Tootfinder

Co-TAP: Three-Layer Agent Interaction Protocol Technical Report
Shunyu An, Miao Wang, Yongchao Li, Dong Wan, Lina Wang, Ling Qin, Liqin Gao, Congyao Fan, Zhiyong Mao, Jiange Pu, Wenji Xia, Dong Zhao, Rui Hu, Ji Lu, Guiyue Zhou, Baoyu Tang, Yanqin Gao, Yongsheng Du, Daigang Xu, Lingjun Huang, Baoli Wang, Xiwen Zhang, Luyao Wang, Shilong Liu
https://

Co-TAP: Three-Layer Agent Interaction Protocol Technical Report
This paper proposes Co-TAP (T: Triple, A: Agent, P: Protocol), a three-layer agent interaction protocol designed to address the challenges faced by multi-agent systems across the three core dimensions of Interoperability, Interaction and Collaboration, and Knowledge Sharing. We have designed and proposed a layered solution composed of three core protocols: the Human-Agent Interaction Protocol (HAI), the Unified Agent Protocol (UAP), and the Memory-Extraction-Knowledge Protocol (MEK). HAI focuse…

Single layer tiny Co$^4$ outpaces GPT-2 and GPT-BERT
Noor Ul Zain, Mohsin Raza, Ahsan Adeel
https://arxiv.org/abs/2510.08404 https://arxiv.org/pdf/2510.084…

Single layer tiny Co$^4$ outpaces GPT-2 and GPT-BERT
We show that a tiny Co$^4$ machine(Adeel,2025) with a single layer, two heads, and 8M parameters, operating at an approximate cost of $O(N)$ (where $N$ is the number of input tokens), outpaces the BabyLM Challenge baselines GPT-2 (124M, 12 layers, $O(N^2))$ and GPT-BERT (30M, 12 layers, $O(N^2))$ in just two epochs, while both are trained for ten. Co$^4$ achieves orders-of-magnitude greater training efficiency on 10M tokens, demonstrating highly sample efficient pretraining. Using the BabyLM ch…

Enhanced SegNet with Integrated Grad-CAM for Interpretable Retinal Layer Segmentation in OCT Images
S M Asiful Islam Saky, Ugyen Tshering
https://arxiv.org/abs/2509.07795 https:…

Enhanced SegNet with Integrated Grad-CAM for Interpretable Retinal Layer Segmentation in OCT Images
Optical Coherence Tomography (OCT) is essential for diagnosing conditions such as glaucoma, diabetic retinopathy, and age-related macular degeneration. Accurate retinal layer segmentation enables quantitative biomarkers critical for clinical decision-making, but manual segmentation is time-consuming and variable, while conventional deep learning models often lack interpretability. This work proposes an improved SegNet-based deep learning framework for automated and interpretable retinal layer s…

Layer-dependent Charge Transfer and inter-layer coupling in WSe2/Graphene Heterostructures
Lan Huang, Laric Bobzien, \'Angel Labordet \'Alvarez, Daniel E. Cintron Figueroa, Li-Syuan Lu, Chengye Dong, Joshua A. Robinson, Bruno Schuler, Mirjana Dimitrievska
https://arxiv.org/abs/2509.08340

Layer-dependent Charge Transfer and inter-layer coupling in WSe2/Graphene Heterostructures
Understanding interfacial interactions in two-dimensional (2D) heterostructures is essential for advancing optoelectronic and quantum technologies. We investigate metal-organic chemical vapor deposition (MOCVD)-grown WSe$_2$ films (one to five layers) on graphene/SiC, directly compared to exfoliated WSe$_2$ on SiO$_2$, using Raman and photoluminescence (PL) spectroscopy complemented by atomic force microscopy (AFM). Raman measurements reveal compressive strain and interfacial charge transfer in…

Gravity drives the flow within the Stewartson layer in centrifugal convection
Rushi Lai, Jun Zhong, Chao Sun
https://arxiv.org/abs/2509.07550 https://arxiv…

Gravity drives the flow within the Stewartson layer in centrifugal convection
We conduct three-dimensional numerical simulations on centrifugal convection (CC) in a closed annular container, incorporating gravity and no-slip top and bottom boundaries, to systematically investigate rotation-induced secondary flow. The Stewartson layer, identified by an elongated circulation in mean vertical velocity plots, emerges near the inner and outer cylinders only beyond a critical gravitational forcing. Quantitative analyses confirm that the layer thickness scales as $δ_{st}\sim E…

🥡 3D printed food: Yuck or yes? Researchers ask South African consumers
#food

3D printed food: Yuck or yes? Researchers ask South African consumers
Would you eat food that was printed by a machine? 3D printed food is built up by equipment (a 3D food printer), layer after layer, using edible pastes, dough and food slurries in three-dimensional forms. These machines use digital models to produce precise, often personalized food items. Most 3D printed foods are made from nutrient-dense sources (plant and animal), which means they can offer health benefits.

Compressing CNN models for resource-constrained systems by channel and layer pruning
Ahmed Sadaqa, Di Liu
https://arxiv.org/abs/2509.08714 https://arxiv.or…

Compressing CNN models for resource-constrained systems by channel and layer pruning
Convolutional Neural Networks (CNNs) have achieved significant breakthroughs in various fields. However, these advancements have led to a substantial increase in the complexity and size of these networks. This poses a challenge when deploying large and complex networks on edge devices. Consequently, model compression has emerged as a research field aimed at reducing the size and complexity of CNNs. One prominent technique in model compression is model pruning. This paper will present a new tech…

D-LEAF: Localizing and Correcting Hallucinations in Multimodal LLMs via Layer-to-head Attention Diagnostics
Tiancheng Yang, Lin Zhang, Jiaye Lin, Guimin Hu, Di Wang, Lijie Hu
https://arxiv.org/abs/2509.07864

D-LEAF: Localizing and Correcting Hallucinations in Multimodal LLMs via Layer-to-head Attention Diagnostics
Multimodal Large Language Models (MLLMs) achieve strong performance on tasks like image captioning and visual question answering, but remain prone to hallucinations, where generated text conflicts with the visual input. Prior work links this partly to insufficient visual attention, but existing attention-based detectors and mitigation typically apply uniform adjustments across layers and heads, obscuring where errors originate. In this paper, we first show these methods fail to accurately local…

🇺🇦 #NowPlaying on KEXP's #MiddayShow
The Smashing Pumpkins:
🎵 1979
#TheSmashingPumpkins
https://soundwavesofficial.bandcamp.com/album/the-smashing-pumpkins-1979-layer-by-layer
https://open.spotify.com/track/3h5zik31hTTat9jmpCZZNC

Koi, which is developing a centralized protection layer for enterprise endpoints, raised a $38M Series A, taking its total funding to $48M (Meir Orbach/CTech)
https://www.calcalistech.com/ctechnews/article/syoe1xjslx

Cyber startup Koi raises $38M Series A to build a central checkpoint for enterprise software
The startup, founded by Unit 8200 veterans, was born from a VSCode hack and aims to give enterprises control over every application installed on their endpoints.

Tonight, we're not just printing—we're building a fleet! Firing up the 3D printer to create a massive Wasp-class amphibious assault ship (LHD). Get ready to see this miniature marvel come to life, layer by layer! #3DPrinting #WASPClass

Twisted bi-layer magnetic photonic crystals
You-Ming Liu, Shi-Kai Lin, Pei-Shi Li, Yi-Ran Hao, Biao Yang
https://arxiv.org/abs/2510.07714 https://arxiv.org…

Twisted bi-layer magnetic photonic crystals
In photonics, twisted bi-layer systems have demonstrated unprecedented control over light-matter interactions, primarily through the modulation of photonic band structures and the formation of Moiré patterns. Meanwhile, magnetic photonic crystals have served as cornerstone platforms for manipulating light propagation, facilitating key applications such as Faraday rotation-based isolators and non-reciprocal devices. Nevertheless, the synergistic integration of twist engineering and magneto-opti…

Saturday AM, a story in two acts...
(Incl. placing crumbs by approaching it like a #PoissonDisk / #BlueNoise problem... 😅)
#CakeDay

Gate Voltage Tunable Second Harmonic Generation in Mono- and Bi-layer Black Phosphene
Yan Meng, Kainan Chang, Yanyan Qian, Luxia Wang, Jin Luo Cheng
https://arxiv.org/abs/2510.08014

