Tootfinder

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

Near-future rocket launches could slow ozone recovery: #launches would start harming the ozone layer – new research: https://theconversation.com/a-10-fold-increase-in-rocket-launches-would-start-harming-the-ozone-layer-new-research-257480

A Survey on Artificial Noise for Physical Layer Security: Opportunities, Technologies, Guidelines, Advances, and Trends
Hong Niu, Yue Xiao, Xia Lei, Jiangong Chen, Zhihan Xiao, Mao Li, Chau Yuen
https://arxiv.org/abs/2507.06500

A Survey on Artificial Noise for Physical Layer Security: Opportunities, Technologies, Guidelines, Advances, and Trends
Due to the broadcast nature of wireless communications, physical-layer security has attracted increasing concerns from both academia and industry. Artificial noise (AN), as one of the promising physical-layer security techniques, is capable of utilizing the spatial degree-of-freedom of channels to effectively enhance the security of wireless communications. In contrast to other physicallayer security techniques, the key distinguishing feature of AN is to generate specific interfering signals ac…

Layer Cake Representations for Quantum Divergences
Po-Chieh Liu, Christoph Hirche, Hao-Chung Cheng
https://arxiv.org/abs/2507.07065 https://

Layer Cake Representations for Quantum Divergences
Defining suitable quantum extensions of classical divergences often poses a challenge due to the non-commutative nature of quantum information. In this work, we propose a new approach via what we call the layer cake representation. The resulting quantum Rényi and $f$-divergences are then proven to be equivalent to those recently defined via integral representations. Nevertheless, the approach can provide several insights. We give an alternative proof of the integral representation of the relat…

Leveraging Power Amplifier Distortion for Physical Layer Security
Reza Ghasemi Alavicheh, Thomas Feys, MD Arifur Rahman, Fran\c{c}ois Rottenberg
https://arxiv.org/abs/2507.07567

Leveraging Power Amplifier Distortion for Physical Layer Security
This paper introduces a new approach to physical layer security (PLS) by leveraging power amplifier (PA) nonlinear distortion through distortion-aware precoding. While some conventional PLS techniques inject artificial noise orthogonal to legitimate channels, we demonstrate that inherent PA nonlinearities typically considered undesirable can be exploited to enhance security. The zero 3rd order (Z3RO) precoder applies a negative polarity to several antennas to cancel the PA distortion at the use…

Input Conditioned Layer Dropping in Speech Foundation Models
Abdul Hannan, Daniele Falavigna, Alessio Brutti
https://arxiv.org/abs/2507.07954 https://

Input Conditioned Layer Dropping in Speech Foundation Models
Curating foundation speech models for edge and IoT settings, where computational resources vary over time, requires dynamic architectures featuring adaptable reduction strategies. One emerging approach is layer dropping ($\mathcal{LD}$) which skips fraction of the layers of a backbone network during inference to reduce the computational load. This allows transforming static models into dynamic ones. However, existing approaches exhibit limitations either in the mode of selecting layers or by si…

On the Effect of Uncertainty on Layer-wise Inference Dynamics
Sunwoo Kim, Haneul Yoo, Alice Oh
https://arxiv.org/abs/2507.06722 https://

On the Effect of Uncertainty on Layer-wise Inference Dynamics
Understanding how large language models (LLMs) internally represent and process their predictions is central to detecting uncertainty and preventing hallucinations. While several studies have shown that models encode uncertainty in their hidden states, it is underexplored how this affects the way they process such hidden states. In this work, we demonstrate that the dynamics of output token probabilities across layers for certain and uncertain outputs are largely aligned, revealing that uncerta…

Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs
Ziyue Li, Yang Li, Tianyi Zhou
https://arxiv.org/abs/2507.07996 https://arxiv.org/pdf/2507.07996 https://arxiv.org/html/2507.07996
arXiv:2507.07996v1 Announce Type: new
Abstract: Can a pretrained neural network adapt its architecture to different inputs without any finetuning? Do we need all layers for simple tasks, and are they adequate for challenging tasks? We found that the layers of a pretrained large language model (LLM) can be manipulated as separate modules to build a better and even shallower model customized for each test sample. In particular, each layer from the pretrained model can be skipped/pruned or repeated multiple times as recurrent neural networks (RNN), and stacked with others in arbitrary orders, yielding a chain-of-layers (CoLa) per sample. This compositional space greatly expands the scope of existing works on looped/recurrent pretrained modules, layer pruning, or early-exit networks. We develop a Monte Carlo Tree Search (MCTS) protocol to explore and identify the optimal CoLa for each sample from math and commonsense reasoning benchmarks. Compared to a static model of a fixed depth, CoLa allows shortcut paths (fast thinking), recurrence of the same layer(s) (slow thinking), and combining both, offering more flexible, dynamic architectures for different inputs. We conduct an extensive analysis of the MCTS-optimized CoLa, which leads to two key findings: (1) For >75% of samples with correct predictions by the original LLM, we can find shorter CoLa, suggesting a large space for improving inference efficiency; (2) For >60% of samples with originally incorrect predictions, we can identify CoLa achieving correct predictions, suggesting a large space of performance enhancement. Our results highlight the shortcomings of using a fixed architecture of pre-trained LLMs for inference on different samples and pave the way to unlock the generalization power of test-time depth adaptation.
toXiv_bot_toot

