Tootfinder

Metropolitana VII - 🆙 🆙 🆙
城 VII - 🆙 🆙 🆙
📷 Pentax MX
🎞️ Ilford Pan 100
#filmphotography #Photography #blackandwhite

High-Rate Surgery: towards constant-overhead logical operations
Guo Zheng, Liang Jiang, Qian Xu
https://arxiv.org/abs/2510.08523 https://arxiv.org/pdf/2510…

High-Rate Surgery: towards constant-overhead logical operations
Scalable quantum computation requires not only quantum codes with low memory overhead but also encoded operations with low space-time overhead. High rate quantum low-density parity-check (qLDPC) codes address the former by achieving a high information-encoding rate, yet existing methods for implementing logical operations often suffer from a low information-processing rate, leading to substantial space-time costs. Here, we introduce high-rate surgery, a general scheme that can perform extensive…

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

Welcome back - been busy the last days with meeting friends etc 🙂
But I wanted to show some more #photos from the #Lotharsteig in the #nationalparkschwarzwald . Different from …

Is the REM the Future of Canadian Transit Expansion?
Reflections with the big REM Opening, and the idea of CanadaMetro.
Reece Martin Nov 15

Today I was shopping at www.balduccis.com and the cashier mentioned that their work is being checked by overhead cameras (unsurprising) and AI (infuriating).

One of the chores I hate most is painting. It's at least top 3. But I'm continuing work on my man cave, which eventually will contain my ##GolfSimulator. I'm hopeful that I'll finish in the next month and get serious about reducing that 20-ish handicap. New exterior double doors will be installed next week, removing the overhead garage door, and electrical work at the end of October that includes installing an electric heater.
#Golf #WinterIsComing

Noisy Neighbor: Exploiting RDMA for Resource Exhaustion Attacks in Containerized Clouds
Gunwoo Kim, Taejune Park, Jinwoo Kim
https://arxiv.org/abs/2510.12629 https://

Noisy Neighbor: Exploiting RDMA for Resource Exhaustion Attacks in Containerized Clouds
In modern containerized cloud environments, the adoption of RDMA (Remote Direct Memory Access) has expanded to reduce CPU overhead and enable high-performance data exchange. Achieving this requires strong performance isolation to ensure that one container's RDMA workload does not degrade the performance of others, thereby maintaining critical security assurances. However, existing isolation techniques are difficult to apply effectively due to the complexity of microarchitectural resource manage…

Analyzing and Internalizing Complex Policy Documents for LLM Agents
Jiateng Liu, Zhenhailong Wang, Xiaojiang Huang, Yingjie Li, Xing Fan, Xiang Li, Chenlei Guo, Ruhi Sarikaya, Heng Ji
https://arxiv.org/abs/2510.11588

Analyzing and Internalizing Complex Policy Documents for LLM Agents
Large Language Model (LLM)-based agentic systems rely on in-context policy documents encoding diverse business rules. As requirements grow, these documents expand rapidly, causing high computational overhead. This motivates developing internalization methods that embed policy documents into model priors while preserving performance. Prior prompt compression work targets generic prompts, but agentic policy documents span multiple complexity levels and require deeper reasoning, making internaliza…

QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
Wei Huang, Yi Ge, Shuai Yang, Yicheng Xiao, Huizi Mao, Yujun Lin, Hanrong Ye, Sifei Liu, Ka Chun Cheung, Hongxu Yin, Yao Lu, Xiaojuan Qi, Song Han, Yukang Chen
https://arxiv.org/abs/2510.11696

QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
We propose QeRL, a Quantization-enhanced Reinforcement Learning framework for large language models (LLMs). While RL is essential for LLMs' reasoning capabilities, it is resource-intensive, requiring substantial GPU memory and long rollout durations. QeRL addresses these issues by combining NVFP4 quantization with Low-Rank Adaptation (LoRA), accelerating rollout phase of RL while reducing memory overhead. Beyond efficiency, our findings show that quantization noise increases policy entropy, enh…

An Empirical Study of Reducing AV1 Decoder Complexity and Energy Consumption via Encoder Parameter Tuning
Vibhoothi Vibhoothi, Julien Zouein, Shanker Shreejith, Jean-Baptiste Kempf, Anil Kokaram
https://arxiv.org/abs/2510.12380

An Empirical Study of Reducing AV1 Decoder Complexity and Energy Consumption via Encoder Parameter Tuning
The widespread adoption of advanced video codecs such as AV1 is often hindered by their high decoding complexity, posing a challenge for battery-constrained devices. While encoders can be configured to produce bitstreams that are decoder-friendly, estimating the decoding complexity and energy overhead for a given video is non-trivial. In this study, we systematically analyse the impact of disabling various coding tools and adjusting coding parameters in two AV1 encoders, libaom-av1 and SVT-AV1.…

Grounded AI for Code Review: Resource-Efficient Large-Model Serving in Enterprise Pipelines
Sayan Mandal, Hua Jiang
https://arxiv.org/abs/2510.10290 https://

Grounded AI for Code Review: Resource-Efficient Large-Model Serving in Enterprise Pipelines
Automated code review adoption lags in compliance-heavy settings, where static analyzers produce high-volume, low-rationale outputs, and naive LLM use risks hallucination and incurring cost overhead. We present a production system for grounded, PR-native review that pairs static-analysis findings with AST-guided context extraction and a single-GPU, on-demand serving stack (quantized open-weight model, multi-tier caching) to deliver concise explanations and remediation guidance. Evaluated on saf…

RAID-0e: A Resilient Striping Array Architecture for Balanced Performance and Availability
Yanzhao Jia, Zhaobo Wu, Zheyi Cao, Shihao Ji, Xu Tianhao, Zihui Song
https://arxiv.org/abs/2510.12139

RAID-0e: A Resilient Striping Array Architecture for Balanced Performance and Availability
This paper introduces a novel disk array architecture, designated RAID-0e (Resilient Striping Array), designed to superimpose a low-overhead fault tolerance layer upon traditional RAID 0 (striping). By employing a logically and physically separate parity domain to protect a primary data domain, RAID-0e mitigates the risk of array-wide data loss from common, non-catastrophic media failures, such as isolated bad blocks, transient read errors, or sector-level corruption. The architecture is engine…

FedMon: Federated eBPF Monitoring for Distributed Anomaly Detection in Multi-Cluster Cloud Environments
Sehar Zehra, Hassan Jamil Syed, Ummay Faseeha
https://arxiv.org/abs/2510.10126