Gate Voltage Tunable Second Harmonic Generation in Mono- and Bi-layer Black Phosphene
Black phosphorene (BP) has emerged as a promising platform for tunable nonlinear photonics due to its layer-dependent bandgap, high carrier mobility, and remarkable in-plane anisotropy. This study investigates the second-harmonic generation (SHG) of monolayer and bilayer BP under an external static electric field, with describing the electronic states by a tight-binding model and the dynamics by semiconductor Bloch equations. Our results reveal that BP exhibits large second-order nonlinear opti…

Checking on the Ceres FPGA board sims for @… that I had running while I was away. We're looking at a PCIe differential pair layer transition plus AC coupling capacitor.
With no cutout (green trace) response is awful: -3 dB insertion loss at 9.1 GHz, and -10 dB return loss at only 2.8 GHz.
Adding the cutout in the gerbers (blue trace) ma…

Node Position Estimation in Diffusion-Based Molecular Communications Using Multi-Layer Perceptron
Sangjun Hwang, Chan-Byoung Chae
https://arxiv.org/abs/2509.07441 https://

Node Position Estimation in Diffusion-Based Molecular Communications Using Multi-Layer Perceptron
This paper proposes a method for accurately estimating the relative position between two nodes with unknown locations in a diffusion-based molecular communication environment. A specialized node structure is designed, combining a central absorbing receiver with multiple transmitters placed at predefined spherical coordinates. Pilot molecules are released, and their absorption time and concentration are measured. By partitioning the spherical coordinate space, these spatially distinct measuremen…

URLLC for 6G Enabled Industry 5.0: A Taxonomy of Architectures, Cross Layer Techniques, and Time Critical Applications
Abdikarim Mohamed Ibrahim, Rosdiadee Nordin, Yahya S. M. Khamayseh, Angela Amphawan, Muhammed Basheer Jasser
https://arxiv.org/abs/2510.08080

URLLC for 6G Enabled Industry 5.0: A Taxonomy of Architectures, Cross Layer Techniques, and Time Critical Applications
The evolution from Industry 4.0 to Industry 5.0 introduces stringent requirements for ultra reliable low latency communication (URLLC) to support human centric, intelligent, and resilient industrial systems. Sixth-generation (6G) wireless networks aim to meet these requirements through sub-millisecond end-to-end delays, microsecond level jitter, and near perfect reliability, enabled by advances such as terahertz (THz) communication, reconfigurable intelligent surfaces (RIS), multi-access edge c…

Unstable mode around the 3D boundary layer flow
Cheng-Jie Liu, Mengjun Ma, Di Wu, Zhu Zhang
https://arxiv.org/abs/2509.06089 https://arxiv.org/pdf/2509.060…

Unstable mode around the 3D boundary layer flow
We study the stability properties of boundary layer-type shear flows for the three-dimensional Navier-Stokes equations in the limit of small viscosity $0<ν\ll 1$. When the streamwise and spanwise velocity profiles are linearly independent near the boundary, we construct an unstable mode that exhibits rapid growth at the rate of $e^{t/\sqrtν}$. Our results reveal an analytic instability in the three-dimensional Navier-Stokes equations around generic boundary layer profiles. This instability ar…

Unified Graph-Theoretic Modeling of Multi-Energy Flows in Distribution Systems
Marwan Mostafa, Daniel Wenser, Payam Teimourzadeh Baboli, Christian Becker
https://arxiv.org/abs/2509.06447

Unified Graph-Theoretic Modeling of Multi-Energy Flows in Distribution Systems
The increasing complexity of energy systems due to sector coupling and decarbonization calls for unified modeling frameworks that capture the physical and structural interactions between electricity, gas, and heat networks. This paper presents a graph-based modeling approach for multi-energy systems, where each domain is represented as a layer in a multi-layer graph, and coupling technologies are modeled as inter-layer edges via a dedicated coupling layer. A steady-state solver based on a block…

Goodness-of-fit test for multi-layer stochastic block models
Huan Qing
https://arxiv.org/abs/2508.04957 https://arxiv.org/pdf/2508.04957

Goodness-of-fit test for multi-layer stochastic block models
Community detection in multi-layer networks is a fundamental task in complex network analysis across various areas like social, biological, and computer sciences. However, most existing algorithms assume that the number of communities is known in advance, which is usually impractical for real-world multi-layer networks. To address this limitation, we develop a novel goodness-of-fit test for the popular multi-layer stochastic block model. The test statistic is derived from a normalized aggregati…

B06 - Trial
BLAKE: I never doubted it for a moment. [To Avon] Is something wrong?
AVON: It just occurred to me, that as the description of a highly sophisticated technological achievement, "Avon's gadget works," seems to lack a certain style.
https://blake.torpidity.net/m/206/472

Crown, Frame, Reverse: Layer-Wise Scaling Variants for LLM Pre-Training
Andrei Baroian, Kasper Notebomer
https://arxiv.org/abs/2509.06518 https://arxiv.org…

Crown, Frame, Reverse: Layer-Wise Scaling Variants for LLM Pre-Training
Transformer-based language models traditionally use uniform (isotropic) layer sizes, yet they ignore the diverse functional roles that different depths can play and their computational capacity needs. Building on Layer-Wise Scaling (LWS) and pruning literature, we introduce three new LWS variants - Framed, Reverse, and Crown - that redistribute FFN widths and attention heads via two or three-point linear interpolation in the pre-training stage. We present the first systematic ablation of LWS an…

Design of high-strength, radiopure, electroformed copper-based alloys for rare-event searches: Impact of layer configuration on heat treatments
D. Spathara, P. Knights, K. Nikolopoulos
https://arxiv.org/abs/2509.07406

Design of high-strength, radiopure, electroformed copper-based alloys for rare-event searches: Impact of layer configuration on heat treatments
State-of-the-art and next-generation rare-event search experiments rely on detector materials with stringent requirements on radiopurity and mechanical performance. Additive-free electroformed copper offers exceptional radiopurity, but is limited in mechanical strength, motivating the exploration of application-specific copper-based alloys. Early investigations, based on direct experimentation, explored the synthesis of CuCr alloys through electrodeposition and thermal processing. Subsequently,…

Sparse Transformer for Ultra-sparse Sampled Video Compressive Sensing
Miao Cao, Siming Zheng, Lishun Wang, Ziyang Chen, David Brady, Xin Yuan
https://arxiv.org/abs/2509.08228 ht…

Sparse Transformer for Ultra-sparse Sampled Video Compressive Sensing
Digital cameras consume ~0.1 microjoule per pixel to capture and encode video, resulting in a power usage of ~20W for a 4K sensor operating at 30 fps. Imagining gigapixel cameras operating at 100-1000 fps, the current processing model is unsustainable. To address this, physical layer compressive measurement has been proposed to reduce power consumption per pixel by 10-100X. Video Snapshot Compressive Imaging (SCI) introduces high frequency modulation in the optical sensor layer to increase effe…

Lighthill's mechanism and vorticity cascade in the logarithmic layer of wall turbulence
Samvit Kumar, Simon Toedtli, Tamer A. Zaki, Gregory Eyink
https://arxiv.org/abs/2509.07746

Lighthill's mechanism and vorticity cascade in the logarithmic layer of wall turbulence
We investigate Lighthill's proposed turbulent mechanism for near-wall concentration of spanwise vorticity by calculating mean flows conditioned on motion away from or toward the wall in an $Re_τ=1000$ database of plane-parallel channel flow. Our results corroborate Lighthill's proposal throughout the entire logarithmic layer, but extended by counterflows that help explain anti-correlation of vorticity transport by advection and by stretching/tilting. We present evidence also for Lighthill's hy…

Weakly superconducting anisotropy in 4Hb-Nb0.95Ti0.05Se2 with 1T/1H heterostructure
Fanyu Meng, Hechang Lei
https://arxiv.org/abs/2509.08716 https://arxiv.…

Weakly superconducting anisotropy in 4Hb-Nb0.95Ti0.05Se2 with 1T/1H heterostructure
Heterostructures of layered transition metal dichalcogenide (TMD) exhibit rich physical properties by the combination of strong electronic correlation effects in 1T layer and superconductivity in 1H layer. But the limited number of bulk TMD materials with such heterostructures impedes the in-depth understanding of the physical mechanisms behind these properties, as well as research on tuning of these properties. Here, we report a systematic study on physical properties of the 4Hb-Nb0.95Ti0.05Se…