This https://arxiv.org/abs/2505.18132 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models
Large vision models (LVM) based gait recognition has achieved impressive performance. However, existing LVM-based approaches may overemphasize gait priors while neglecting the intrinsic value of LVM itself, particularly the rich, distinct representations across its multi-layers. To adequately unlock LVM's potential, this work investigates the impact of layer-wise representations on downstream recognition tasks. Our analysis reveals that LVM's intermediate layers offer complementary properties a…

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

why does kicad have this insistence on putting PCB artwork on sheets
nobody does that. nobody wants to have PCBs on A4 sheets on paper. nobody wants to have them printed out layer by layer, it's 2025, come on

This https://arxiv.org/abs/2309.04154 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Modeling, control, and stiffness regulation of layer jamming-based continuum robots
Continuum robots with variable compliance have gained significant attention due to their adaptability in unstructured environments. Among various stiffness modulation techniques, layer jamming (LJ) provides a simple yet effective approach for achieving tunable stiffness. However, most existing LJ-based continuum robot models rely on static or quasi-static approximations, lacking a rigorous control-oriented dynamical formulation. Consequently, they are unsuitable for real-time control tasks requ…

'Sovereign AI' is just political branding, signaling to nationalist impulses while continuing to pour money into big tech firms. It's a mask for a sucker's game.
"This reveals a new kind of digital colonialism. Not one where countries are denied access, but one where they are structurally bound into dependencies across every layer of the AI stack. A European lab may host its own weights on a data center in France, but that center runs on American hardware, software…

‘Sovereign AI’ is political branding. The reality is closer to digital colonialism
The harder nations push for AI independence, the deeper their dependencies become.

Explaining Surface Layer Theory Departures in Marine Flux Profiles with Data-Driven Discovery
Jack Foxabbott, Leo Mckee-Reid, Andrew Cusick, Robbie McCorkell, Jugal Patel, Jamie Rumbelow, Jessica Rumbelow, Zohreh Shams, Arush Tagade, Patrick Hawbecker, Sue Ellen Haupt
https://arxiv.org/abs/2507.06425

Explaining Surface Layer Theory Departures in Marine Flux Profiles with Data-Driven Discovery
Monin--Obukhov Similarity Theory (MOST), which underpins nearly all bulk estimates of surface fluxes in the atmospheric surface layer, assumes monotonic wind profiles and vertically uniform momentum and heat fluxes. Here, we show that conditions frequently arise in coastal marine settings where these assumptions do not hold. Using flux measurements from the Coastal Land-Air-Sea Interaction (CLASI) project's Air-Sea Interaction Spar (ASIS) buoys with wind and flux measurements at typically ~3m a…

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…

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

Enhanced superconductivity via layer differentiation in trilayer Hubbard model
Xun Liu, Mi Jiang
https://arxiv.org/abs/2507.06614 https://

Enhanced superconductivity via layer differentiation in trilayer Hubbard model
Motivated by the highest superconducting transition temperature ($T_c$) in multilayer cuprates,we investigated the trilayer Hubbard model by adopting the large-scale dynamical cluster quantum Monte Carlo simulations. Focusing on the systems with higher hole dopings within the two outer layers (OL) than the inner layer (IL), which is believed to be relevant to the realistic multilayer cuprates, our exploration discovered that the IL and OL manifest strong differentiation in a wide range of hole …

good morning.
#SanFrancisco

Boosting Parameter Efficiency in LLM-Based Recommendation through Sophisticated Pruning
Shanle Zheng, Keqin Bao, Jizhi Zhang, Yang Zhang, Fuli Feng, Xiangnan He
https://arxiv.org/abs/2507.07064

Boosting Parameter Efficiency in LLM-Based Recommendation through Sophisticated Pruning
LLM-based recommender systems have made significant progress; however, the deployment cost associated with the large parameter volume of LLMs still hinders their real-world applications. This work explores parameter pruning to improve parameter efficiency while maintaining recommendation quality, thereby enabling easier deployment. Unlike existing approaches that focus primarily on inter-layer redundancy, we uncover intra-layer redundancy within components such as self-attention and MLP modules…

Fast Forward and Inverse Thermal Modeling for Parameter Estimation of Multi-Layer composites -- Part I: Forward Modeling
Gan Fu, Calina Ciuhu, Mitrofan Curti, Elena A. Lomonova
https://arxiv.org/abs/2507.06727

