (ACADA) system will process data from telescope arrays in real time, using dedicated pipelines for data reconstruction (SAG-REC…

MorphoNAS: Embryogenic Neural Architecture Search Through Morphogen-Guided Development
Mykola Glybovets, Sergii Medvid
https://arxiv.org/abs/2507.13785 htt…

MorphoNAS: Embryogenic Neural Architecture Search Through Morphogen-Guided Development
While biological neural networks develop from compact genomes using relatively simple rules, modern artificial neural architecture search methods mostly involve explicit and routine manual work. In this paper, we introduce MorphoNAS (Morphogenetic Neural Architecture Search), a system able to deterministically grow neural networks through morphogenetic self-organization inspired by the Free Energy Principle, reaction-diffusion systems, and gene regulatory networks. In MorphoNAS, simple genomes …

This bridge connects the new CS building to Gates Hall which has been the home of the CS department for years
#photo #photography #buildings

Dealing with #EventualConsistency, and Causal Consistency using Predictable Identifiers
https://www.architecture-weekly.com/p/dealing-with-eventual-co…

Robust Vision-Language Models via Tensor Decomposition: A Defense Against Adversarial Attacks
Het Patel, Muzammil Allie, Qian Zhang, Jia Chen, Evangelos E. Papalexakis
https://arxiv.org/abs/2509.16163 …

Robust Vision-Language Models via Tensor Decomposition: A Defense Against Adversarial Attacks
Vision language models (VLMs) excel in multimodal understanding but are prone to adversarial attacks. Existing defenses often demand costly retraining or significant architecture changes. We introduce a lightweight defense using tensor decomposition suitable for any pre-trained VLM, requiring no retraining. By decomposing and reconstructing vision encoder representations, it filters adversarial noise while preserving meaning. Experiments with CLIP on COCO and Flickr30K show improved robustness.…

CHIMERA: Compressed Hybrid Intelligence for Twin-Model Enhanced Multi-Agent Deep Reinforcement Learning for Multi-Functional RIS-Assisted Space-Air-Ground Integrated Networks
Li-Hsiang Shen, Jyun-Jhe Huang
https://arxiv.org/abs/2507.16204

CHIMERA: Compressed Hybrid Intelligence for Twin-Model Enhanced Multi-Agent Deep Reinforcement Learning for Multi-Functional RIS-Assisted Space-Air-Ground Integrated Networks
A space-air-ground integrated network (SAGIN) architecture is proposed, empowered by multi-functional reconfigurable intelligent surfaces (MF-RIS) capable of simultaneously reflecting, amplifying, and harvesting wireless energy. The MF-RIS plays a pivotal role in addressing the energy shortages of low-Earth orbit (LEO) satellites operating in shadowed regions, while explicitly accounting for both communication and computing energy consumption across the SAGIN nodes. To maximize the long-term en…

At the 2025 RISC-V Summit in China, Nvidia says CUDA will now be compatible with RISC-V's instruction set architecture, making RISC-V a viable x86 and Arm rival (Anton Shilov/Tom's Hardware)
https://www.

Nvidia's CUDA platform now supports RISC-V — support brings open source instruction set to AI platforms, joining x86 and Arm
Yet another alternative.

Fortifying the Agentic Web: A Unified Zero-Trust Architecture Against Logic-layer Threats
Ken Huang, Yasir Mehmood, Hammad Atta, Jerry Huang, Muhammad Zeeshan Baig, Sree Bhargavi Balija
https://arxiv.org/abs/2508.12259

Fortifying the Agentic Web: A Unified Zero-Trust Architecture Against Logic-layer Threats
This paper presents a Unified Security Architecture that fortifies the Agentic Web through a Zero-Trust IAM framework. This architecture is built on a foundation of rich, verifiable agent identities using Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), with discovery managed by a protocol-agnostic Agent Name Service (ANS). Security is operationalized through a multi-layered Trust Fabric which introduces significant innovations, including Trust-Adaptive Runtime Environments (T…