Secure Hybrid Key Growing via Coherence Witnessing and Bipartite Encoding
Pol Juli\`a Farr\'e, Chris Aaron Schneider, Christian Deppe
https://arxiv.org/abs/2508.06294 https:…

Secure Hybrid Key Growing via Coherence Witnessing and Bipartite Encoding
We propose a novel Hybrid Key Growing (HKG) protocol based on quantum principles and a classical physical-layer assumption. We simultaneously exploit the quantum photon-number and photon-time-bin Degrees of Freedom (DoFs), effectively doubling the bit-per-pulse rate compared to conventional Quantum Key Growing (QKG) schemes. Our protocol integrates entity authentication, and is designed for practical implementation by avoiding reliance on single-photon sources or detectors. By incorporating pri…

paris_transportation: Paris Transportation (2016)
A multiplex transportation network of Ile-de-France, jointly representing the road network, train network, and subway network. Nodes are intersections, rail stations or metro stations. Different modes of transportation are given by different layers. Within-layer edges represent direct transference by a particular mode of transportation, and between-layer edges represent the conjunction of those layers at that node.
This network ha…

Accelerating Diffusion Transformer-Based Text-to-Speech with Transformer Layer Caching
Siratish Sakpiboonchit
https://arxiv.org/abs/2509.08696 https://arxi…

Accelerating Diffusion Transformer-Based Text-to-Speech with Transformer Layer Caching
This paper presents a method to accelerate the inference process of diffusion transformer (DiT)-based text-to-speech (TTS) models by applying a selective caching mechanism to transformer layers. Specifically, I integrate SmoothCache into the F5-TTS architecture, focusing on caching outputs of self-attention and feed-forward network layers to reduce redundant computations during the denoising process. A calibration phase is introduced to analyze L1 relative errors between timesteps, guiding the …

Embedding Poisoning: Bypassing Safety Alignment via Embedding Semantic Shift
Shuai Yuan, Zhibo Zhang, Yuxi Li, Guangdong Bai, Wang Kailong
https://arxiv.org/abs/2509.06338 https…

Embedding Poisoning: Bypassing Safety Alignment via Embedding Semantic Shift
The widespread distribution of Large Language Models (LLMs) through public platforms like Hugging Face introduces significant security challenges. While these platforms perform basic security scans, they often fail to detect subtle manipulations within the embedding layer. This work identifies a novel class of deployment phase attacks that exploit this vulnerability by injecting imperceptible perturbations directly into the embedding layer outputs without modifying model weights or input text. …

An HPC-Inspired Blueprint for a Technology-Agnostic Quantum Middle Layer
Stefano Markidis, Gilbert Netzer, Luca Pennati, Ivy Peng
https://arxiv.org/abs/2510.07079 https://

An HPC-Inspired Blueprint for a Technology-Agnostic Quantum Middle Layer
We present a blueprint for a quantum middle layer that supports applications across various quantum technologies. Inspired by concepts and abstractions from HPC libraries and middleware, our design is backend-neutral and context-aware. A program only needs to specify its intent once as typed data and operator descriptors. It declares what the quantum registers mean and which logical transformations are required, without committing to gates, pulses, continuous-variable routines, or anneal backen…

Short-Term Gaze Prediction: Analysis of Individual Differences, Typical and Extreme-Case Errors
Kateryna Melnyk, Lee Friedman, Oleg Komogortsev
https://arxiv.org/abs/2509.07126 …

Short-Term Gaze Prediction: Analysis of Individual Differences, Typical and Extreme-Case Errors
Gaze prediction is a diverse field of study with multiple research focuses and practical applications. This article investigates how recurrent neural networks and transformers perform short-term gaze prediction. We used three models: a three-layer long-short-term memory (LSTM) network, a simple transformer-encoder model (TF), and a classification-predictor network (ClPr), which simultaneously classifies the signal into eye movement events and predicts the positions of gaze. The performance of t…

"Ozone layer recovery shows what global cooperation can achieve"
#OzoneLayer #Emissions

Ozone layer recovery shows what global cooperation can achieve | The Optimist Daily
The UN’s latest ozone bulletin finds steady recovery under the Montreal Protocol, a proof point that science-based policy can repair the planet.

Anomalies in #space: A silent risk to critical infrastructure
30 July 2025
"The Earth’s #ionosphere an electrified layer of the upper atmosphere, is a vital part of our space environment. It enables or interferes with many of the technologies we rely on daily, such as …

“We are definitely concerned about them and have taken efforts to protect those trees.”
Smoke jumpers have been requested to help put out the fires burning high in the trees.
Firefighters, meanwhile, placed sprinklers in key areas within the McKinley Grove to at least minimize damage to any of the ancient Sequoia trees.
The duff layer also was removed to reduce possibility of the forest floor near the Sequoias of burning with high intensity.
As of Tuesday evening, the Ga…

Low-Complexity CSI Acquisition Exploiting Geographical Diversity in Fluid Antenna System
Zhentian Zhang, David Morales-Jimenez, Jian Dang, Zaichen Zhang, Christos Masouros, Hao Jiang
https://arxiv.org/abs/2509.08598

Low-Complexity CSI Acquisition Exploiting Geographical Diversity in Fluid Antenna System
The fluid antenna system (FAS) employs reconfigurable antennas for high spatial gains in compact spaces, enhancing physical layer flexibility. Channel state information (CSI) acquisition is vital for port selection and FAS optimization. Greedy algorithms rely on signal assumptions, and model-free methods face high complexity. A flexible, low-complexity solution is needed for massive connectivity in FAS. Based on expectation maximization-approximate message passing (EM-AMP) framework, efficient …

Atomic Layer Etching of Aluminum Nitride: Mechanistic Insights from First-Principles Studies of Chlorine Chemistry
Sanjay Nayak, Nikolai Andrianov, Thomas Gruhn, Joaquin Miranda
https://arxiv.org/abs/2509.07554

Atomic Layer Etching of Aluminum Nitride: Mechanistic Insights from First-Principles Studies of Chlorine Chemistry
Using first-principles density functional theory calculations in combination with the climbing-image nudged elastic band method, we investigated the adsorption, desorption, and diffusion of atomic chlorine and molecular chlorine on the Al terminated 0001 surface of aluminum nitride. Our results reveal that both atomic Cl and Cl2 exhibit a chemisorption character with high binding energies. Calculations revealed that the splitting pathway of Cl2 on the Al terminated AlN 0001 surface is a barrier…

Heterogeneous optimized Schwarz Methods for heat conduction in composites with thermal contact resistance
Huan Zhang, Hui Zhang, Yan Wang, Yingxiang Xu
https://arxiv.org/abs/2508.06408

Heterogeneous optimized Schwarz Methods for heat conduction in composites with thermal contact resistance
Heat transfer in composites is critical in engineering, where imperfect layer contact causes thermal contact resistance (TCR), leading to interfacial temperature discontinuity. We propose solving this numerically using the optimized Schwarz method (OSM), which decouples the heterogeneous problem into homogeneous subproblems. This avoids ill-conditioned systems from monolithic solving due to high contrast and interface jumps. Both energy estimate and Fourier analysis are used to prove the conver…

ELMUR: External Layer Memory with Update/Rewrite for Long-Horizon RL
Egor Cherepanov, Alexey K. Kovalev, Aleksandr I. Panov
https://arxiv.org/abs/2510.07151 https://

ELMUR: External Layer Memory with Update/Rewrite for Long-Horizon RL
Real-world robotic agents must act under partial observability and long horizons, where key cues may appear long before they affect decision making. However, most modern approaches rely solely on instantaneous information, without incorporating insights from the past. Standard recurrent or transformer models struggle with retaining and leveraging long-term dependencies: context windows truncate history, while naive memory extensions fail under scale and sparsity. We propose ELMUR (External Laye…

Identity Increases Stability in Neural Cellular Automata
James Stovold
https://arxiv.org/abs/2508.06389 https://arxiv.org/pdf/2508.06389

Identity Increases Stability in Neural Cellular Automata
Neural Cellular Automata (NCAs) offer a way to study the growth of two-dimensional artificial organisms from a single seed cell. From the outset, NCA-grown organisms have had issues with stability, their natural boundary often breaking down and exhibiting tumour-like growth or failing to maintain the expected shape. In this paper, we present a method for improving the stability of NCA-grown organisms by introducing an 'identity' layer with simple constraints during training. Results show that…

Developing a Sequential Deep Learning Pipeline to Model Alaskan Permafrost Thaw Under Climate Change
Addina Rahaman
https://arxiv.org/abs/2510.06258 https://

Developing a Sequential Deep Learning Pipeline to Model Alaskan Permafrost Thaw Under Climate Change
Changing climate conditions threaten the natural permafrost thaw-freeze cycle, leading to year-round soil temperatures above 0°C. In Alaska, the warming of the topmost permafrost layer, known as the active layer, signals elevated greenhouse gas release due to high carbon storage. Accurate soil temperature prediction is therefore essential for risk mitigation and stability assessment; however, many existing approaches overlook the numerous factors driving soil thermal dynamics. This study prese…

October 23rd, 1989, 36 years ago, on #climate, we got the POV from outside. 🚀🌍👀
Atlantis Commander Donald Williams said:
“The world as we know it is a very fragile place.”
And mission specialist Ellen Baker 👩‍🚀 on the ozone layer:
“Our world is a beautiful place, and we do need to take care of it. You get an appreciation of how thin the protective layer is above the planet …

Shuttle to Land 3 Hours Earlier Because of Winds
The five astronauts aboard the shuttle Atlantis prepared Sunday to return to a planet they now view with increased awareness of its fragility, planning to touch down today at Edwards Air Force Base at 9:33 a.m., three hours earlier than planned.