Fast Forward and Inverse Thermal Modeling for Parameter Estimation of Multi-Layer composites -- Part I: Forward Modeling
This study presents fast and accurate analytical methods for transient thermal modeling in multi-layer composites with an arbitrary number of layers. The proposed approach accounts for internal heat generation and non-homogeneities in the heat diffusion equation. The separation of variables (SOV) method is employed to decouple spatial and temporal components, enabling the determination of eigenvalues. The orthogonal expansion (OE) technique is then applied to compute Fourier coefficients using …

Orbital Hall Effect Enables Field-Free Magnetization Reversal in Ferrimagnets without Additional Conversion Layer
Zelalem Abebe Bekele, Kun Lei, Xiukai Lan, Xiangyu Liu, Hui Wen, Weihao Li, Yongcheng Deng, Wenkai Zhu, Kaiming Cai, Kaiyou Wang
https://arxiv.org/abs/2506.07608

Orbital Hall Effect Enables Field-Free Magnetization Reversal in Ferrimagnets without Additional Conversion Layer
The spin Hall effect (SHE) enables efficient electrical manipulation of magnetization through the spin Hall current \left(\mathbit{J}_{\mathbit{SHE}}\right), advancing energy-efficient spintronics. In parallel, the orbital Hall effect (OHE) offers an alternative pathway to SHE for converting charge current into an angular momentum flow. In this study, we demonstrate field-free current-induced perpendicular ferrimagnetic deterministic switching within a Mo/CoGd device without an additional orbit…

Just playing a bit of Dwarf Fortress in a biome where it rains poisonous mucus, so the surface is kind of tricky.
All my grazing animals died before I could reach a cavern layer (my incompetence).
Traders came but brought no food.
The only thing my dwarves have eaten for 2 years is turkey eggs.
Hopefully I can sort this out before they are all clad head to toe only in turkey leather.
Got some pigs but they take ages to grow to adulthood.
Spirits are amazing…

Superheating and melting phenomena of a vibrated granular layer of cubic particles
Francisco L\'opez-Gonz\'alez, Gustavo M. Rodr\'iguez-Li\~n\'an, Fernando Donado, Felipe Pacheco V\'azquez, Luis Fernando Elizondo-Aguilera
https://arxiv.org/abs/2507.07305

Superheating and melting phenomena of a vibrated granular layer of cubic particles
We report the combined results of experiments and molecular dynamics simulations conducted to investigate superheating phenomena in vertically vibrated granular matter. Specifically, we consider a system of cubic particles densely packed in a square-lattice array and subjected to different shaking strengths, denoted by Gamma, approaching a critical value Gamma_c. Below Gamma_c, the superheated crystalline array remains indefinitely stable. Above Gamma_c, it transitions progressively into a gran…

A Comparative Analyses Of Network Formation In Low-power Lossy Networks: ContikiMAC vs Orchestra-enabled TSCH
Heerok Banerjee
https://arxiv.org/abs/2506.06688

A Comparative Analyses Of Network Formation In Low-power Lossy Networks: ContikiMAC vs Orchestra-enabled TSCH
Medium Access Control (MAC) layer protocols are the underlying paradigms which dictate the transmission & reception of data in any network. Particularly for Low-powered Lossy Networks (LLNs), the design and selection of appropiate MAC-layer protocols is crucial inorder to satisfy several networking objectives such as joining time, network lifetime, energy consumption, end-to-end-delay, etc. In this report, we have presented a comparative analysis between Contiki-MAC and Orchestra-enabled TSCH p…

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…

Does Movable Antenna Present A Dual-edged Nature? From the Perspective of Physical Layer Security: A Joint Design of Fixed-position Antenna and Movable Antenna
Kan Yu, Wenxu Wang, Xiaowu Liu, Yujia Zhao, Qixun Zhang, Zhiyong Feng, Dong Li
https://arxiv.org/abs/2507.05784

Does Movable Antenna Present A Dual-edged Nature? From the Perspective of Physical Layer Security: A Joint Design of Fixed-position Antenna and Movable Antenna
In conventional artificial noise (AN)-aided physical-layer security systems, fixed-position antenna (FPA) arrays exhibit inherent vulnerability to coverage gaps due to their static spatial configuration. Adversarial eavesdroppers can strategically exploit their mobility to infiltrate these spatial nulls of AN radiation patterns, thereby evading interference suppression and successfully intercepting the confidential communication. To overcome this limitation, in this paper, we investigate a hybr…

@… Hi Sean. I’ve been doing science all night, and I think I’ve uncovered a new layer to Autechre (the “tapestry behind the cotton wool”, as Virgina Woolf might put it.) Please bear with me as I explain:
A = 1
U = 21
T = 20
E = 5
C = 3
H = 8
R = 18
E = 5
− [1, 21, 20, 5, 3, 8, 18, 5]
Sum = 81
Your name, Se…