Supernova: Achieving More with Less in Transformer Architectures
Andrei-Valentin Tanase, Elena Pelican
https://arxiv.org/abs/2507.15773 https://

Supernova: Achieving More with Less in Transformer Architectures
We present Supernova, a 650M-parameter decoder-only transformer that demonstrates how careful architectural design and tokenization innovation can achieve the performance of larger models while maintaining computational efficiency. Our architecture combines Rotary Positional Embeddings (RoPE), Grouped Query Attention (GQA) with a 3:1 compression ratio, RMSNorm for computational efficiency, and SwiGLU activation functions. A critical innovation is our custom 128,000-vocabulary byte-level BPE tok…

Software architecture and manual for novel versatile CT image analysis toolbox -- AnatomyArchive
Lei Xu, Torkel B Brismar
https://arxiv.org/abs/2507.13901 …

Software architecture and manual for novel versatile CT image analysis toolbox -- AnatomyArchive
We have developed a novel CT image analysis package named AnatomyArchive, built on top of the recent full body segmentation model TotalSegmentator. It provides automatic target volume selection and deselection capabilities according to user-configured anatomies for volumetric upper- and lower-bounds. It has a knowledge graph-based and time efficient tool for anatomy segmentation mask management and medical image database maintenance. AnatomyArchive enables automatic body volume cropping, as wel…

NPUEval: Optimizing NPU Kernels with LLMs and Open Source Compilers
Sarunas Kalade, Graham Schelle
https://arxiv.org/abs/2507.14403 https://

NPUEval: Optimizing NPU Kernels with LLMs and Open Source Compilers
Neural processing units (NPUs) are gaining prominence in power-sensitive devices like client devices, with AI PCs being defined by their inclusion of these specialized processors. Running AI workloads efficiently on these devices requires libraries of optimized kernels. Creating efficient kernels demands expertise in domain-specific C++ with vector intrinsics and in-depth knowledge of the target architecture. Unlike GPU programming, which has had years to mature, NPU programming is new, with sm…

Replaced article(s) found for cs.CY. https://arxiv.org/list/cs.CY/new
[1/1]:
- Using a cognitive architecture to consider antiBlackness in design and development of AI systems
Christopher L. Dancy

Beyond DNS: Unlocking the Internet of AI Agents via the NANDA Index and Verified AgentFacts
Ramesh Raskar, Pradyumna Chari, John Zinky, Mahesh Lambe, Jared James Grogan, Sichao Wang, Rajesh Ranjan, Rekha Singhal, Shailja Gupta, Robert Lincourt, Raghu Bala, Aditi Joshi, Abhishek Singh, Ayush Chopra, Dimitris Stripelis, Bhuwan B, Sumit Kumar, Maria Gorskikh

Beyond DNS: Unlocking the Internet of AI Agents via the NANDA Index and Verified AgentFacts
The Internet is poised to host billions to trillions of autonomous AI agents that negotiate, delegate, and migrate in milliseconds and workloads that will strain DNS-centred identity and discovery. In this paper, we describe the NANDA index architecture, which we envision as a means for discoverability, identifiability and authentication in the internet of AI agents. We present an architecture where a minimal lean index resolves to dynamic, cryptographically verifiable AgentFacts that supports …

Reverse Attention for Lightweight Speech Enhancement on Edge Devices
Shuubham Ojha, Felix Gervits, Carol Espy-Wilson
https://arxiv.org/abs/2509.16705 https://

Reverse Attention for Lightweight Speech Enhancement on Edge Devices
This paper introduces a lightweight deep learning model for real-time speech enhancement, designed to operate efficiently on resource-constrained devices. The proposed model leverages a compact architecture that facilitates rapid inference without compromising performance. Key contributions include infusing soft attention-based attention gates in the U-Net architecture which is known to perform well for segmentation tasks and is optimized for GPUs. Experimental evaluations demonstrate that the …

Neural Architecture Search with Mixed Bio-inspired Learning Rules
Imane Hamzaoui, Riyadh Baghdadi
https://arxiv.org/abs/2507.13485 https://