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Slayer:
🎵 South of Heaven
#Slayer
https://yinteklop.bandcamp.com/track/slayer-south-of-heaven
https://open.spotify.com/track/3U9gogDStAfWxSm91PJuxb

Orbital Hybridization-Induced Ising-Type Superconductivity in a Confined Gallium Layer
Hemian Yi, Yunzhe Liu, Chengye Dong, Yiheng Yang, Zi-Jie Yan, Zihao Wang, Lingjie Zhou, Dingsong Wu, Houke Chen, Stephen Paolini, Bing Xia, Bomin Zhang, Xiaoda Liu, Hongtao Rong, Annie G. Wang, Saswata Mandal, Kaijie Yang, Benjamin N. Katz, Lunhui Hu, Jieyi Liu, Tien-Lin Lee, Vincent H. Crespi, Yuanxi Wang, Yulin Chen, Joshua A. Robinson, Chao-Xing Liu, Cui-Zu Chang

Orbital Hybridization-Induced Ising-Type Superconductivity in a Confined Gallium Layer
In low-dimensional superconductors, the interplay between quantum confinement and interfacial hybridization effects can reshape Cooper pair wavefunctions and induce novel forms of unconventional superconductivity. In this work, we employ a plasma-free, carbon buffer layer-assisted confinement epitaxy method to synthesize trilayer gallium (Ga) sandwiched between a graphene layer and a 6H-SiC(0001) substrate, forming an air-stable graphene/trilayer Ga/SiC heterostructure. In this confined light-e…

ReMu: Reconstructing Multi-layer 3D Clothed Human from Image Layers
Onat Vuran, Hsuan-I Ho
https://arxiv.org/abs/2508.01381 https://arxiv.org/pdf/2508.0138…

ReMu: Reconstructing Multi-layer 3D Clothed Human from Image Layers
The reconstruction of multi-layer 3D garments typically requires expensive multi-view capture setups and specialized 3D editing efforts. To support the creation of life-like clothed human avatars, we introduce ReMu for reconstructing multi-layer clothed humans in a new setup, Image Layers, which captures a subject wearing different layers of clothing with a single RGB camera. To reconstruct physically plausible multi-layer 3D garments, a unified 3D representation is necessary to model these gar…

Tigris, which is building a network of localized data storage centers that it claims can meet the compute needs of AI workloads, raised a $25M Series A (Rebecca Bellan/TechCrunch)
https://techcrunch.com/2025/10/09/this-distributed-data-stor…

This distributed data storage startup wants to take on Big Cloud | TechCrunch
Tigris has raised $25 million to expand its growing network of localized data storage centers — the storage layer for decentralized computing infrastructure.

A Multi-Layer Electronic and Cyber Interference Model for AI-Driven Cruise Missiles: The Case of Khuzestan Province
Pouriya Alimoradi, Ali Barati, Hamid Barati
https://arxiv.org/abs/2510.03542

A Multi-Layer Electronic and Cyber Interference Model for AI-Driven Cruise Missiles: The Case of Khuzestan Province
The rapid advancement of Artificial Intelligence has enabled the development of cruise missiles endowed with high levels of autonomy, adaptability, and precision. These AI driven missiles integrating deep learning algorithms, real time data processing, and advanced guidance systems pose critical threats to strategic infrastructures, especially under complex geographic and climatic conditions such as those found in Irans Khuzestan Province. In this paper, we propose a multi layer interference mo…

A 66-Gb/s/5.5-W RISC-V Many-Core Cluster for 5G Software-Defined Radio Uplinks
Marco Bertuletti, Yichao Zhang, Alessandro Vanelli-Coralli, Luca Benini
https://arxiv.org/abs/2508.06176

A 66-Gb/s/5.5-W RISC-V Many-Core Cluster for 5G+ Software-Defined Radio Uplinks
Following the scale-up of new radio (NR) complexity in 5G and beyond, the physical layer's computing load on base stations is increasing under a strictly constrained latency and power budget; base stations must process > 20-Gb/s uplink wireless data rate on the fly, in < 10 W. At the same time, the programmability and reconfigurability of base station components are the key requirements; it reduces the time and cost of new networks' deployment, it lowers the acceptance threshold for industry pl…

paris_transportation: Paris Transportation (2016)
A multiplex transportation network of Ile-de-France, jointly representing the road network, train network, and subway network. Nodes are intersections, rail stations or metro stations. Different modes of transportation are given by different layers. Within-layer edges represent direct transference by a particular mode of transportation, and between-layer edges represent the conjunction of those layers at that node.
This network ha…

AI Compute Architecture and Evolution Trends
Bor-Sung Liang
https://arxiv.org/abs/2508.21394 https://arxiv.org/pdf/2508.21394

AI Compute Architecture and Evolution Trends
The focus of AI development has shifted from academic research to practical applications. However, AI development faces numerous challenges at various levels. This article will attempt to analyze the opportunities and challenges of AI from several different perspectives using a structured approach. This article proposes a seven-layer model for AI compute architecture, including Physical Layer, Link Layer, Neural Network Layer, Context Layer, Agent Layer, Orchestrator Layer, and Application Laye…

Guided Star-Shaped Masked Diffusion
Viacheslav Meshchaninov, Egor Shibaev, Artem Makoian, Ivan Klimov, Danil Sheshenya, Andrei Malinin, Nikita Balagansky, Daniil Gavrilov, Aibek Alanov, Dmitry Vetrov
https://arxiv.org/abs/2510.08369

Guided Star-Shaped Masked Diffusion
The performance of pre-trained masked diffusion models is often constrained by their sampling procedure, which makes decisions irreversible and struggles in low-step generation regimes. We introduce a novel sampling algorithm that works with pre-trained models and, after a lightweight fine-tuning of a single layer, significantly improves sample quality and efficiency. Our method reformulates the generation process using a star-shaped paradigm, which inherently allows for error correction. To ma…

Topological Magnon-Plasmon Hybrids
Tomoki Hirosawa, Pieter M. Gunnink, Alexander Mook
https://arxiv.org/abs/2510.07916 https://arxiv.org/pdf/2510.07916

Topological Magnon-Plasmon Hybrids
We study magnon-plasmon coupling in effectively two-dimensional stacks of van der Waals layers in the context of the band structure topology. Invoking the quasiparticle approximation, we show that the magnetic dipole coupling between the plasmons in a metallic layer and the magnons in a neighboring magnetic layer gives rise to a Berry curvature. As a result, the hybrid quasiparticles acquire an anomalous velocity, leading to intrinsic anomalous thermal Hall and spin-Nernst effects in ferromagne…

Large AI Models for Wireless Physical Layer
Jiajia Guo, Yiming Cui, Shi Jin, Jun Zhang
https://arxiv.org/abs/2508.02314 https://arxiv.org/pdf/2508.02314

Large AI Models for Wireless Physical Layer
Large artificial intelligence models (LAMs) are transforming wireless physical layer technologies through their robust generalization, multitask processing, and multimodal capabilities. This article reviews recent advancements in LAM applications for physical layer communications, addressing limitations of conventional AI-based approaches. LAM applications are classified into two strategies: leveraging pre-trained LAMs and developing native LAMs designed specifically for physical layer tasks. T…