Federated Learning-based MARL for Strengthening Physical-Layer Security in B5G Networks
Deemah H. Tashman, Soumaya Cherkaoui, Walaa Hamouda
https://arxiv.org/abs/2507.06997

Federated Learning-based MARL for Strengthening Physical-Layer Security in B5G Networks
This paper explores the application of a federated learning-based multi-agent reinforcement learning (MARL) strategy to enhance physical-layer security (PLS) in a multi-cellular network within the context of beyond 5G networks. At each cell, a base station (BS) operates as a deep reinforcement learning (DRL) agent that interacts with the surrounding environment to maximize the secrecy rate of legitimate users in the presence of an eavesdropper. This eavesdropper attempts to intercept the confid…

Electronic transport and anti-super-Klein tunneling in few-layer black phosphorous
Jorge Alfonso Lizarraga-Brito, Armando Arciniega-Guti\'errez, Yonatan Betancur-Ocampo, Thomas Stegmann
https://arxiv.org/abs/2507.05462

Electronic transport and anti-super-Klein tunneling in few-layer black phosphorous
The electronic transport in few-layer black phosphorus (FLBP) nanoribbons is studied theoretically. The system is modeled on the basis of band-structures, which have been measured recently by $μ$-ARPES experiments. We show that the anisotropic bands of FLBP leads to highly anisotropic transport properties; while the current in one direction can be rather focused, it can be strongly disperse in the orthogonal direction. The low-energy current is carried mainly in the central layer due to the ve…

Boundary-layer modeling of polymer-based acoustofluidic devices
Sazid Z. Hoque, Henrik Bruus
https://arxiv.org/abs/2507.05924 https://

Boundary-layer modeling of polymer-based acoustofluidic devices
In fluid-filled microchannels embedded in solid devices and driven by MHz ultrasound transducers, the thickness of the viscous boundary layer in the fluid near the confining walls is typically 3 to 4 orders of magnitude smaller than the acoustic wavelength and 5 orders of magnitude smaller than the longest dimension of the device. This large span in length scale renders direct numerical simulations of such devices prohibitively expensive in terms of computer memory requirements, and consequentl…

This https://arxiv.org/abs/2506.03940 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Depermissioning Web3: a Permissionless Accountable RPC Protocol for Blockchain Networks
In blockchain networks, so-called "full nodes" serve data to and relay transactions from clients through an RPC interface. This serving layer enables integration of "Web3" data, stored on blockchains, with "Web2" mobile or web applications that cannot directly participate as peers in a blockchain network. In practice, the serving layer is dominated by a small number of centralized services ("node providers") that offer permissioned access to RPC endpoints. Clients register with these providers …

Solace! Spirit cleansing.
(Incredible light on our morning hike today. So very grateful for these experiences, especially these days... 🙏)
#LandscapePhotography #NaturePhotography #MorningWalk

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 …

Evaluation of Machine Learning Models in Student Academic Performance Prediction
A. G. R. Sandeepa, Sanka Mohottala
https://arxiv.org/abs/2506.08047 https:…

Evaluation of Machine Learning Models in Student Academic Performance Prediction
This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train…

Tilings of the sphere by congruent pentagons V: Edge combination $a^{4}b$ with rational angles
Jinjin Liang, Yixi Liao, Wenchuan Hu, Erxiao Wang
https://arxiv.org/abs/2507.07038

Tilings of the sphere by congruent pentagons V: Edge combination $a^{4}b$ with rational angles
We classify edge-to-edge tilings of the sphere by congruent pentagons with the edge combination $a^4b$ and with rational angles in degree: they are a one-parameter family of symmetric $a^4b$-pentagonal subdivisions of the tetrahedron with $12$ tiles; a sequence of unique symmetric $a^4b$-pentagons admitting a symmetric $3$-layer earth map tiling by $4m$ tiles for any $m\ge4$, among which each odd $m$ case admits two standard flip modifications; and a unique non-symmetric and degenerate $a^4b$-p…

Replaced article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new/
[1/1]:
Variable Rate Learned Wavelet Video Coding using Temporal Layer Adaptivity

Estimation of Electron Screening Potential in the 6Li(d,{\alpha})4He Reaction Using Multi-Layer Perceptron Neural Network
D. Chattopadhyay
https://arxiv.org/abs/2506.08044

Estimation of Electron Screening Potential in the 6Li(d,α)4He Reaction Using Multi-Layer Perceptron Neural Network
Reactions between light charged nuclei at sub-Coulomb energies are crucial in astrophysical environments, but accurate cross-section measurements are hindered by electron screening. Traditional methods, including polynomial extrapolation and the Trojan Horse Method, often yield screening potentials exceeding adiabatic predictions. Building on the success of an MLP-based Artificial Neural Network (ANN) for the 6Li(p, α)3He reaction [1], this work applies the same approach to the 6Li(d, α)4He r…

Gradient flow in the kernel learning problem
Yang Li, Feng Ruan
https://arxiv.org/abs/2506.08550 https://arxiv.org/pdf/2506.08550