Neural Architecture Search with Mixed Bio-inspired Learning Rules
Bio-inspired neural networks are attractive for their adversarial robustness, energy frugality, and closer alignment with cortical physiology, yet they often lag behind back-propagation (BP) based models in accuracy and ability to scale. We show that allowing the use of different bio-inspired learning rules in different layers, discovered automatically by a tailored neural-architecture-search (NAS) procedure, bridges this gap. Starting from standard NAS baselines, we enlarge the search space to…

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- Efficient Adaptable High-throughput Streaming Aggregation Engines
Philippos Papaphilippou, Wayne Luk

ENSAM: an efficient foundation model for interactive segmentation of 3D medical images
Elias Stenhede, Agnar Martin Bj{\o}rnstad, Arian Ranjbar
https://arxiv.org/abs/2509.15874 …

ENSAM: an efficient foundation model for interactive segmentation of 3D medical images
We present ENSAM (Equivariant, Normalized, Segment Anything Model), a lightweight and promptable model for universal 3D medical image segmentation. ENSAM combines a SegResNet-based encoder with a prompt encoder and mask decoder in a U-Net-style architecture, using latent cross-attention, relative positional encoding, normalized attention, and the Muon optimizer for training. ENSAM is designed to achieve good performance under limited data and computational budgets, and is trained from scratch o…

Emergent Cognitive Convergence via Implementation: A Structured Loop Reflecting Four Theories of Mind (A Position Paper)
Myung Ho Kim
https://arxiv.org/abs/2507.16184

Emergent Cognitive Convergence via Implementation: A Structured Loop Reflecting Four Theories of Mind (A Position Paper)
We report the discovery of a structural convergence across four influential theories of mind: Kahneman's dual-system theory, Friston's predictive processing, Minsky's society of mind, and Clark's extended mind-emerging unintentionally within a practical AI agent architecture called Agentic Flow. Designed to address limitations in large language models (LLMs), Agentic Flow comprises five interdependent modules such as Retrieval, Cognition, Control, Memory, and Action arranged in a recurrent cogn…

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs
Ruyi Ding, Tianhong Xu, Xinyi Shen, Aidong Adam Ding, Yunsi Fei
https://arxiv.org/abs/2508.15036

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs
The transformer architecture has become a cornerstone of modern AI, fueling remarkable progress across applications in natural language processing, computer vision, and multimodal learning. As these models continue to scale explosively for performance, implementation efficiency remains a critical challenge. Mixture of Experts (MoE) architectures, selectively activating specialized subnetworks (experts), offer a unique balance between model accuracy and computational cost. However, the adaptive …

NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model
NVIDIA, :, Aarti Basant, Abhijit Khairnar, Abhijit Paithankar, Abhinav Khattar, Adi Renduchintala, Adithya Renduchintala, Aditya Malte, Akhiad Bercovich, Akshay Hazare, Alejandra Rico, Aleksander Ficek, Alex Kondratenko, Alex Shaposhnikov, Ali Taghibakhshi, Amelia Barton, Ameya Sunil Mahabaleshwarkar, Amy Shen, Andrew Tao, Ann Guan, Anna Shors, Anubhav Mandarwal, Arham Mehta, Arun Venkatesan, As…

NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model
We introduce Nemotron-Nano-9B-v2, a hybrid Mamba-Transformer language model designed to increase throughput for reasoning workloads while achieving state-of-the-art accuracy compared to similarly-sized models. Nemotron-Nano-9B-v2 builds on the Nemotron-H architecture, in which the majority of the self-attention layers in the common Transformer architecture are replaced with Mamba-2 layers, to achieve improved inference speed when generating the long thinking traces needed for reasoning. We crea…

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Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[2/2]:
- Information-acquiring von Neumann architecture of a computer: Functionality and subjectivity
Eiji Konishi

Alibaba releases Qwen3-Next, a new model architecture optimized for long-context understanding, large parameter scale, and better computational efficiency (Crystal Liu/Alizila)
https://www.alizila.com/qwen3-next-a-new-generation-of-ultra-ef…