FedMon: Federated eBPF Monitoring for Distributed Anomaly Detection in Multi-Cluster Cloud Environments
Kubernetes multi-cluster deployments demand scalable and privacy-preserving anomaly detection. Existing eBPF-based monitors provide low-overhead system and network visibility but are limited to single clusters, while centralized approaches incur bandwidth, privacy, and heterogeneity challenges. We propose FedMon, a federated eBPF framework that unifies kernel-level telemetry with federated learning (FL) for cross-cluster anomaly detection. Lightweight eBPF agents capture syscalls and network ev…

Series C, Episode 01 - Aftermath
CALLY: Jenna's gone with Blake.
AVON: So let's get on with it. [Explosion. Avon is unconscious]
CALLY: He's out cold. We must get him into a capsule fast. And then you can put Orac in with him. Come on, quick!
https://blake.torpidity.net/m/301/14

Metropolitana VII - Markings 💮
城 VII - 印记 💮
📷 Pentax MX
🎞️ Ilford Pan 100
#filmphotography #Photography #blackandwhite

CSI Prediction Using Diffusion Models
Mehdi Sattari, Javad Aliakbari, Alexandre Graell i Amat, Tommy Svensson
https://arxiv.org/abs/2510.11214 https://arxi…

CSI Prediction Using Diffusion Models
Acquiring accurate channel state information (CSI) is critical for reliable and efficient wireless communication, but challenges such as high pilot overhead and channel aging hinder timely and accurate CSI acquisition. CSI prediction, which forecasts future CSI from historical observations, offers a promising solution. Recent deep learning approaches, including recurrent neural networks and Transformers, have achieved notable success but typically learn deterministic mappings, limiting their ab…

GrASP: A Generalizable Address-based Semantic Prefetcher for Scalable Transactional and Analytical Workloads
Farzaneh Zirak, Farhana Choudhury, Renata Borovica-Gajic
https://arxiv.org/abs/2510.11011

GrASP: A Generalizable Address-based Semantic Prefetcher for Scalable Transactional and Analytical Workloads
Data prefetching--loading data into the cache before it is requested--is essential for reducing I/O overhead and improving database performance. While traditional prefetchers focus on sequential patterns, recent learning-based approaches, especially those leveraging data semantics, achieve higher accuracy for complex access patterns. However, these methods often struggle with today's dynamic, ever-growing datasets and require frequent, timely fine-tuning. Privacy constraints may also restrict a…

Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches
Rohit Goswami (Science Institute and Faculty of Physical Sciences, University of Iceland, Reykjav\'ik, Iceland), Hannes J\'onsson (Science Institute and Faculty of Physical Sciences, University of Iceland, Reykjav\'ik, Iceland)

Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches
Gaussian process (GP) regression provides a strategy for accelerating saddle point searches on high-dimensional energy surfaces by reducing the number of times the energy and its derivatives with respect to atomic coordinates need to be evaluated. The computational overhead in the hyperparameter optimization can, however, be large and make the approach inefficient. Failures can also occur if the search ventures too far into regions that are not represented well enough by the GP model. Here, the…

Gate Teleportation vs Circuit Cutting in Distributed Quantum Computing
Shobhit Gupta, Nikolay Sheshko, Daniel J. Dilley, Alvin Gonzales, Manish K. Singh, Zain H. Saleem
https://arxiv.org/abs/2510.08894

Gate Teleportation vs Circuit Cutting in Distributed Quantum Computing
Distributing circuits across quantum processor modules will enable the execution of circuits larger than the qubit count limitations of monolithic processors. While distributed quantum computation has primarily utilized circuit cutting, it incurs an exponential growth of sub-circuit sampling and classical post-processing overhead with an increasing number of cuts. The entanglement-based gate teleportation approach does not inherently incur exponential sampling overhead, provided that quantum in…

Decentralized Multi-Robot Relative Navigation in Unknown, Structurally Constrained Environments under Limited Communication
Zihao Mao, Yunheng Wang, Yunting Ji, Yi Yang, Wenjie Song
https://arxiv.org/abs/2510.09188

Decentralized Multi-Robot Relative Navigation in Unknown, Structurally Constrained Environments under Limited Communication
Multi-robot navigation in unknown, structurally constrained, and GPS-denied environments presents a fundamental trade-off between global strategic foresight and local tactical agility, particularly under limited communication. Centralized methods achieve global optimality but suffer from high communication overhead, while distributed methods are efficient but lack the broader awareness to avoid deadlocks and topological traps. To address this, we propose a fully decentralized, hierarchical rela…

Like a rat, in a maze, the path before me lies.
#Satire ?…

All topped off by the sight of about a thousand Snow Geese passing high overhead in silvery chevrons.

"the woman and her son were chased by a quadcopter drone about one kilometre [half a mile] from the yellow line and “left there to bleed to death” as the aircraft continued hovering overhead, preventing anyone from reaching them."
Israeli drone chases and kills elderly woman in Gaza as attacks continue | Israel-Palestine conflict News | Al Jazeera
https://www.aljazeera.com/news/2025/12/6/israeli-drone-chases-and-kills-elderly-woman-in-gaza-as-attacks-continue?traffic_source=rss

Drinking my black coffee and thinking of the post @… just wrote about coffee that you should read (https://schafe-sind-bessere-rasenmaehe

Site-Specific RIS Deployment in Cellular Networks via Calibrated Ray Tracing
Sina Beyraghi, Javad Shabanpour, Giovanni Geraci, Paul Almasan, Angel Lozano
https://arxiv.org/abs/2510.09478

Site-Specific RIS Deployment in Cellular Networks via Calibrated Ray Tracing
This work introduces a fully-automated RIS deployment strategy validated through a digital twin, powered by Sionna ray tracing, of a UK city. On a scene calibrated with measured data, the method jointly optimizes RIS placement, orientation, configuration, and BS beamforming across 4G, 5G, and hypothetical 6G frequencies. Candidate RIS sites are identified via scattering-based rays, while user clustering reduces deployment overhead. Results show that meaningful coverage enhancement requires dens…

Me, a hammer salesman: “Let's fix that window, you know, for accessibility.”
[ smashes glass ]
you: “Why did you do that?”
me: “For accessibility.
you: “It wasn’t broken.”
me: “I made it better for accessibility.
you: “But how does th—”
me: “FOR ACCESSIBILITY!”
[ waggles hammer overhead ]

Which Heads Matter for Reasoning? RL-Guided KV Cache Compression
Wenjie Du, Li Jiang, Keda Tao, Xue Liu, Huan Wang
https://arxiv.org/abs/2510.08525 https://

Which Heads Matter for Reasoning? RL-Guided KV Cache Compression
Reasoning large language models exhibit complex reasoning behaviors through the extended chain-of-thought generation, creating unprecedented Key-Value (KV) cache overhead during the decoding phase. Existing KV cache compression methods underperform on reasoning models: token-dropping methods break reasoning integrity by discarding critical information, while head-reallocating methods mistakenly compress reasoning-critical heads since they are designed for retrieval tasks, resulting in significa…

Response to President Trump’s Unauthorized Use of “Danger Zone”
Late Saturday, Oct. 18, Donald Trump posted an AI-generated video on his Truth Social account that portrayed him dumping brown sludge on a crowd of protesters while wearing a crown and flying overhead in a jet labeled “King Trump.”
The music accompanying the video clip was Kenny Loggins’ hit recording “Danger Zone,” from the 1986 motion picture Top Gun (it was also used in the 2022 sequel Top Gun: Maverick).
 