Gradient flow in the kernel learning problem
This is a sequel to our paper `On the kernel learning problem'. We identify a canonical choice of Riemannian gradient flow, to find the stationary points in the kernel learning problem. In the presence of Gaussian noise variables, this flow enjoys the remarkable property of having a continuous family of Lyapunov functionals, and the interpretation is the automatic reduction of noise. PS. We include an extensive discussion in the postcript explaining the comparison with the 2-layer neural netw…

Fast Forward and Inverse Thermal Modeling for Parameter Estimation of Multi-Layer Composites -- Part II: Inverse Modeling and Applications
Gan Fu, Mitrofan Curti, Calina Ciuhu, Elena A. Lomonova
https://arxiv.org/abs/2507.06746

Fast Forward and Inverse Thermal Modeling for Parameter Estimation of Multi-Layer Composites -- Part II: Inverse Modeling and Applications
A fast inverse heat conduction model (IHCM) is developed for estimating unknown properties of multi-layer composites considering internal heat generation. This work builds on the validated analytical forward models presented in Part I. Transient temperature at a single point is used as input, with the objective function minimized through an interior-point optimization algorithm. The IHCM accurately estimates thermal properties such as thermal conductivity, specific heat capacity, density, and h…

Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gases
Axel TahmasebiMoradi, Vincent Ren, Benjamin Le-Creurer, Chetra Mang
https://arxiv.org/abs/2506.08033

Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gases
Aiming to reduce the computational cost of numerical simulations, a convolutional neural network (CNN) and a multi-layer perceptron (MLP) are introduced to build a surrogate model to approximate radiative heat transfer solutions in a 2-D walled domain with participative gases. The originality of this work lays in the adaptation of the inputs of the problem (gas and wall properties) in order to fit with the CNN architecture, more commonly used for image processing. Two precision datasets have be…

The role of gain neuromodulation in layer-5 pyramidal neurons
Alejandro Rodriguez-Garcia, Christopher J. Whyte, Brandon R. Munn, Jie Mei, James M. Shine, Srikanth Ramaswamy
https://arxiv.org/abs/2507.03222

The role of gain neuromodulation in layer-5 pyramidal neurons
Biological and artificial learning systems alike confront the plasticity-stability dilemma. In the brain, neuromodulators such as acetylcholine and noradrenaline relieve this tension by tuning neuronal gain and inhibitory gating, balancing segregation and integration of circuits. Fed by dense cholinergic and noradrenergic projections from the ascending arousal system, layer-5 pyramidal neurons in the cerebral cortex offer a relevant substrate for understanding these dynamics. When distal dendri…

POLARON: Precision-aware On-device Learning and Adaptive Runtime-cONfigurable AI acceleration
Mukul Lokhande, Santosh Kumar Vishvakarma
https://arxiv.org/abs/2506.08785

POLARON: Precision-aware On-device Learning and Adaptive Runtime-cONfigurable AI acceleration
The increasing complexity of AI models requires flexible hardware capable of supporting diverse precision formats, particularly for energy-constrained edge platforms. This work presents PARV-CE, a SIMD-enabled, multi-precision MAC engine that performs efficient multiply-accumulate operations using a unified data-path for 4/8/16-bit fixed-point, floating point, and posit formats. The architecture incorporates a layer adaptive precision strategy to align computational accuracy with workload sensi…

This https://arxiv.org/abs/2506.05588 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
Reservoir computing (RC) has attracted attention as an efficient recurrent neural network architecture due to its simplified training, requiring only its last perceptron readout layer to be trained. When implemented with memristors, RC systems benefit from their dynamic properties, which make them ideal for reservoir construction. However, achieving high performance in memristor-based RC remains challenging, as it critically depends on the input preprocessing method and reservoir size. Despite …

Resurgence of CO in a warm bubble around accreting protoplanets and its observability
O. Chrenko, S. Casassus, R. O. Chametla
https://arxiv.org/abs/2507.03370

Resurgence of CO in a warm bubble around accreting protoplanets and its observability
The cold outer regions of protoplanetary disks are expected to contain a midplane-centered layer in which gas-phase CO molecules freeze-out and their overall abundance is low. The layer then manifests itself as a void in the channel maps of CO rotational emission lines. We explore whether the frozen-out layer can expose the circumplanetary environment of embedded accreting protoplanets to observations. To this end, we performed 3D radiative gas-dust hydrodynamic simulations, directly linking op…

Mathematical Framework of Subwavelength Resonance in Elastic Metascreen
Wei Wu, Youzi He
https://arxiv.org/abs/2507.07837 https://arx…

Mathematical Framework of Subwavelength Resonance in Elastic Metascreen
In this paper we provide a mathematical framework based on asymptotic analysis of layer potential operators for determining the existence of subwavelength resonance in elastic metascreen and resonance frequency calculation. We consider the case of high shear modulus contrast between metascreen inclusions and background medium. When the shear modulus of inclusions tends to infinity, we prove the occurrence of subwavelength resonance and demonstrate the dramatic change of scattered field due to s…