Qwen3-Next: A New Generation of Ultra-Efficient Model Architecture Unveiled
Alibaba has launched Qwen3-Next, a brand-new model architecture optimized for long-context understanding, large parameter scale, and unprecedented

Cognitive Castes: Artificial Intelligence, Epistemic Stratification, and the Dissolution of Democratic Discourse
Craig S Wright
https://arxiv.org/abs/2507.14218

Cognitive Castes: Artificial Intelligence, Epistemic Stratification, and the Dissolution of Democratic Discourse
Artificial intelligence functions not as an epistemic leveller, but as an accelerant of cognitive stratification, entrenching and formalising informational castes within liberal-democratic societies. Synthesising formal epistemology, political theory, algorithmic architecture, and economic incentive structures, the argument traces how contemporary AI systems selectively amplify the reasoning capacity of individuals equipped with recursive abstraction, symbolic logic, and adversarial interrogati…

Crosslisted article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- Quantized Neural Networks for Microcontrollers: A Comprehensive Review of Methods, Platforms, and...
Hamza A. Abushahla, Dara Varam, Ariel J. N. Panopio, Mohamed I. AlHajri

RADE for Land Mobile Radio: A Neural Codec for Transmission of Speech over Baseband FM Radio Channels
David Rowe, Tibor Bece
https://arxiv.org/abs/2509.17286 https://

RADE for Land Mobile Radio: A Neural Codec for Transmission of Speech over Baseband FM Radio Channels
In the 1990s Land Mobile Radio (LMR) systems evolved from analog frequency modulation (FM) to standardised digital systems. Both digital and analog FM systems now co-exist in various services and exhibit similar speech quality. The architecture of many digital radios retains the analog FM modulator and demodulator from legacy analog radios, but driven by a multi-level digital pulse train rather than an analog voice signal. We denote this architecture baseband FM (BBFM). In this paper we describ…

RARE-UNet: Resolution-Aligned Routing Entry for Adaptive Medical Image Segmentation
Simon Winther Albertsen, Hjalte Svaneborg Bj{\o}rnstrup, Mostafa Mehdipour Ghazi
https://arxiv.org/abs/2507.15524

RARE-UNet: Resolution-Aligned Routing Entry for Adaptive Medical Image Segmentation
Accurate segmentation is crucial for clinical applications, but existing models often assume fixed, high-resolution inputs and degrade significantly when faced with lower-resolution data in real-world scenarios. To address this limitation, we propose RARE-UNet, a resolution-aware multi-scale segmentation architecture that dynamically adapts its inference path to the spatial resolution of the input. Central to our design are multi-scale blocks integrated at multiple encoder depths, a resolution-…

Does Audio Matter for Modern Video-LLMs and Their Benchmarks?
Geewook Kim, Minjoon Seo
https://arxiv.org/abs/2509.17901 https://arxiv.org/pdf/2509.17901

Does Audio Matter for Modern Video-LLMs and Their Benchmarks?
Modern multimodal large language models often claim "video understanding," yet most evaluations use muted videos or simply discard audio. We ask a direct question: how much does audio actually matter for contemporary Video-LLMs and the benchmarks that certify them? We audit widely used suites and observe that many items are even solvable from a single frame, rendering audio largely redundant. Building on LLaVA-OneVision architecture, we attach a speech/audio encoder (e.g., Whisper) and analyze …

Cuckoo Attack: Stealthy and Persistent Attacks Against AI-IDE
Xinpeng Liu, Junming Liu, Peiyu Liu, Han Zheng, Qinying Wang, Mathias Payer, Shouling Ji, Wenhai Wang
https://arxiv.org/abs/2509.15572

Cuckoo Attack: Stealthy and Persistent Attacks Against AI-IDE
Modern AI-powered Integrated Development Environments (AI-IDEs) are increasingly defined by an Agent-centric architecture, where an LLM-powered Agent is deeply integrated to autonomously execute complex tasks. This tight integration, however, also introduces a new and critical attack surface. Attackers can exploit these components by injecting malicious instructions into untrusted external sources, effectively hijacking the Agent to perform harmful operations beyond the user's intention or awar…