SlimInfer: Accelerating Long-Context LLM Inference via Dynamic Token Pruning
Lingkun Long, Rubing Yang, Yushi Huang, Desheng Hui, Ao Zhou, Jianlei Yang
https://arxiv.org/abs/2508.06447

SlimInfer: Accelerating Long-Context LLM Inference via Dynamic Token Pruning
Long-context inference for Large Language Models (LLMs) is heavily limited by high computational demands. While several existing methods optimize attention computation, they still process the full set of hidden states at each layer, limiting overall efficiency. In this work, we propose SlimInfer, an innovative framework that aims to accelerate inference by directly pruning less critical prompt tokens during the forward pass. Our key insight is an information diffusion phenomenon: As information…

Enhancing Optical Performance of Liquid Crystal Lens Arrays via Electrode Design Optimization
Rui Ding, Li-Lan Tian, Yi Zheng, Yu-Meng Zeng, Fan Zou, Yue Niu, Rong-Fu Liu, Ji-Wei Zhou
https://arxiv.org/abs/2509.03861

Enhancing Optical Performance of Liquid Crystal Lens Arrays via Electrode Design Optimization
A liquid crystal (LC) lens array based on double-layer composite electrodes, characterized by a large aperture, short focal length, and low operating voltage is demonstrated. The lens array consists of an LC layer, a top common electrode, a bottom double-layer composite electrode layer, and an oxide layer. The bottom double-layer composite electrode layer comprises the pixel electrodes and the auxiliary electrode. In focusing mode, the pixel electrodes receive operational voltage to establish t…

BlockSDN: Towards a High-Performance Blockchain via Software-Defined Cross Networking optimization
Wenyang Jia, Jingjing Wang, Ziwei Yan, Xiangli Peng, Guohui Yuan
https://arxiv.org/abs/2510.08139

BlockSDN: Towards a High-Performance Blockchain via Software-Defined Cross Networking optimization
The scalability of blockchain systems is constrained by inefficient P2P broadcasting, as most existing optimizations focus only on the logical layer without considering physical network conditions. To address this, we propose BlockSDN, the first SDN-based integrated architecture for blockchain. BlockSDN employs a distributed control plane for a global network view, a graph engine for hierarchical clustering, and a hybrid macro-micro neighbor selection with hierarchical broadcasting. A dedicated…

SBS: Enhancing Parameter-Efficiency of Neural Representations for Neural Networks via Spectral Bias Suppression
Qihu Xie, Yuan Li, Yi Kang
https://arxiv.org/abs/2509.07373 https…

SBS: Enhancing Parameter-Efficiency of Neural Representations for Neural Networks via Spectral Bias Suppression
Implicit neural representations have recently been extended to represent convolutional neural network weights via neural representation for neural networks, offering promising parameter compression benefits. However, standard multi-layer perceptrons used in neural representation for neural networks exhibit a pronounced spectral bias, hampering their ability to reconstruct high-frequency details effectively. In this paper, we propose SBS, a parameter-efficient enhancement to neural representatio…

Studies on the effect of low-fluence proton and neutron irradiation on n-type LGADs
Veronika Kraus, Marcos Fernandez Garcia, Salvador Hidalgo, Michael Moll, Jairo Villegas
https://arxiv.org/abs/2508.06282

Studies on the effect of low-fluence proton and neutron irradiation on n-type LGADs
The presented study investigates the effects of low fluences from $5\times10^{12}$ up to $1\times10^{14}$ particles/cm$^{2}$ of 60MeV proton and neutron irradiation on n-type Low Gain Avalanche Detectors (nLGADs). An nLGAD is a silicon sensor with a highly doped gain layer that enables controlled charge multiplication via impact ionization. In contrast to the well-established p-type LGADs for high-energy physics (HEP) applications, nLGADs are optimized for the detection of low-penetrating parti…

Systematic evolution of superconducting pairing strength and Seebeck coefficients in correlated infinite-layer La$_{1-x}$Sr$_x$NiO$_2$
Motoki Osada, Shusaku Imajo, Yuji Seki, Kousuke Ishida, Tsutomu Nojima, Kohei Fujiwara, Koichi Kindo, Yusuke Nomura, Atsushi Tsukazaki
https://arxiv.org/abs/2510.04431

Systematic evolution of superconducting pairing strength and Seebeck coefficients in correlated infinite-layer La$_{1-x}$Sr$_x$NiO$_2$
The recently discovered superconducting infinite-layer nickelates offer a novel platform to explore an exotic pairing mechanism in multi-band systems towards high-temperature superconductivity and associated rich quantum phases, contrasting with cuprates. Here, we show that infinite-layer (La,Sr)NiO$_2$ exhibits strong-coupling superconductivity, resilient to in-plane magnetic fields exceeding 47 T at optimal doping - more than twice the Pauli limit for conventional BCS superconductors. This vi…

Longitudinal spin current absorption in bilayers composed of ferromagnetic and highly-resistive non-magnetic layers
Sosuke Hori, Kohei Ueda, Junichi Shiogai, Jobu Matsuno
https://arxiv.org/abs/2509.07390

Longitudinal spin current absorption in bilayers composed of ferromagnetic and highly-resistive non-magnetic layers
Spin Hall magnetoresistance (SMR) is an intriguing spin-dependent transport phenomenon in bilayers consisting of non-magnetic and magnetic layers. Here, we report on the influence of longitudinal spin current absorption by the magnetic layer on SMR in bilayers composed of Co$_{20}$Fe$_{60}$B$_{20}$ (CoFeB) and epitaxial SrIrO$_{3}$, where SrIrO$_{3}$ is used as a highly-resistive spin current source. We observed a clear SMR signal and an enhancement in the SMR ratio with increasing CoFeB layer …

EICAP: Deep Dive in Assessment and Enhancement of Large Language Models in Emotional Intelligence through Multi-Turn Conversations
Nizi Nazar, Ehsaneddin Asgari
https://arxiv.org/abs/2508.06196

EICAP: Deep Dive in Assessment and Enhancement of Large Language Models in Emotional Intelligence through Multi-Turn Conversations
Emotional Intelligence (EI) is a critical yet underexplored dimension in the development of human-aligned LLMs. To address this gap, we introduce a unified, psychologically grounded four-layer taxonomy of EI tailored for large language models (LLMs), encompassing emotional tracking, cause inference, appraisal, and emotionally appropriate response generation. Building on this framework, we present EICAP-Bench, a novel MCQ style multi-turn benchmark designed to evaluate EI capabilities in open-so…

Boundary layer effects induced by the fluid in a chemotaxis-Navier-Stokes system
Qianqian Hou
https://arxiv.org/abs/2509.03028 https://arxiv.org/pdf/2509.0…

Boundary layer effects induced by the fluid in a chemotaxis-Navier-Stokes system
This paper is concerned with the boundary layer problem on a chemotaxis-Navier-Stokes system modelling the boundary layer formation of aerobic bacteria in fluids. Completing this system with Neumann boundary conditions on oxygen, we show that gradients of its second solution component in the half plane of $\mathbb{R}^2$ possess boundary layer effects as the oxygen diffusion rate goes to zero. However, neglecting the influence of the fluid, gradients of solutions to the chemotaxis-only subsystem…

Transient almost-invariant sets reveal convective heat transfer patterns in plane-layer Rayleigh-B\'enard convection
Aleksandar Badza, Gary Froyland, Roshan J. Samuel, J\"org Schumacher
https://arxiv.org/abs/2510.05590

Transient almost-invariant sets reveal convective heat transfer patterns in plane-layer Rayleigh-Bénard convection
Horizontally extended plane-layer convection flows are characterized by characteristic patterns of turbulent heat transfer due to the convective fluid motion consisting of a nearly-regular ridge network where hot fluid rises and cold fluid sinks. Here, we analyse this transport behavior by the so-called inflated generator framework, which identifies quasi-stationary families of almost-invariant sets, derived from leading inflated generator eigenvectors. We demonstrate the effectiveness of this …

Intrinsic Layer-Dependent Surface Energy and Exfoliation Energy of van der Waals Materials
Lin-Lin Wang, Jiaqiang Yan, Yong Han, Claire C. Wang, Jian-Xiang Qiu, Su-Yang Xu, Adam Kaminski, Michael C. Tringides, Paul C. Canfield
https://arxiv.org/abs/2508.04898