Sharp uniform approximation for spectral Barron functions by deep neural networks
Yulei Liao, Pingbing Ming, Hao Yu
https://arxiv.org/abs/2507.06789 https:…

Sharp uniform approximation for spectral Barron functions by deep neural networks
This work explores the neural network approximation capabilities for functions within the spectral Barron space $\mathscr{B}^s$, where $s$ is the smoothness index. We demonstrate that for functions in $\mathscr{B}^{1/2}$, a shallow neural network (a single hidden layer) with $N$ units can achieve an $L^p$-approximation rate of $\mathcal{O}(N^{-1/2})$. This rate also applies to uniform approximation, differing by at most a logarithmic factor. Our results significantly reduce the smoothness requi…

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…

Theoretical Learning Performance of Graph Neural Networks: The Impact of Jumping Connections and Layer-wise Sparsification
Jiawei Sun, Hongkang Li, Meng Wang
https://arxiv.org/abs/2507.05533

Theoretical Learning Performance of Graph Neural Networks: The Impact of Jumping Connections and Layer-wise Sparsification
Jumping connections enable Graph Convolutional Networks (GCNs) to overcome over-smoothing, while graph sparsification reduces computational demands by selecting a sub-matrix of the graph adjacency matrix during neighborhood aggregation. Learning GCNs with graph sparsification has shown empirical success across various applications, but a theoretical understanding of the generalization guarantees remains limited, with existing analyses ignoring either graph sparsification or jumping connections.…

LRScheduler: A Layer-aware and Resource-adaptive Container Scheduler in Edge Computing
Zhiqing Tang, Wentao Peng, Jianxiong Guo, Jiong Lou, Hanshuai Cui, Tian Wang, Yuan Wu, Weijia Jia
https://arxiv.org/abs/2506.03694

LRScheduler: A Layer-aware and Resource-adaptive Container Scheduler in Edge Computing
Lightweight containers provide an efficient approach for deploying computation-intensive applications in network edge. The layered storage structure of container images can further reduce the deployment cost and container startup time. Existing researches discuss layer sharing scheduling theoretically but with little attention paid to the practical implementation. To fill in this gap, we propose and implement a Layer-aware and Resource-adaptive container Scheduler (LRScheduler) in edge computin…

Don't mind me, just over here escape-routing this BGA inside out

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…

🇺🇦 #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

Optimizing Cognitive Networks: Reinforcement Learning Meets Energy Harvesting Over Cascaded Channels
Deemah H. Tashman, Soumaya Cherkaoui, Walaa Hamouda
https://arxiv.org/abs/2507.06981

Optimizing Cognitive Networks: Reinforcement Learning Meets Energy Harvesting Over Cascaded Channels
This paper presents a reinforcement learning (RL) based approach to improve the physical layer security (PLS) of an underlay cognitive radio network (CRN) over cascaded channels. These channels are utilized in highly mobile networks such as cognitive vehicular networks (CVN). In addition, an eavesdropper aims to intercept the communications between secondary users (SUs). The SU receiver has full-duplex and energy harvesting capabilities to generate jamming signals to confound the eavesdropper a…

Error Exponents for Quantum Packing Problems via An Operator Layer Cake Theorem
Hao-Chung Cheng, Po-Chieh Liu
https://arxiv.org/abs/2507.06232 https://

Error Exponents for Quantum Packing Problems via An Operator Layer Cake Theorem
In this work, we prove a one-shot random coding bound for classical-quantum channel coding, a problem conjectured by Burnashev and Holevo in 1998. By choosing the optimal input distribution, we recover the optimal error exponent (i.e., the reliability function) of classical-quantum channels for rates above the critical rate. Our result extends to various quantum packing-type problems, including classical communication over any fully quantum channel with or without entanglement-assistance, const…

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…

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…

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…

Topological impact of nanopore electrodes on the structure of the electrical double layer and the di erential capacitance
A. Silva-Caballero, A. Lozada-Hidalgo, M. Lozada-Cassou
https://arxiv.org/abs/2506.05654

Topological impact of nanopore electrodes on the structure of the electrical double layer and the di erential capacitance
The electrical double layer for three different topologies of nanopore electrodes is studied, i.e., the interior and exterior electrical double layers of planar, cylindrical and spherical nanopores immersed into a point-ions electrolyte, and not connected to a power source, are analytically attained through the linearized Poisson-Boltzmann equation.