Crosslisted article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[1/1]:
- Quantum Long Short-term Memory with Differentiable Architecture Search
Samuel Yen-Chi Chen, Prayag Tiwari

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- ForestColl: Throughput-Optimal Collective Communications on Heterogeneous Network Fabrics
Zhao, Maleki, Wang, Wang, Yang, Pourreza, Krishnamurthy

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Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- HEPPO-GAE: Hardware-Efficient Proximal Policy Optimization with Generalized Advantage Estimation
Hazem Taha, Ameer M. S. Abdelhadi

Waver: Wave Your Way to Lifelike Video Generation
Yifu Zhang, Hao Yang, Yuqi Zhang, Yifei Hu, Fengda Zhu, Chuang Lin, Xiaofeng Mei, Yi Jiang, Zehuan Yuan, Bingyue Peng
https://arxiv.org/abs/2508.15761 …

Waver: Wave Your Way to Lifelike Video Generation
We present Waver, a high-performance foundation model for unified image and video generation. Waver can directly generate videos with durations ranging from 5 to 10 seconds at a native resolution of 720p, which are subsequently upscaled to 1080p. The model simultaneously supports text-to-video (T2V), image-to-video (I2V), and text-to-image (T2I) generation within a single, integrated framework. We introduce a Hybrid Stream DiT architecture to enhance modality alignment and accelerate training c…

Using Modular Arithmetic Optimized Neural Networks To Crack Affine Cryptographic Schemes Efficiently
Vanja Stojanovi\'c, \v{Z}iga Lesar, CIril Bohak
https://arxiv.org/abs/2507.14229

Using Modular Arithmetic Optimized Neural Networks To Crack Affine Cryptographic Schemes Efficiently
We investigate the cryptanalysis of affine ciphers using a hybrid neural network architecture that combines modular arithmetic-aware and statistical feature-based learning. Inspired by recent advances in interpretable neural networks for modular arithmetic and neural cryptanalysis of classical ciphers, our approach integrates a modular branch that processes raw ciphertext sequences and a statistical branch that leverages letter frequency features. Experiments on datasets derived from natural En…

Crosslisted article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Hybrid Deep Learning-Federated Learning Powered Intrusion Detection System for IoT/5G Advanced Ed...
Rasil Baidar, Sasa Maric, Robert Abbas

Universality in the Anticoncentration of Noisy Quantum Circuits at Finite Depths
Arman Sauliere, Guglielmo Lami, Corentin Boyer, Jacopo De Nardis, Andrea De Luca
https://arxiv.org/abs/2508.14975

Universality in the Anticoncentration of Noisy Quantum Circuits at Finite Depths
We study the anticoncentration properties of quantum circuits in the presence of different sources of noise. In the \textit{weak-noise regime}, we show that different types of noise act in a similar fashion, leading to a universal distribution for the probability of a given bit-string, largely independent of the specific noise channel or circuit architecture. In addition, we can identify three distinct depth-dependent regimes, each signaled by a different scaling of cross-entropy benchmarking (…

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- Per-Bank Bandwidth Regulation of Shared Last-Level Cache for Real-Time Systems
Connor Sullivan, Alex Manley, Mohammad Alian, Heechul Yun

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- JugglePAC: A Pipelined Accumulation Circuit
Ahmad Houraniah, H. Fatih Ugurdag, Furkan Aydin

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- INTA: Intent-Based Translation for Network Configuration with LLM Agents
Yunze Wei, Xiaohui Xie, Tianshuo Hu, Yiwei Zuo, Xinyi Chen, Kaiwen Chi, Yong Cui

Integrated Telecom Wavelength Heralded Single-Photon Source based on GHz gated detectors
Maria Ana Pereira, Mingsong Wu, Arslan Sajid Raja, Rui Ning Wang, Tobias Kippenberg, Hugo Zbinden, Tiff Brydges, Rob Thew
https://arxiv.org/abs/2509.16049