Intrinsic Layer-Dependent Surface Energy and Exfoliation Energy of van der Waals Materials
Stacking and twisting 2D van der Waals (vdW) layers have become versatile platforms to tune electron correlation. These platforms rely on exfoliating vdW materials down to a single and few vdW layers. We calculate the intrinsic layer-dependent surface and exfoliation energies of typical vdW materials such as, graphite, h-BN, black P, MX$_2$ (M=Mo and W, X=S, Se and Te), MX (M=Ga and In, X=S, Se and Te), Bi$_2$Te$_3$ and MnBi$_2$Te$_4$ using density functional theory. For exchange-correlation fu…

Quantum Reservoir Computing for Credit Card Default Prediction on a Neutral Atom Platform
Giacomo Vitali, Chiara Vercellino, Paolo Viviani, Olivier Terzo, Bartolomeo Montrucchio, Valeria Zaffaroni, Francesca Cibrario, Christian Mattia, Giacomo Ranieri, Alessandro Sabatino, Francesco Bonazzi, Davide Corbelletto
https://arxiv.org/abs/2510.04…

Quantum Reservoir Computing for Credit Card Default Prediction on a Neutral Atom Platform
In this paper, we define and benchmark a hybrid quantum-classical machine learning pipeline by performing a binary classification task applied to a real-world financial use case. Specifically, we implement a Quantum Reservoir Computing (QRC) layer within a classical routine that includes data preprocessing and binary classification. The reservoir layer has been executed on QuEra's Aquila, a 256-qubit neutral atom simulator, using two different types of encoding: position and local detuning. In …

Toward Model Matching for Remotely Controlled Differential Drive Robotic Vehicles
Nikolaos D. Kouvakas, Fotis N. Koumboulis, Konstantinos G. Tzierakis, John Sigalas, Anastasios Dimakakos
https://arxiv.org/abs/2510.06081

Toward Model Matching for Remotely Controlled Differential Drive Robotic Vehicles
The problem of regulation of the orientation angle of a remotely controlled differential-drive mobile robot with actuator dynamics and network-induced delays is studied. Using a preinstalled two-layer nonlinear control scheme that decouples linear and angular velocities and regulates heading, a third, delay-dependent layer that achieves exact model matching from the orientation angle command to the orientation angle is introduced. The proposed outer loop controller is a delay dependent dynamic …

Accelerating vRAN and O-RAN with SIMD: Architectural Perspectives and Performance Evaluation
Jaebum Park, Chan-Byoung Chae, Robert W. Heath Jr
https://arxiv.org/abs/2510.07843 h…

Accelerating vRAN and O-RAN with SIMD: Architectural Perspectives and Performance Evaluation
The evolution of radio access networks (RANs) toward virtualization and openness creates new opportunities for flexible, cost-effective, and high-performance deployments. Achieving real-time and energy-efficient baseband processing on commercial off-the-shelf platforms, however, remains a critical challenge. This article explores how single instruction multiple data (SIMD) architectures can accelerate RAN workloads. We first outline why key physical-layer functions, such as channel estimation, …

Learning What's Missing: Attention Dispersion and EMA Stabilization in Length Generalization
P\'al Zs\'amboki, Benjamin Levi, David Ansel Josef Smith, Mitansh Kagalwala, Arlington Kell, Samuel Liechty, Cong Wang
https://arxiv.org/abs/2510.08341

Learning What's Missing: Attention Dispersion and EMA Stabilization in Length Generalization
We study length generalization in transformers through the set complement task, where a model must predict a uniform distribution over tokens absent from an input sequence -- an ability central to board-game style reasoning. Our main theoretical result establishes two statements. First, we prove tight bounds on embedding and value dimensions for single-layer attention-only transformers. Second, we show that if such a model achieves balanced logit displacement at lengths 1 and 2, then it must ge…

paris_transportation: Paris Transportation (2016)
A multiplex transportation network of Ile-de-France, jointly representing the road network, train network, and subway network. Nodes are intersections, rail stations or metro stations. Different modes of transportation are given by different layers. Within-layer edges represent direct transference by a particular mode of transportation, and between-layer edges represent the conjunction of those layers at that node.
This network ha…

Upscaling Tomographic Volumetric 3D Printing via Virtual Stitching of Coordinated Projections
Hossein Safari, S. Kaveh Hedayati, Aminul Islam, Yi Yang
https://arxiv.org/abs/2510.07981

Upscaling Tomographic Volumetric 3D Printing via Virtual Stitching of Coordinated Projections
Tomographic volumetric 3D printing offers layer free, rapid fabrication of objects with high design freedom, but is limited to relatively small curing volumes because of the optical constraints imposed by an assumed need for telecentricity. We present a method to virtually stitch multiple projections from different light sources to build a single workpiece. To avoid the built in requirement for telecentricity and thus the need for an index matching vat, projections are produced by a graphics pr…

Native Hybrid Attention for Efficient Sequence Modeling
Jusen Du, Jiaxi Hu, Tao Zhang, Weigao Sun, Yu Cheng
https://arxiv.org/abs/2510.07019 https://arxiv.…

Native Hybrid Attention for Efficient Sequence Modeling
Transformers excel at sequence modeling but face quadratic complexity, while linear attention offers improved efficiency but often compromises recall accuracy over long contexts. In this work, we introduce Native Hybrid Attention (NHA), a novel hybrid architecture of linear and full attention that integrates both intra \& inter-layer hybridization into a unified layer design. NHA maintains long-term context in key-value slots updated by a linear RNN, and augments them with short-term tokens fro…

Stable, which is building a new Layer 1 blockchain for stablecoins, raised a $28M seed co-led by Bitfinex and Hack VC (Yogita Khatri/The Block)
https://www.theblock.co/post/364952/tether-focused-layer-1-stable-raises-28-mi…

Latent Uncertainty Representations for Video-based Driver Action and Intention Recognition
Koen Vellenga, H. Joe Steinhauer, Jonas Andersson, Anders Sj\"ogren
https://arxiv.org/abs/2510.05006

Latent Uncertainty Representations for Video-based Driver Action and Intention Recognition
Deep neural networks (DNNs) are increasingly applied to safety-critical tasks in resource-constrained environments, such as video-based driver action and intention recognition. While last layer probabilistic deep learning (LL-PDL) methods can detect out-of-distribution (OOD) instances, their performance varies. As an alternative to last layer approaches, we propose extending pre-trained DNNs with transformation layers to produce multiple latent representations to estimate the uncertainty. We ev…

A layered architecture for log analysis in complex IT systems
Thorsten Wittkopp
https://arxiv.org/abs/2509.08698 https://arxiv.org/pdf/2509.08698

A layered architecture for log analysis in complex IT systems
In the evolving IT landscape, stability and reliability of systems are essential, yet their growing complexity challenges DevOps teams in implementation and maintenance. Log analysis, a core element of AIOps, provides critical insights into complex behaviors and failures. This dissertation introduces a three-layered architecture to support DevOps in failure resolution. The first layer, Log Investigation, performs autonomous log labeling and anomaly classification. We propose a method that label…

The Door to Policy Portability might be an IP Overlay
Behrooz Farkiani, Fan Liu, Patrick Crowley
https://arxiv.org/abs/2510.04052 https://arxiv.org/pdf/251…

The Door to Policy Portability might be an IP Overlay
Portable service mesh implementations enable layer 4 to layer 7 policy enforcement across diverse infrastructures, but they remain tied to infrastructure-specific layer 3 network policies. Network policies enable control over IP traffic flow regardless of whether traffic is authorized at the application level. However, not all infrastructure supports enforcing them, and achieving consistent enforcement across heterogeneous environments is challenging. For example, studies have shown that the ma…

Backward similarity solution of the Boussinesq groundwater equation
Shuntaro Togo, Koichi Unami
https://arxiv.org/abs/2509.07478 https://arxiv.org/pdf/2509…

Backward similarity solution of the Boussinesq groundwater equation
Groundwater flow in an unconfined aquifer resting on a horizontal impermeable layer with a boundary condition of a rapid increase in the source water level is considered in this work. The newly introduced condition, referred to as the backward power-law head condition, represents a situation where the water level in the adjacent water body increases more rapidly than do conventional problems, which can only represent a situation akin to a traveling wave under rising water level conditions, give…

Mo Atom Rearrangement Drives Layer-Dependent Reactivity in Two-Dimensional MoS2
Zifan Wang, Jiaxuan Wen, Tina Mihm, Shaopeng Feng, Kelvin Huang, Jing Tang, Tianshu Li, Liangbo Liang, Sahar Sharifzadeh, Keji Lai, Xi Ling
https://arxiv.org/abs/2509.04648