Response to President Trump’s Unauthorized Use of “Danger Zone” - Kenny Loggins
Late Saturday, Oct. 18, President Donald Trump posted an AI-generated video on his Truth Social account that portrayed him dumping brown sludge on a crowd of protesters while wearing a crown and flying overhead in a jet labeled “King Trump.” The music accompanying the video clip was Kenny Loggins’ hit recording “Danger Zone,” from the …

This looks helpful: #rstats #optimization

Workout of the day.

S-D-RSM: Stochastic Distributed Regularized Splitting Method for Large-Scale Convex Optimization Problems
Maoran Wang, Xingju Cai, Yongxin Chen
https://arxiv.org/abs/2511.10133 https://arxiv.org/pdf/2511.10133 https://arxiv.org/html/2511.10133
arXiv:2511.10133v1 Announce Type: new
Abstract: This paper investigates the problems large-scale distributed composite convex optimization, with motivations from a broad range of applications, including multi-agent systems, federated learning, smart grids, wireless sensor networks, compressed sensing, and so on. Stochastic gradient descent (SGD) and its variants are commonly employed to solve such problems. However, existing algorithms often rely on vanishing step sizes, strong convexity assumptions, or entail substantial computational overhead to ensure convergence or obtain favorable complexity. To bridge the gap between theory and practice, we integrate consensus optimization and operator splitting techniques (see Problem Reformulation) to develop a novel stochastic splitting algorithm, termed the \emph{stochastic distributed regularized splitting method} (S-D-RSM). In practice, S-D-RSM performs parallel updates of proximal mappings and gradient information for only a randomly selected subset of agents at each iteration. By introducing regularization terms, it effectively mitigates consensus discrepancies among distributed nodes. In contrast to conventional stochastic methods, our theoretical analysis establishes that S-D-RSM achieves global convergence without requiring diminishing step sizes or strong convexity assumptions. Furthermore, it achieves an iteration complexity of $\mathcal{O}(1/\epsilon)$ with respect to both the objective function value and the consensus error. Numerical experiments show that S-D-RSM achieves up to 2--3$\times$ speedup compared to state-of-the-art baselines, while maintaining comparable or better accuracy. These results not only validate the algorithm's theoretical guarantees but also demonstrate its effectiveness in practical tasks such as compressed sensing and empirical risk minimization.
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A 3D Generation Framework from Cross Modality to Parameterized Primitive
Yiming Liang, Huan Yu, Zili Wang, Shuyou Zhang, Guodong Yi, Jin Wang, Jianrong Tan
https://arxiv.org/abs/2510.08656

A 3D Generation Framework from Cross Modality to Parameterized Primitive
Recent advancements in AI-driven 3D model generation have leveraged cross modality, yet generating models with smooth surfaces and minimizing storage overhead remain challenges. This paper introduces a novel multi-stage framework for generating 3D models composed of parameterized primitives, guided by textual and image inputs. In the framework, A model generation algorithm based on parameterized primitives, is proposed, which can identifies the shape features of the model constituent elements, …

LiteVPNet: A Lightweight Network for Video Encoding Control in Quality-Critical Applications
Vibhoothi Vibhoothi, Fran\c{c}ois Piti\'e, Anil Kokaram
https://arxiv.org/abs/2510.12379

LiteVPNet: A Lightweight Network for Video Encoding Control in Quality-Critical Applications
In the last decade, video workflows in the cinema production ecosystem have presented new use cases for video streaming technology. These new workflows, e.g. in On-set Virtual Production, present the challenge of requiring precise quality control and energy efficiency. Existing approaches to transcoding often fall short of these requirements, either due to a lack of quality control or computational overhead. To fill this gap, we present a lightweight neural network (LiteVPNet) for accurately pr…

Low Overhead Universal Quantum Computation with Triorthogonal Codes
Dawei Jiao, Mahdi Bayanifar, Alexei Ashikhmin, Olav Tirkkonen
https://arxiv.org/abs/2510.05708 https://

Low Overhead Universal Quantum Computation with Triorthogonal Codes
We explore the use of triorthogonal codes for universal fault-tolerant quantum computation and propose two methods to circumvent the Eastin-Knill theorem, which prohibits any single quantum error-correcting code from supporting both universality and a transversal gate set. First, we simplify the implementation of the logical Hadamard gate for triorthogonal codes by exploiting the fact that they have transversal controlled-Z (CZ) gates, resulting in a circuit with reduced overhead. Then, we intr…

Wishing C had the ability to do compile-time polymorphism.
Like, I have base class Foo with Bar/Baz derived from it.
I want to be able to put a method DoSomething() in Foo and call it on a Bar or Baz object in a template, without putting a vtable in Foo or incurring the overhead of a virtual method call.
You can sort of get this effect by having Bar/Baz be separate classes with no common base and make the template just call T.Foo() for parameter T, but this eliminates som…

Optimizing Phase-Scheduling with Throughput Trade-offs in AQFP Digital Circuits
Robert S. Aviles, Peter A. Beerel
https://arxiv.org/abs/2510.03956 https://…

Optimizing Phase-Scheduling with Throughput Trade-offs in AQFP Digital Circuits
Adiabatic Quantum-Flux-Parametron (AQFP) logic is a promising emerging superconducting technology for ultra-low power digital circuits, offering orders of magnitude lower power consumption than CMOS. However, AQFP scalability is challenged by excessive buffer overhead due to path balancing technology constraints. Addressing this, recent AQFP works have proposed design solutions to reduce path balancing overhead using phase-skipping and phase-alignment. Phase-skipping is a circuit-level techniqu…

Worked on some more #Gentoo global #jobserver goodies today.
Firstly, Portage jobserver support patch: #PyTest jobs will also be counted towards total job count.
Again, it's not a perfect solution, but it works reasonably. The plugin still starts -n jobs as specified by the arguments, but it acquired job tokens prior to executing every test, therefore delaying actual testing until tokens are available. It doesn't seem to cause noticeable overhead either.