Integrated Telecom Wavelength Heralded Single-Photon Source based on GHz gated detectors
We introduce a simple and flexible concept for a heralded -- spectrally pure -- single photon source. The scheme uses a probabilistic photon pair source pumped with a CW laser, whereby a rapid gating InGaAs/InP single photon avalanche diode provides a synchronous clock and temporally resolves, and hence spectrally filters, the heralded photons. We demonstrate the concept by combining this with a narrow-band integrated silicon nitride photon-pair source. This simple architecture is capable of he…

GPU-Accelerated Interpretable Generalization for Rapid Cyberattack Detection and Forensics
Shu-Ting Huang, Wen-Cheng Chung, Hao-Ting Pai
https://arxiv.org/abs/2507.14222

GPU-Accelerated Interpretable Generalization for Rapid Cyberattack Detection and Forensics
The Interpretable Generalization (IG) mechanism recently published in IEEE Transactions on Information Forensics and Security delivers state-of-the-art, evidence-based intrusion detection by discovering coherent normal and attack patterns through exhaustive intersect-and-subset operations-yet its cubic-time complexity and large intermediate bitsets render full-scale datasets impractical on CPUs. We present IG-GPU, a PyTorch re-architecture that offloads all pairwise intersections and subset eva…

Crosslisted article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- CLASS: A Controller-Centric Layout Synthesizer for Dynamic Quantum Circuits
Yu Chen, Yilun Zhao, Bing Li, He Li, Mengdi Wang, Yinhe Han, Ying Wang

Crosslisted article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- B5GRoam: A Zero Trust Framework for Secure and Efficient On-Chain B5G Roaming
Rezazi, Bouchiha, Bendada, Ghamri-Doudane

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Binary VPN Traffic Detection Using Wavelet Features and Machine Learning
Yasameen Sajid Razooqi, Adrian Pekar

AutoPower: Automated Few-Shot Architecture-Level Power Modeling by Power Group Decoupling
Qijun Zhang, Yao Lu, Mengming Li, Zhiyao Xie
https://arxiv.org/abs/2508.12294 https://

AutoPower: Automated Few-Shot Architecture-Level Power Modeling by Power Group Decoupling
Power efficiency is a critical design objective in modern CPU design. Architects need a fast yet accurate architecture-level power evaluation tool to perform early-stage power estimation. However, traditional analytical architecture-level power models are inaccurate. The recently proposed machine learning (ML)-based architecture-level power model requires sufficient data from known configurations for training, making it unrealistic. In this work, we propose AutoPower targeting fully automated…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- InfiniteHBD: Building Datacenter-Scale High-Bandwidth Domain for LLM with Optical Circuit Switchi...
Shou, Liu, Nie, Meng, Zhou, Jiang, Lv, Xu, Lu, Chen, Yu, Shen, Zhu, Jiang

Replaced article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- UpANNS: Enhancing Billion-Scale ANNS Efficiency with Real-World PIM Architecture
Sitian Chen, Amelie Chi Zhou, Yucheng Shi, Yusen Li, Xin Yao

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- TrimCaching: Parameter-sharing Edge Caching for AI Model Downloading
Guanqiao Qu, Zheng Lin, Qian Chen, Jian Li, Fangming Liu, Xianhao Chen, Kaibin Huang

Crosslisted article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Holo-Artisan: A Personalized Multi-User Holographic Experience for Virtual Museums on the Edge In...
Nan-Hong Kuo, Hojjat Baghban

[2025-07-23 Wed (UTC), 6 new articles found for cs.AR Hardware Architecture]
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[2025-08-22 Fri (UTC), 4 new articles found for cs.AR Hardware Architecture]
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[2025-09-22 Mon (UTC), 1 new article found for cs.AR Hardware Architecture]
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[2025-07-22 Tue (UTC), 7 new articles found for cs.AR Hardware Architecture]
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