Mo Atom Rearrangement Drives Layer-Dependent Reactivity in Two-Dimensional MoS2
Two-dimensional (2D) materials offer a valuable platform for manipulating and studying chemical reactions at atomic level, owing to the ease of controlling their microscopic structure at the nanometer scale. While extensive research has been conducted on the structure-dependent chemical activity of 2D materials, the influence of structural transformation during the reaction remains largely unexplored. In this work, we report the layer-dependent chemical reactivity of MoS2 during a nitridation a…

BATTLE for Bitcoin: Capital-Efficient Optimistic Bridges with Large Committees
Sergio Demian Lerner, Ariel Futoransky
https://arxiv.org/abs/2510.06468 https://

BATTLE for Bitcoin: Capital-Efficient Optimistic Bridges with Large Committees
We present BATTLE for Bitcoin, a DoS-resilient dispute layer that secures optimistic bridges between Bitcoin and rollups or sidechains. Our design adapts the BATTLE tournament protocol to Bitcoin's UTXO model using BitVM-style FLEX components and garbled circuits with on-demand L1 security bonds. Disputes are resolved in logarithmic rounds while recycling rewards, keeping the honest asserter's minimum initial capital constant even under many permissionless challengers. The construction is fully…

Recent progress in nickelate superconductors
Yuxin Wang, Kun Jiang, Jianjun Ying, Tao Wu, Jinguang Cheng, Jiangping Hu, Xianhui Chen
https://arxiv.org/abs/2509.08386 https://

Recent progress in nickelate superconductors
The discovery of superconductivity in nickelate compounds has opened new avenues in the study of high-temperature superconductors. Here we provide a comprehensive overview of recent progress in the field, including all different nickelate systems, reduced-Ruddlesden-Popper-type infinite layer LaNiO$_2$, Ruddlesden-Popper-type bilayer La$_3$Ni$_2$O$_7$ and trilayer La$_4$Ni$_3$O$_{10}$. We begin by introducing the superconducting properties of the hole-doped LaNiO$_2$ system, which marked the st…

Behind the scenes of the Quantum Extreme Learning Machines
A. De Lorenzis, M. P. Casado, N. Lo Gullo, T. Lux, F. Plastina, A. Riera
https://arxiv.org/abs/2509.06873 https://

Behind the scenes of the Quantum Extreme Learning Machines
In recent years, Quantum Machine Learning (QML) has grown rapidly, emerging as a promising approach to make quantum computing implementation competitive. In this work, we investigate Quantum Extreme Learning Machines (QELM), a quantum variant of Extreme Learning Machines where training is restricted to the output layer. The proposed architecture combines dimensionality reduction (via PCA or Autoencoders), quantum state encoding, evolution under an XX Hamiltonian, and measurement, providing feat…

Optical absorption of a Cu$_2$SnS$_3$ (CTS) layer trapped by metallic thin films in multilayer configuration
Le Tri Dat, Nguyen Duy Vy
https://arxiv.org/abs/2508.04113 https://

Optical absorption of a Cu$_2$SnS$_3$ (CTS) layer trapped by metallic thin films in multilayer configuration
Coating and reflecting thin films for energy harvesting purposes are interesting topics in both theoretical and experimental research. The thin film could help to enhance the absorption of the system via its specific optical properties depending on the optical wavelength and the stacked layer thickness. Here, by using Maxwell's equations for the electromagnetic fields penetrating thin films, we examined in detail the absorption of a CTS layer coated by nanometer-thick thin films of several mate…

Heart Disease Prediction: A Comparative Study of Optimisers Performance in Deep Neural Networks
Chisom Chibuike, Adeyinka Ogunsanya
https://arxiv.org/abs/2509.08499 https://

Heart Disease Prediction: A Comparative Study of Optimisers Performance in Deep Neural Networks
Optimization has been an important factor and topic of interest in training deep learning models, yet less attention has been given to how we select the optimizers we use to train these models. Hence, there is a need to dive deeper into how we select the optimizers we use for training and the metrics that determine this selection. In this work, we compare the performance of 10 different optimizers in training a simple Multi-layer Perceptron model using a heart disease dataset from Kaggle. We se…

The Geometry of Truth: Layer-wise Semantic Dynamics for Hallucination Detection in Large Language Models
Amir Hameed Mir
https://arxiv.org/abs/2510.04933 https://

The Geometry of Truth: Layer-wise Semantic Dynamics for Hallucination Detection in Large Language Models
Large Language Models (LLMs) often produce fluent yet factually incorrect statements-a phenomenon known as hallucination-posing serious risks in high-stakes domains. We present Layer-wise Semantic Dynamics (LSD), a geometric framework for hallucination detection that analyzes the evolution of hidden-state semantics across transformer layers. Unlike prior methods that rely on multiple sampling passes or external verification sources, LSD operates intrinsically within the model's representational…

Replaced article(s) found for physics.flu-dyn. https://arxiv.org/list/physics.flu-dyn/new
[1/1]:
- On the enhancement of boundary layer skin friction by turbulence: an angular momentum approach
Ahmed Elnahhas, Perry L. Johnson

Operation Regimes and Design Principles of Delta-E Effect Sensors
Fatih Ilgaz, Elizaveta Spetzler, Patrick Wiegand, Robert Rieger, Jeffrey McCord, Benjamin Spetzler
https://arxiv.org/abs/2508.06114

Operation Regimes and Design Principles of Delta-E Effect Sensors
Delta-E effect-based magnetoelectric sensors have emerged as promising technology for detecting weak magnetic fields at low frequencies. However, the performance of such sensors remains difficult to predict, as signal and noise characteristics are dictated by interdependent parameters such as magnetic layer geometry, magnetic microstructure, and loss. In this work, we present a systematic experimental study of sub-mm-sized delta-E effect sensors, comprising 24 device configurations that vary in…

Unveiling Intrinsic Triplet Superconductivity in Noncentrosymmetric NbRe through Inverse Spin-Valve Effects
F. Colangelo, M. Modestino, F. Avitabile, A. Galluzzi, Z. Makhdoumi Kakhaki, A. Kumar, J. Linder, M. Polichetti, C. Attanasio, C. Cirillo
https://arxiv.org/abs/2510.08110

Unveiling Intrinsic Triplet Superconductivity in Noncentrosymmetric NbRe through Inverse Spin-Valve Effects
NbRe is a non-centrosymmetric superconductor that has been proposed as a candidate for intrinsic spin-triplet pairing. However, a conclusive demonstration of triplet pairing in NbRe is yet to be found. To probe the presence of equal-spin triplet Cooper pairs, we fabricated Py/NbRe/Py trilayers capped with an antiferromagnetic layer. Magnetic and electrical measurements reveal an inverse spin-valve effect, which could indicate equal-spin triplet superconductivity. The minimal sample structure an…

A Secure Sequencer and Data Availability Committee for Rollups (Extended Version)
Margarita Capretto, Mart\'in Ceresa, Antonio Fern\'andez Anta, Pedro Moreno S\'anchez, C\'esar S\'anchez
https://arxiv.org/abs/2509.06614

A Secure Sequencer and Data Availability Committee for Rollups (Extended Version)
Blockchains face a scalability limitation, partly due to the throughput limitations of consensus protocols, especially when aiming to obtain a high degree of decentralization. Layer 2 Rollups (L2s) are a faster alternative to conventional blockchains. L2s perform most computations offchain using minimally blockchains (L1) under-the-hood to guarantee correctness. A sequencer is a service that receives offchain L2 transaction requests, batches these transactions, and commits compressed or hashed …

Strong interlayer coupling and chiral flat-band cascades in twisted bilayer gratings
Daegwang Choi, Soon-Jae Lee, Seung-Woon Cho, Chan Bin Bark, Seik Pak, Dong-Jin Shin, Moon Jip Park, Su-Hyun Gong
https://arxiv.org/abs/2510.07381

Strong interlayer coupling and chiral flat-band cascades in twisted bilayer gratings
From atomic crystals to macroscopic material structures, twisted bilayer systems have emerged as a promising route to control wave phenomena. In few-layer van der Waals (vdW) materials, however, the intrinsically weak interlayer coupling typically demands fine control of small twist angles to reach magic-angle conditions. Here, we show that one-dimensional photonic crystal bilayers can overcome this limitation by accessing a regime of strong interlayer coupling -- comparable to intralayer coupl…

Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification
Borja Aizpurua, Sukhbinder Singh, Rom\'an Or\'us
https://arxiv.org/abs/2509.06653

Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification
We address the problem of implementing bottleneck layers from classical pre-trained neural networks on a quantum computer, with the goal of achieving quantum advantage on near-term devices. Our approach begins with a compression step in which the target linear layer is represented as an effective matrix product operator (MPO) without degrading model performance. The MPO is then further disentangled into a more compact form. This enables a hybrid classical-quantum execution scheme, where the dis…

Observation of momentum dependent charge density wave gap in EuTe4
Iftakhar Bin Elius, Nathan Valadez, Gyanendra Dhakal, Volodymyr Buturlim, Sabin Regmi, Dante James, Peter Radanovich, Matthew Yankowitz, Tetiana Romanova, Andrzej Ptok, Krzysztof Gofryk, Dariusz Kaczorowski, Madhab Neupane
https://arxiv.org/abs/2508.06464

Observation of momentum dependent charge density wave gap in EuTe4
The occurrence of charge density wave (CDW) phenomena, particularly in low dimensional rare-earth chalcogenides, has attracted substantial research interest. Among these materials, EuTe4, which features multiple Te layers and a single Eu-Te layer, serves as a promising platform to study the interplay between CDW order and 4f electron configurations, including magnetism. In this study, First principles based density functional theory (DFT) calculations were carried out to investigate the electro…

Revealing the Staging Structural Evolution and Li (De)Intercalation Kinetics in Graphite Anodes via Machine Learning Potential
Liqi Wang, Xuhe Gong, Zicun Li, Ruijuan Xiao, Hong Li
https://arxiv.org/abs/2508.06156

Revealing the Staging Structural Evolution and Li (De)Intercalation Kinetics in Graphite Anodes via Machine Learning Potential
Revealing the dynamic structural evolution and lithium transport properties during the charge/discharge processes is crucial for optimizing graphite anodes in lithium-ion batteries, enabling high stability and fast-charging performance. However, the dynamic coupling mechanisms among carbon layer kinetics, lithium (de)intercalation/diffusion, and defects regulation remain insufficiently understood. In this study, we developed a universal automated workflow based on machine learning potentials to…

Rethinking Layer-wise Gaussian Noise Injection: Bridging Implicit Objectives and Privacy Budget Allocation
Qifeng Tan (Xi'an Jiaotong University), Shusen Yang (Xi'an Jiaotong University), Xuebin Ren (Xi'an Jiaotong University), Yikai Zhang (Xi'an Jiaotong University)
https://arxiv.org/abs/2509.04232

Rethinking Layer-wise Gaussian Noise Injection: Bridging Implicit Objectives and Privacy Budget Allocation
Layer-wise Gaussian mechanisms (LGM) enhance flexibility in differentially private deep learning by injecting noise into partitioned gradient vectors. However, existing methods often rely on heuristic noise allocation strategies, lacking a rigorous understanding of their theoretical grounding in connecting noise allocation to formal privacy-utility tradeoffs. In this paper, we present a unified analytical framework that systematically connects layer-wise noise injection strategies with their im…

Ultrafast Spin Injection in Graphene via Dynamical Carrier Filtering at Transition Metal Dichalcogenide Interfaces
Shunsuke Yamada, Arqum Hashmi, Tomohito Otobe
https://arxiv.org/abs/2509.08339

Ultrafast Spin Injection in Graphene via Dynamical Carrier Filtering at Transition Metal Dichalcogenide Interfaces
We report a real-time first-principles study of ultrafast spin injection in a WSe$_2$-graphene heterobilayer under circularly polarized laser irradiation, using time-dependent density functional theory. Contrary to conventional expectations, spin transfer into graphene is not a passive process but is actively driven by spin-selective carrier filtering at the interface. Spin-polarized carriers generated in the WSe$_2$ layer induce a preferential migration of opposite-spin carriers from graphene,…

3D Mapping of Defects and Moir\'e Corrugations via Electron Ptychography Atomic Coordinate Retrieval
Jeffrey Huang, Yichao Zhang, Sang hyun Bae, Ballal Ahammed, Elif Ertekin, Pinshane Y. Huang
https://arxiv.org/abs/2509.07140

3D Mapping of Defects and Moiré Corrugations via Electron Ptychography Atomic Coordinate Retrieval
Defects and reconstructions in 2D moiré materials cause out-of-plane deformations which strongly modify their electronic properties but are difficult to experimentally access. Here, we solve the 3D atomic coordinates of twisted bilayer WSe$_2$ with picometer-scale accuracy using multislice electron ptychography (MEP) acquired from a single orientation. The resulting atomic models individually visualize each of the six atomic planes, revealing the curvature of each WSe$_2$ layer, variations in …

A Comparative Analysis of Contextual Representation Flow in State-Space and Transformer Architectures
Nhat M. Hoang, Do Xuan Long, Cong-Duy Nguyen, Min-Yen Kan, Luu Anh Tuan
https://arxiv.org/abs/2510.06640

A Comparative Analysis of Contextual Representation Flow in State-Space and Transformer Architectures
State Space Models (SSMs) have recently emerged as efficient alternatives to Transformer-Based Models (TBMs) for long-sequence processing, offering linear scaling and lower memory use. Yet, how contextual information flows across layers and tokens in these architectures remains understudied. We present the first unified, token- and layer-level analysis of representation propagation in SSMs and TBMs. Using centered kernel alignment, stability metrics, and probing, we characterize how representat…

Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Models
Ej Zhou, Caiqi Zhang, Tiancheng Hu, Chengzu Li, Nigel Collier, Ivan Vuli\'c, Anna Korhonen
https://arxiv.org/abs/2510.03136

Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Models
Confidence calibration, the alignment of a model's predicted confidence with its actual accuracy, is crucial for the reliable deployment of Large Language Models (LLMs). However, this critical property remains largely under-explored in multilingual contexts. In this work, we conduct the first large-scale, systematic studies of multilingual calibration across six model families and over 100 languages, revealing that non-English languages suffer from systematically worse calibration. To diagnose …

Finding the temperature window for atomic layer deposition of ruthenium metal via efficient phonon calculations
Alexandr Fonari, Simon D. Elliott, Casey N. Brock, Yan Li, Jacob Gavartin, Mathew D. Halls
https://arxiv.org/abs/2510.03975

Finding the temperature window for atomic layer deposition of ruthenium metal via efficient phonon calculations
We investigate the use of first principles thermodynamics based on periodic density functional theory (DFT) to examine the gas-surface chemistry of an oxidized ruthenium surface reacting with hydrogen gas. This reaction system features in the growth of ultrathin Ru films by atomic layer deposition (ALD). We reproduce and rationalize the experimental observation that ALD of the metal from RuO4 and H2 occurs only in a narrow temperature window above 100°C, and this validates the approach. Specif…

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…

Proof-Carrying Numbers (PCN): A Protocol for Trustworthy Numeric Answers from LLMs via Claim Verification
Aivin V. Solatorio
https://arxiv.org/abs/2509.06902 https://

Proof-Carrying Numbers (PCN): A Protocol for Trustworthy Numeric Answers from LLMs via Claim Verification
Large Language Models (LLMs) as stochastic systems may generate numbers that deviate from available data, a failure known as \emph{numeric hallucination}. Existing safeguards -- retrieval-augmented generation, citations, and uncertainty estimation -- improve transparency but cannot guarantee fidelity: fabricated or misquoted values may still be displayed as if correct. We propose \textbf{Proof-Carrying Numbers (PCN)}, a presentation-layer protocol that enforces numeric fidelity through mechanic…

Growth of few-layer molecular crystals of PTCDI on hexagonal boron nitride by microspacing air-gap sublimation
Nils LeCoutre, Tolibjon Abdurakhmonov, Paul Weinbrenner, Kenji Watanabe, Takashi Taniguchi, Tobias Korn, Franziska Fennel, Oliver K\"uhn, Friedemann Reinhard
https://arxiv.org/abs/2508.04591

Growth of few-layer molecular crystals of PTCDI on hexagonal boron nitride by microspacing air-gap sublimation
Extended two-dimensional (2D) crystals of dye molecules adsorbed on 2D material substrates like boron nitride have recently become a subject of intense study, with potential applications ranging from quantum technology to optoelectronics. The most established technique for the production of these films is physical vapor transport in vacuum. We demonstrate that few-layer crystalline films of the organic dye molecule PTCDI on boron nitride can be produced by microspacing in-air sublimation, a rad…