Detection of Performance Changes in MooBench Results Using Nyrki\"o on GitHub Actions
Shinhyung Yang, David Georg Reichelt, Henrik Ingo, Wilhelm Hasselbring
https://arxiv.org/abs/2510.11310

Detection of Performance Changes in MooBench Results Using Nyrkiö on GitHub Actions
In GitHub with its 518 million hosted projects, performance changes within these projects are highly relevant to the project's users. Although performance measurement is supported by GitHub CI/CD, performance change detection is a challenging topic. In this paper, we demonstrate how we incorporated Nyrkiö to MooBench. Prior to this work, Moobench continuously ran on GitHub virtual machines, measuring overhead of tracing agents, but without change detection. By adding the upload of the measur…

Don't Just Chase "Highlighted Tokens" in MLLMs: Revisiting Visual Holistic Context Retention
Xin Zou, Di Lu, Yizhou Wang, Yibo Yan, Yuanhuiyi Lyu, Xu Zheng, Linfeng Zhang, Xuming Hu
https://arxiv.org/abs/2510.02912

Don't Just Chase "Highlighted Tokens" in MLLMs: Revisiting Visual Holistic Context Retention
Despite their powerful capabilities, Multimodal Large Language Models (MLLMs) suffer from considerable computational overhead due to their reliance on massive visual tokens. Recent studies have explored token pruning to alleviate this problem, which typically uses text-vision cross-attention or [\texttt{CLS}] attention to assess and discard redundant visual tokens. In this work, we identify a critical limitation of such attention-first pruning approaches, i.e., they tend to preserve semanticall…

A Decentralized Microservice Scheduling Approach Using Service Mesh in Cloud-Edge Systems
Yangyang Wen, Paul Townend, Per-Olov \"Ostberg, Abel Souza, Cl\'ement Courageux-Sudan
https://arxiv.org/abs/2510.11189

A Decentralized Microservice Scheduling Approach Using Service Mesh in Cloud-Edge Systems
As microservice-based systems scale across the cloud-edge continuum, traditional centralized scheduling mechanisms increasingly struggle with latency, coordination overhead, and fault tolerance. This paper presents a new architectural direction: leveraging service mesh sidecar proxies as decentralized, in-situ schedulers to enable scalable, low-latency coordination in large-scale, cloud-native environments. We propose embedding lightweight, autonomous scheduling logic into each sidecar, allowin…

Er, I think you'll find...

I'm #ActuallyAutistic, and I love this woman deeply. I have been a huge fan of her music, especially my special interest in her song “Loin d’ici” (ESC Version) since May 14, 2016.
But now, watching Taylor Swift, Cœur de Pirate, and other artists slowly take her place feels like an unspoken farewell, like a song fading softly into the distance. It’s as if she herself is tell…

FMCache: File-System Metadata Caching in Programmable Switches
Qingxiu Liu (The Chinese University of Hong Kong), Jiazhen Cai (The Chinese University of Hong Kong), Siyuan Sheng (The Chinese University of Hong Kong), Yuhui Chen (Xiamen University), Lu Tang (Xiamen University), Zhirong Shen (Xiamen University), Patrick P. C. Lee (The Chinese University of Hong Kong)

FMCache: File-System Metadata Caching in Programmable Switches
Fast and scalable metadata management across multiple metadata servers is crucial for distributed file systems to handle numerous files and directories. Client-side caching of frequently accessed metadata can mitigate server loads, but incurs significant overhead and complexity in maintaining cache consistency when the number of clients increases. We propose FMCache, an in-switch file-system metadata caching framework that leverages programmable switches to serve file-system metadata requests f…

Securing Operating Systems Through Fine-grained Kernel Access Limitation for IoT Systems
Dongyang Zhan (Harbin Institute of Technology), Zhaofeng Yu (Harbin Institute of Technology), Xiangzhan Yu (Harbin Institute of Technology), Hongli Zhang (Harbin Institute of Technology), Lin Ye (Harbin Institute of Technology), Likun Liu (Harbin Institute of Technology)
ht…

Securing Operating Systems Through Fine-grained Kernel Access Limitation for IoT Systems
With the development of Internet of Things (IoT), it is gaining a lot of attention. It is important to secure the embedded systems with low overhead. The Linux Seccomp is widely used by developers to secure the kernels by blocking the access of unused syscalls, which introduces less overhead. However, there are no systematic Seccomp configuration approaches for IoT applications without the help of developers. In addition, the existing Seccomp configuration approaches are coarse-grained, which c…

When Should Users Check? A Decision-Theoretic Model of Confirmation Frequency in Multi-Step AI Agent Tasks
Jieyu Zhou, Aryan Roy, Sneh Gupta, Daniel Weitekamp, Christopher J. MacLellan
https://arxiv.org/abs/2510.05307

When Should Users Check? A Decision-Theoretic Model of Confirmation Frequency in Multi-Step AI Agent Tasks
Existing AI agents typically execute multi-step tasks autonomously and only allow user confirmation at the end. During execution, users have little control, making the confirm-at-end approach brittle: a single error can cascade and force a complete restart. Confirming every step avoids such failures, but imposes tedious overhead. Balancing excessive interruptions against costly rollbacks remains an open challenge. We address this problem by modeling confirmation as a minimum time scheduling pro…

IntMeanFlow: Few-step Speech Generation with Integral Velocity Distillation
Wei Wang, Rong Cao, Yi Guo, Zhengyang Chen, Kuan Chen, Yuanyuan Huo
https://arxiv.org/abs/2510.07979 …

IntMeanFlow: Few-step Speech Generation with Integral Velocity Distillation
Flow-based generative models have greatly improved text-to-speech (TTS) synthesis quality, but inference speed remains limited by the iterative sampling process and multiple function evaluations (NFE). The recent MeanFlow model accelerates generation by modeling average velocity instead of instantaneous velocity. However, its direct application to TTS encounters challenges, including GPU memory overhead from Jacobian-vector products (JVP) and training instability due to self-bootstrap processes…

Good thing the Governor and the mayor are doing so much about this
#chicago @… https://

Warner Crocker (@WarnerCrocker@mastodon.social)
Attached: 1 image Dark skies in Chicago. Helicopters and drones overhead all day. #Chicago #ICE

Moody Urbanity - UP 🔝
情绪化城市 - 上面 🔝
📷 Nikon FE
🎞️ Ilford HP5 Plus 400, expired 1993
#filmphotography #Photography #blackandwhite

San Jose mayor Matt Mahan brags about "solving" the homeless problem in San Jose.
People are chased out of one area, and then they filter through neighborhoods to new spots, and the cycle repeats over and over...
#SanJose #homeless

today in "Rob Pike thinks you are literally too stupid and will personally make Go worse because of it" https://www.dolthub.com/blog/2024-12-20-collection-functions-in-go-1-23/

LRQ-Solver: A Transformer-Based Neural Operator for Fast and Accurate Solving of Large-scale 3D PDEs
Peijian Zeng, Guan Wang, Haohao Gu, Xiaoguang Hu, TiezhuGao, Zhuowei Wang, Aimin Yang, Xiaoyu Song
https://arxiv.org/abs/2510.11636

LRQ-Solver: A Transformer-Based Neural Operator for Fast and Accurate Solving of Large-scale 3D PDEs
Solving large-scale Partial Differential Equations (PDEs) on complex three-dimensional geometries represents a central challenge in scientific and engineering computing, often impeded by expensive pre-processing stages and substantial computational overhead. We introduce Low-Rank Query-based PDE Solver (LRQ-Solver), a physics-integrated framework engineered for rapid, accurate, and highly scalable simulations of industrial-grade models. This framework is built upon two primary technical innovat…

I found this an interesting view on the AI scraping and Hoovering everything disease.
This view focuses on the overhead and costs that such hoovering places on our packet carrying infrastructure.
"The hiQ Decision Legalized Infrastructure Theft - We Need a Federal Fix"
https://

The hiQ Decision Legalized Infrastructure Theft - We Need a Federal Fix
The hiQ ruling erased legal protections against commercial scraping, leaving infrastructure providers to absorb escalating costs. Without federal action defining data misappropriation, a free-rider AI economy could undermine open networks, investment, and long-term data integrity.

Simulating fermions with exponentially lower overhead
Nathan Constantinides, Jeffery Yu, Dhruv Devulapalli, Ali Fahimniya, Andrew M. Childs, Michael J. Gullans, Alexander Schuckert, Alexey V. Gorshkov
https://arxiv.org/abs/2510.05099

Simulating fermions with exponentially lower overhead
Simulating time evolution under fermionic Hamiltonians is a compelling application of quantum computers because it lies at the core of predicting the properties of materials and molecules. Fermions can be simulated on qubit-based quantum computers using a fermion-to-qubit mapping, subject to an overhead -- the circuit depth on a qubit quantum computer divided by that on a quantum computer built from native fermionic modes -- at worst scaling linearly with the number of modes $N$. Existing appro…

AILoRA: Function-Aware Asymmetric Initialization for Low-Rank Adaptation of Large Language Models
Xiaoshuang Ji, Zhendong Zhao, Xiaoyan Gu, Xiaojun Chen, Xin Zhao, Zeyao Liu
https://arxiv.org/abs/2510.08034

AILoRA: Function-Aware Asymmetric Initialization for Low-Rank Adaptation of Large Language Models
Parameter-efficient finetuning (PEFT) aims to mitigate the substantial computational and memory overhead involved in adapting large-scale pretrained models to diverse downstream tasks. Among numerous PEFT strategies, Low-Rank Adaptation (LoRA) has emerged as one of the most widely adopted approaches due to its robust empirical performance and low implementation complexity. In practical deployment, LoRA is typically applied to the $W^Q$ and $W^V$ projection matrices of self-attention modules, en…

Metropolitana VI - Asymmetry ✅
城 VI - 非对称 ✅
📷 Pentax MX
🎞️ Ilford Pan 100
#filmphotography #Photography #blackandwhite

Continuing a bit with the #photos from our #schwarzwald #vacations : (unknowingly) we walked along the Western-way. It was pretty cool because the trail was very diverse as you can see on the photo…

HTMformer: Hybrid Time and Multivariate Transformer for Time Series Forecasting
Tan Wang, Yun Wei Dong, Tao Zhang, Qi Wang
https://arxiv.org/abs/2510.07084 https://

HTMformer: Hybrid Time and Multivariate Transformer for Time Series Forecasting
Transformer-based methods have achieved impressive results in time series forecasting. However, existing Transformers still exhibit limitations in sequence modeling as they tend to overemphasize temporal dependencies. This incurs additional computational overhead without yielding corresponding performance gains. We find that the performance of Transformers is highly dependent on the embedding method used to learn effective representations. To address this issue, we extract multivariate features…

An inexact semismooth Newton-Krylov method for semilinear elliptic optimal control problem
Shiqi Chen, Xuesong Chen
https://arxiv.org/abs/2511.10058 https://arxiv.org/pdf/2511.10058 https://arxiv.org/html/2511.10058
arXiv:2511.10058v1 Announce Type: new
Abstract: An inexact semismooth Newton method has been proposed for solving semi-linear elliptic optimal control problems in this paper. This method incorporates the generalized minimal residual (GMRES) method, a type of Krylov subspace method, to solve the Newton equations and utilizes nonmonotonic line search to adjust the iteration step size. The original problem is reformulated into a nonlinear equation through variational inequality principles and discretized using a second-order finite difference scheme. By leveraging slanting differentiability, the algorithm constructs semismooth Newton directions and employs GMRES method to inexactly solve the Newton equations, significantly reducing computational overhead. A dynamic nonmonotonic line search strategy is introduced to adjust stepsizes adaptively, ensuring global convergence while overcoming local stagnation. Theoretical analysis demonstrates that the algorithm achieves superlinear convergence near optimal solutions when the residual control parameter $\eta_k$ approaches to 0. Numerical experiments validate the method's accuracy and efficiency in solving semilinear elliptic optimal control problems, corroborating theoretical insights.
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Workout of the day.

DCP: Addressing Input Dynamism In Long-Context Training via Dynamic Context Parallelism
Chenyu Jiang, Zhenkun Cai, Ye Tian, Zhen Jia, Yida Wang, Chuan Wu
https://arxiv.org/abs/2510.10620

DCP: Addressing Input Dynamism In Long-Context Training via Dynamic Context Parallelism
Context parallelism has emerged as a key technique to support long-context training, a growing trend in generative AI for modern large models. However, existing context parallel methods rely on static parallelization configurations that overlook the dynamic nature of training data, specifically, the variability in sequence lengths and token relationships (i.e., attention patterns) across samples. As a result, these methods often suffer from unnecessary communication overhead and imbalanced comp…

Differentiable Particle Optimization for Fast Sequential Manipulation
Lucas Chen, Shrutheesh Raman Iyer, Zachary Kingston
https://arxiv.org/abs/2510.07674 https://

Differentiable Particle Optimization for Fast Sequential Manipulation
Sequential robot manipulation tasks require finding collision-free trajectories that satisfy geometric constraints across multiple object interactions in potentially high-dimensional configuration spaces. Solving these problems in real-time and at large scales has remained out of reach due to computational requirements. Recently, GPU-based acceleration has shown promising results, but prior methods achieve limited performance due to CPU-GPU data transfer overhead and complex logic that prevents…

Towards Precise Channel Knowledge Map: Exploiting Environmental Information from 2D Visuals to 3D Point Clouds
Yancheng Wang, Chuan Huang, Songyang Zhang, Guanying Chen, Wei Guo, Shenglun Lan, Lexi Xu, Xinzhou Cheng, Xiongyan Tang, Shuguang Cui
https://arxiv.org/abs/2510.08140

Towards Precise Channel Knowledge Map: Exploiting Environmental Information from 2D Visuals to 3D Point Clouds
The substantial communication resources consumed by conventional pilot-based channel sounding impose an unsustainable overhead, presenting a critical scalability challenge for the future 6G networks characterized by massive channel dimensions, ultra-wide bandwidth, and dense user deployments. As a generalization of radio map, channel knowledge map (CKM) offers a paradigm shift, enabling access to location-tagged channel information without exhaustive measurements. To fully utilize the power of …

A Deep Transfer Learning-Based Low-overhead Beam Prediction in Vehicle Communications
Zhiqiang Xiao, Yuwen Cao, Mondher Bouazizi, Tomoaki Ohtsuki, Shahid Mumtaz
https://arxiv.org/abs/2509.20659

A Deep Transfer Learning-Based Low-overhead Beam Prediction in Vehicle Communications
Existing transfer learning-based beam prediction approaches primarily rely on simple fine-tuning. When there is a significant difference in data distribution between the target domain and the source domain, simple fine-tuning limits the model's performance in the target domain. To tackle this problem, we propose a transfer learning-based beam prediction method that combines fine-tuning with domain adaptation. We integrate a domain classifier into fine-tuning the pre-trained model. The model ext…

For #footpathfriday , here's a nice path I'd say.
This is taken from a bike&hike right after the bike-part and thus the start of a very nice autumn hike last year. When I took this photo, I didn't know - and not even expect how well the rest of the day would be 🙂
Enjoy
#mountains

Moody Urbanity - Passer By 🚶‍♂️
情绪化城市 - 路人 🚶‍♂️
📷 Pentax MX
🎞️ Kentmere Pan 400
#filmphotography #Photography #blackandwhite

Fundamental Costs of Noise-Robust Quantum Control: Speed Limits and Complexity
Junkai Zeng, Xiu-Hao Deng
https://arxiv.org/abs/2510.07183 https://arxiv.org…

Fundamental Costs of Noise-Robust Quantum Control: Speed Limits and Complexity
Noise is ubiquitous in quantum systems and is a major obstacle for the advancement of quantum information science. Noise-robust quantum control achieves high-fidelity operations by engineering the evolution path so that first-order noise contributions cancel at the final time. Such dynamical error correction typically incurs a time overhead beyond standard quantum speed limits. We derive general lower bounds on control complexity that quantify this overhead for quasi-static coherent noise under…

Relational Database Distillation: From Structured Tables to Condensed Graph Data
Xinyi Gao, Jingxi Zhang, Lijian Chen, Tong Chen, Lizhen Cui, Hongzhi Yin
https://arxiv.org/abs/2510.06980

Relational Database Distillation: From Structured Tables to Condensed Graph Data
Relational databases (RDBs) underpin the majority of global data management systems, where information is structured into multiple interdependent tables. To effectively use the knowledge within RDBs for predictive tasks, recent advances leverage graph representation learning to capture complex inter-table relations as multi-hop dependencies. Despite achieving state-of-the-art performance, these methods remain hindered by the prohibitive storage overhead and excessive training time, due to the m…

RTGS: Real-Time 3D Gaussian Splatting SLAM via Multi-Level Redundancy Reduction
Leshu Li (Kevin), Jiayin Qin (Kevin), Jie Peng (Kevin), Zishen Wan (Kevin), Huaizhi Qu (Kevin), Ye Han (Kevin), Pingqing Zheng (Kevin), Hongsen Zhang (Kevin), Yu (Kevin), Cao (Katie), Tianlong Chen (Katie), Yang (Katie), Zhao
https://arxiv.org/abs/2510.0664…

RTGS: Real-Time 3D Gaussian Splatting SLAM via Multi-Level Redundancy Reduction
3D Gaussian Splatting (3DGS) based Simultaneous Localization and Mapping (SLAM) systems can largely benefit from 3DGS's state-of-the-art rendering efficiency and accuracy, but have not yet been adopted in resource-constrained edge devices due to insufficient speed. Addressing this, we identify notable redundancies across the SLAM pipeline for acceleration. While conceptually straightforward, practical approaches are required to minimize the overhead associated with identifying and eliminating t…

Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection
Rui Liu, Tao Zhe, Yanjie Fu, Feng Xia, Ted Senator, Dongjie Wang
https://arxiv.org/abs/2510.05535

Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection
Feature selection eliminates redundancy among features to improve downstream task performance while reducing computational overhead. Existing methods often struggle to capture intricate feature interactions and adapt across diverse application scenarios. Recent advances employ generative intelligence to alleviate these drawbacks. However, these methods remain constrained by permutation sensitivity in embedding and reliance on convexity assumptions in gradient-based search. To address these limi…

Workout of the day.

Optimizing Privacy-Preserving Primitives to Support LLM-Scale Applications
Yaman Jandali, Ruisi Zhang, Nojan Sheybani, Farinaz Koushanfar
https://arxiv.org/abs/2509.25072 https:…

Optimizing Privacy-Preserving Primitives to Support LLM-Scale Applications
Privacy-preserving technologies have introduced a paradigm shift that allows for realizable secure computing in real-world systems. The significant barrier to the practical adoption of these primitives is the computational and communication overhead that is incurred when applied at scale. In this paper, we present an overview of our efforts to bridge the gap between this overhead and practicality for privacy-preserving learning systems using multi-party computation (MPC), zero-knowledge proofs …

Expander qLDPC Codes against Long-range Correlated Errors in Memory
Yash Deepak Kashtikar, Pranay Mathur, Sudharsan Senthil, Avhishek Chatterjee
https://arxiv.org/abs/2510.04561

Expander qLDPC Codes against Long-range Correlated Errors in Memory
Fault-tolerance using constant space-overhead against long-range correlated errors is an important practical question. In the pioneering works [Terhal and Burkard, PRA 2005], [Aliferis et al, PRA 2005], [Aharonov et al, PRL 2006], fault-tolerance using poly-logarithmic overhead against long-range correlation modeled by pairwise joint Hamiltonian was proven when the total correlation of an error at a qubit location with errors at other locations was $O(1)$, i.e., the total correlation at a locat…

Digital Twin Aided Massive MIMO CSI Feedback: Exploring the Impact of Twinning Fidelity
Hao Luo, Shuaifeng Jiang, Saeed R. Khosravirad, Ahmed Alkhateeb
https://arxiv.org/abs/2509.25793

Digital Twin Aided Massive MIMO CSI Feedback: Exploring the Impact of Twinning Fidelity
Deep learning (DL) techniques have demonstrated strong performance in compressing and reconstructing channel state information (CSI) while reducing feedback overhead in massive MIMO systems. A key challenge, however, is their reliance on extensive site-specific training data, whose real-world collection incurs significant overhead and limits scalability across deployment sites. To address this, we propose leveraging site-specific digital twins to assist the training of DL-based CSI compression …

Long weekend upcoming here in Germany!
I was able to take this photo during our #vaction in the #schwarzwald on a rainy day. I was really happy to see this kind of misty moment.
I hope you enjoy viewing it as much as I enjoyed the hike.
Some more about this day can be read here:…

AMAQ: Adaptive Mixed-bit Activation Quantization for Collaborative Parameter Efficient Fine-tuning
Yurun Song, Zhuoyi Yang, Ian G. Harris, Sangeetha Abdu Jyothi
https://arxiv.org/abs/2510.05468

AMAQ: Adaptive Mixed-bit Activation Quantization for Collaborative Parameter Efficient Fine-tuning
Large Language Models (LLMs) are scaling rapidly, creating significant challenges for collaborative server client distributed training, particularly in terms of communication efficiency and computational overheads. To address these challenges, we implement Parameter-efficient Split Learning, which effectively balances efficiency and performance for collaborative training on low-resource devices. To reduce communication overhead in collaborative training, we introduce Adaptive Mixed bit Activa…

Moody Urbanity - Oracles III 🈳
情绪化城市 - 谕 III 🈳
📷 Nikon FE
🎞️ Ilford HP5 Plus 400, expired 1993
#filmphotography #Photography #blackandwhite

NanoTag: Systems Support for Efficient Byte-Granular Overflow Detection on ARM MTE
Mingkai Li, Hang Ye, Joseph Devietti, Suman Jana, Tanvir Ahmed Khan
https://arxiv.org/abs/2509.22027

NanoTag: Systems Support for Efficient Byte-Granular Overflow Detection on ARM MTE
Memory safety bugs, such as buffer overflows and use-after-frees, are the leading causes of software safety issues in production. Software-based approaches, e.g., Address Sanitizer (ASAN), can detect such bugs with high precision, but with prohibitively high overhead. ARM's Memory Tagging Extension (MTE) offers a promising alternative to detect these bugs in hardware with a much lower overhead. However, in this paper, we perform a thorough investigation of Google Pixel 8, the first production i…

Channel Charting based Fast Beam Tracking Design and Implementation
Jiawei Zhang, Shihan Wang, Jienan Chen, Fan Wu, Jiyun Tao, Zheqi Gu
https://arxiv.org/abs/2510.08144 https://…

Channel Charting based Fast Beam Tracking Design and Implementation
In the beyond fifth-generation (B5G) and upcoming sixth-generation (6G) wireless communication systems, millimeter (mmWave) wave technology is a promising solution for offering additional bandwidth resources and mitigating spectrum congestion. Beam tracking is an essential procedure for providing reliable communication services in the mmWave communication system, with the challenge of providing consistent and accurate tracking performance. In this study, we introduce a low-overhead beam trackin…

Hey all. Just wanted to post the other two #photos from my recent blog post https://www.franzgraf.de/blog/2025/a-familiar-mountain-never-gets-old/
It's one o…

Metropolitana IV 🏯
城 IV 🏯
📷 Pentax MX
🎞️ Ilford Pan 100
#filmphotography #Photography #blackandwhite

Orders in Chaos: Enhancing Large-Scale MoE LLM Serving with Data Movement Forecasting
Zhongkai Yu, Yue Guan, Zihao Yu, Chenyang Zhou, Shuyi Pei, Yangwook Kang, Yufei Ding, Po-An Tsai
https://arxiv.org/abs/2510.05497

Orders in Chaos: Enhancing Large-Scale MoE LLM Serving with Data Movement Forecasting
Large Language Models (LLMs) with Mixture of Experts (MoE) architectures achieve remarkable performance improvements, but their random expert selection mechanism introduces significant data movement overhead that becomes the dominant bottleneck in multi-unit serving systems. To forecast the patterns underlying this data movement, we conduct comprehensive data-movement-centric profiling across three state-of-the-art large-scale MoE models (200B- 671B) using over 24,000 requests spanning diverse …

AdaBet: Gradient-free Layer Selection for Efficient Training of Deep Neural Networks
Irene Tenison, Soumyajit Chatterjee, Fahim Kawsar, Mohammad Malekzadeh
https://arxiv.org/abs/2510.03101

AdaBet: Gradient-free Layer Selection for Efficient Training of Deep Neural Networks
To utilize pre-trained neural networks on edge and mobile devices, we often require efficient adaptation to user-specific runtime data distributions while operating under limited compute and memory resources. On-device retraining with a target dataset can facilitate such adaptations; however, it remains impractical due to the increasing depth of modern neural nets, as well as the computational overhead associated with gradient-based optimization across all layers. Current approaches reduce trai…

CryptOracle: A Modular Framework to Characterize Fully Homomorphic Encryption
Cory Brynds, Parker McLeod, Lauren Caccamise, Asmita Pal, Dewan Saiham, Sazadur Rahman, Joshua San Miguel, Di Wu
https://arxiv.org/abs/2510.03565

CryptOracle: A Modular Framework to Characterize Fully Homomorphic Encryption
Privacy-preserving machine learning has become an important long-term pursuit in this era of artificial intelligence (AI). Fully Homomorphic Encryption (FHE) is a uniquely promising solution, offering provable privacy and security guarantees. Unfortunately, computational cost is impeding its mass adoption. Modern solutions are up to six orders of magnitude slower than plaintext execution. Understanding and reducing this overhead is essential to the advancement of FHE, particularly as the underl…

Sensing Management for Pilot-Free Predictive Beamforming in Cell-Free Massive MIMO Systems
Eren Berk Kama, Murat Babek Salman, Isaac Skog, Emil Bj\"ornson
https://arxiv.org/abs/2510.06936

Sensing Management for Pilot-Free Predictive Beamforming in Cell-Free Massive MIMO Systems
This paper introduces a sensing management method for integrated sensing and communications (ISAC) in cell-free massive multiple-input multiple-output (MIMO) systems. Conventional communication systems employ channel estimation procedures that impose significant overhead during data transmission, consuming resources that could otherwise be utilized for data. To address this challenge, we propose a state-based approach that leverages sensing capabilities to track the user when there is no commun…

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

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

Transversal dimension jump for product qLDPC codes
Christine Li, John Preskill, Qian Xu
https://arxiv.org/abs/2510.07269 https://arxiv.org/pdf/2510.07269…

Transversal dimension jump for product qLDPC codes
We introduce transversal dimension jump, a code-switching protocol for lifted product (LP) quantum low-density parity-check (qLDPC) codes across different chain-complex dimensions, enabling universal fault-tolerant quantum computation with low overhead. The construction leverages the product structure of LP codes to implement one-way transversal CNOTs between a 3D code and its 2D component codes, enabling teleportation-based switching. Combined with constant-depth CCZ gates in 3D LP codes and l…

Metropolitana II🔶
城 II🔶
📷 Pentax MX
🎞️ Ilford Pan 100
#filmphotography #Photography #blackandwhite

Batched high-rate logical operations for quantum LDPC codes
Qian Xu, Hengyun Zhou, Dolev Bluvstein, Madelyn Cain, Marcin Kalinowski, John Preskill, Mikhail D. Lukin, Nishad Maskara
https://arxiv.org/abs/2510.06159

Batched high-rate logical operations for quantum LDPC codes
High-rate quantum LDPC (qLDPC) codes reduce memory overhead by densely packing many logical qubits into a single block of physical qubits. Here we extend this concept to high-rate computation by constructing \emph{batched} fault-tolerant operations that apply the same logical gate across many code blocks in parallel. By leveraging shared physical resources to execute many logical operations in parallel, these operations realize high rates in space-time and significantly reduce computational cos…

KaVa: Latent Reasoning via Compressed KV-Cache Distillation
Anna Kuzina, Maciej Pioro, Paul N. Whatmough, Babak Ehteshami Bejnordi
https://arxiv.org/abs/2510.02312 https://

KaVa: Latent Reasoning via Compressed KV-Cache Distillation
Large Language Models (LLMs) excel at multi-step reasoning problems with explicit chain-of-thought (CoT), but verbose traces incur significant computational costs and memory overhead, and often carry redundant, stylistic artifacts. Latent reasoning has emerged as an efficient alternative that internalizes the thought process, but it suffers from a critical lack of supervision, limiting its effectiveness on complex, natural-language reasoning traces. In this work, we propose KaVa, the first fram…

Model-based Deep Learning for Joint RIS Phase Shift Compression and WMMSE Beamforming
Alexander James Fernandes, Ioannis Psaromiligkos
https://arxiv.org/abs/2510.05438 https://

Model-based Deep Learning for Joint RIS Phase Shift Compression and WMMSE Beamforming
A model-based deep learning (DL) architecture is proposed for reconfigurable intelligent surface (RIS)-assisted multi-user communications to reduce the overhead of transmitting phase shift information from the access point (AP) to the RIS controller. The phase shifts are computed at the AP, which has access to the channel state information, and then encoded into a compressed binary control message that is sent to the RIS controller for element configuration. To help reduce beamformer mismatches…

Moody Urbanity - Relations III 🧶
情绪化城市 - 关系 III 🧶
📷 Minolta Hi-Matic AF
🎞️ Shanghai GP3 400 Pan
#filmphotography #Photography #blackandwhite

Efficient Post-Selection for General Quantum LDPC Codes
Seok-Hyung Lee, Lucas English, Stephen D. Bartlett
https://arxiv.org/abs/2510.05795 https://arxiv.o…

Efficient Post-Selection for General Quantum LDPC Codes
Post-selection strategies that discard low-confidence computational results can significantly improve the effective fidelity of quantum error correction at the cost of reduced acceptance rates, which can be particularly useful for offline resource state generation. Prior work has primarily relied on the "logical gap" metric with the minimum-weight perfect matching decoder, but this approach faces fundamental limitations including computational overhead that scales exponentially with the number …

Urbanity - Cloud Four ☁️
城市化 - 四云 ☁️
📷 Pentax MX
🎞️ Ilford Pan 100
#filmphotography #Photography #blackandwhite

Embedding-Aware Noise Modeling of Quantum Annealing
Seon-Geun Jeong, Mai Dinh Cong, Dae-Il Noh, Quoc-Viet Pham, Won-Joo Hwang
https://arxiv.org/abs/2510.04594 https://

Embedding-Aware Noise Modeling of Quantum Annealing
Quantum annealing provides a practical realization of adiabatic quantum computation and has emerged as a promising approach for solving large-scale combinatorial optimization problems. However, current devices remain constrained by sparse hardware connectivity, which requires embedding logical variables into chains of physical qubits. This embedding overhead limits scalability and reduces reliability as longer chains are more prone to noise-induced errors. In this work, building on the known st…

Moody Urbanity - Relations 👥
情绪化城市 - 关系 👥
📷 Minolta Hi-Matic AF
🎞️ Shanghai GP3 400 Pan
#filmphotography #Photography #blackandwhite

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

Moody Urbanity - Action Required 🧭
情绪化城市 - 行动指南 🧭
📷 Pentax MX
🎞️ Ilford HP5 Plus 400, expired 1993
#filmphotography #Photography #blackandwhite

Moody Urbanity - Up ☁️
情绪化城市 - 上面 ☁️
📷 Nikon FE
🎞️ Lucky SHD 400
#filmphotography #Photography #blackandwhite

Moody Urbanity - Somewhere 🔲
情绪化城市 - 某处 🔲
📷 Nikon FE
🎞️ Lucky SHD 400
#filmphotography #Photography #blackandwhite