Regional, Lattice and Logical Representations of Neural Networks
Sandro Preto (Federal University of ABC, Brazil), Marcelo Finger (University of Sao Paulo, Brazil)
https://arxiv.org/abs/2506.05834

Regional, Lattice and Logical Representations of Neural Networks
A possible path to the interpretability of neural networks is to (approximately) represent them in the regional format of piecewise linear functions, where regions of inputs are associated to linear functions computing the network outputs. We present an algorithm for the translation of feedforward neural networks with ReLU activation functions in hidden layers and truncated identity activation functions in the output layer. We also empirically investigate the complexity of regional representati…

I share a lot of @…’s feelings here. (He doesn’t mention training data sourcing, but that’s on my mind too.)
There’s a layer of ethics and social impact that hangs, as Tim put it, as a stench over all the thinking about whether and how the tech is useful. I am actually willing to discuss the utility questions, but they are secondary.
https://cosocial.ca/@timbray/114807889514002925

A Soft Robotic Module with Pneumatic Actuation and Enhanced Controllability Using a Shape Memory Alloy Wire
Mohammadnavid Golchin
https://arxiv.org/abs/2506.05741

A Soft Robotic Module with Pneumatic Actuation and Enhanced Controllability Using a Shape Memory Alloy Wire
In this paper, a compressed air-actuated soft robotic module was developed by incorporating a shape memory alloy (SMA) wire into its structure to achieve the desired bending angle with greater precision. First, a fiber-reinforced bending module with a strain-limiting layer made of polypropylene was fabricated. The SMA wire was then placed in a silicon matrix, which was used as a new strain-limiting layer. A simple closed-loop control algorithm was used to regulate the bending angle of the soft …

Temporal Conformal Prediction (TCP): A Distribution-Free Statistical and Machine Learning Framework for Adaptive Risk Forecasting
Agnideep Aich, Ashit Baran Aich, Dipak C. Jain
https://arxiv.org/abs/2507.05470

Temporal Conformal Prediction (TCP): A Distribution-Free Statistical and Machine Learning Framework for Adaptive Risk Forecasting
We propose Temporal Conformal Prediction (TCP), a novel framework for constructing prediction intervals in financial time-series with guaranteed finite-sample validity. TCP integrates quantile regression with a conformal calibration layer that adapts online via a decaying learning rate. This hybrid design bridges statistical and machine learning paradigms, enabling TCP to accommodate non-stationarity, volatility clustering, and regime shifts which are hallmarks of real-world asset returns, with…

Movable-Antenna-Enhanced Physical-Layer Service Integration: Performance Analysis and Optimization
Xuanlin Shen, Xin Wei, Weidong Mei, Zhi Chen, Jun Fang, Boyu Ning
https://arxiv.org/abs/2507.03449

Movable-Antenna-Enhanced Physical-Layer Service Integration: Performance Analysis and Optimization
Movable antennas (MAs) have drawn increasing attention in wireless communications due to their capability to create favorable channel conditions via local movement within a confined region. In this letter, we investigate its application in physical-layer service integration (PHY-SI), where a multi-MA base station (BS) simultaneously transmits both confidential and multicast messages to two users. The multicast message is intended for both users, while the confidential message is intended only f…

Quasi-Vertical $\beta$-Ga$_2$O$_3$ Schottky Diodes on Sapphire Using All-LPCVD Growth and Plasma-Free Ga-Assisted Etching
Saleh Ahmed Khan, Ahmed Ibreljic, A F M Anhar Uddin Bhuiyan
https://arxiv.org/abs/2507.06353

Quasi-Vertical $β$-Ga$_2$O$_3$ Schottky Diodes on Sapphire Using All-LPCVD Growth and Plasma-Free Ga-Assisted Etching
This work demonstrates quasi-vertical beta-Ga2O3 Schottky barrier diodes (SBDs) fabricated on c-plane sapphire using an all-LPCVD, plasma-free process integrating epitaxial growth of high-quality beta-Ga2O3 and in-situ Ga-assisted etching. A 6.3 micron-thick (-201)-oriented beta-Ga2O3 layer was grown on c-sapphire with a 6-degree miscut, comprising a 3.15 micron moderately doped (2.1e17 cm^-3) drift layer and a heavily doped (1e19 cm^-3) contact layer on a UID buffer. Mesa isolation used Ga-ass…

Liquid and solid layers in a thermal deep learning machine
Gang Huang, Lai Shun Chan, Hajime Yoshino, Ge Zhang, Yuliang Jin
https://arxiv.org/abs/2506.06789

Liquid and solid layers in a thermal deep learning machine
Based on deep neural networks (DNNs), deep learning has been successfully applied to many problems, but its mechanism is still not well understood -- especially the reason why over-parametrized DNNs can generalize. A recent statistical mechanics theory on supervised learning by a prototypical multi-layer perceptron (MLP) on some artificial learning scenarios predicts that adjustable parameters of over-parametrized MLPs become strongly constrained by the training data close to the input/output b…

Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation
Szymon P{\l}otka, Maciej Chrabaszcz, Gizem Mert, Ewa Szczurek, Arkadiusz Sitek
https://arxiv.org/abs/2507.06363

Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation
In recent years, artificial intelligence has significantly advanced medical image segmentation. However, challenges remain, including efficient 3D medical image processing across diverse modalities and handling data variability. In this work, we introduce Hierarchical Soft Mixture-of-Experts (HoME), a two-level token-routing layer for efficient long-context modeling, specifically designed for 3D medical image segmentation. Built on the Mamba state-space model (SSM) backbone, HoME enhances seque…

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…

SiCmiR Atlas: Single-Cell miRNA Landscapes Reveals Hub-miRNA and Network Signatures in Human Cancers
Xiao-Xuan Cai, Jing-Shan Liao, Jia-Jun Ma, Yu-Xuan Pang, Yi-Gang Chen, Yang-Chi-Dung Lin, Yi-Dan Chen, Xin Cao, Yi-Cheng Zhang, Tao-Sheng Xu, Tzong-Yi Lee, Hsi-Yuan Huang, Hsien-Da Huang
https://arxiv.org/abs/2508.05692

SiCmiR Atlas: Single-Cell miRNA Landscapes Reveals Hub-miRNA and Network Signatures in Human Cancers
microRNA are pivotal post-transcriptional regulators whose single-cell behavior has remained largely inaccessible owing to technical barriers in single-cell small-RNA profiling. We present SiCmiR, a two-layer neural network that predicts miRNA expression profile from only 977 LINCS L1000 landmark genes reducing sensitivity to dropout of single-cell RNA-seq data. Proof-of-concept analyses illustrate how SiCmiR can uncover candidate hub-miRNAs in bulk-seq cell lines and hepatocellular carcinoma, …

I did not know about ggplot_build() before. It can come in handy in situations where you want to access computed metrics of a #ggplot. https://ggplot2.tidyverse.org/reference/ggplot_build.html…

Build ggplot for rendering. — ggplot_build
ggplot_build() takes the plot object, and performs all steps necessary to produce an object that can be rendered. This function outputs two pieces: a list of data frames (one for each layer), and a panel object, which contain all information about axis limits, breaks etc.

High Temperature Superconductivity Dominated by Inner Underdoped CuO$_2$ Planes in Quadruple-Layer Cuprate (Cu,C)Ba$_2$Ca$_3$Cu$_4$O$_{11 \delta}$
Xingtian Sun, Suppanut Sangphet, Nan Guo, Yu Fan, Yutong Chen, Minyinan Lei, Xue Ming, Xiyu Zhu, Hai-Hu Wen, Haichao Xu, Rui Peng, Donglai Feng
https://arxiv.org/abs/2507.03921…

High Temperature Superconductivity Dominated by Inner Underdoped CuO$_2$ Planes in Quadruple-Layer Cuprate (Cu,C)Ba$_2$Ca$_3$Cu$_4$O$_{11+δ}$
The superconducting transition temperature ($T_{\mathrm{c}}$) of trilayer or quadruple-layer cuprates typically surpasses that of single-layer or bilayer systems. This observation is often interpreted within the ``composite picture", where strong proximity effect between inner CuO$_2$ planes (IPs) and outer CuO$_2$ planes (OPs) is crucial. Albeit intriguing, a straightforward scrutinization of this composite picture is still lacking. In this study, using angle-resolved photoemission spectroscop…

This https://arxiv.org/abs/2505.17282 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Attention with Trained Embeddings Provably Selects Important Tokens
Token embeddings play a crucial role in language modeling but, despite this practical relevance, their theoretical understanding remains limited. Our paper addresses the gap by characterizing the structure of embeddings obtained via gradient descent. Specifically, we consider a one-layer softmax attention model with a linear head for binary classification, i.e., $\texttt{Softmax}( p^\top E_X^\top ) E_X v = \frac{ \sum_{i=1}^T \exp(p^\top E_{x_i}) E_{x_i}^\top v}{\sum_{j=1}^T \exp(p^\top E_{x_{j…

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…

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…

Exploring State-Space-Model based Language Model in Music Generation
Wei-Jaw Lee, Fang-Chih Hsieh, Xuanjun Chen, Fang-Duo Tsai, Yi-Hsuan Yang
https://arxiv.org/abs/2507.06674

Exploring State-Space-Model based Language Model in Music Generation
The recent surge in State Space Models (SSMs), particularly the emergence of Mamba, has established them as strong alternatives or complementary modules to Transformers across diverse domains. In this work, we aim to explore the potential of Mamba-based architectures for text-to-music generation. We adopt discrete tokens of Residual Vector Quantization (RVQ) as the modeling representation and empirically find that a single-layer codebook can capture semantic information in music. Motivated by t…

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…

Direct numerical simulation of complete transition to turbulence with a fluid at supercritical pressure
Pietro Carlo Boldini, Benjamin Bugeat, Jurriaan W. R. Peeters, Markus Kloker, Rene Pecnik
https://arxiv.org/abs/2506.06703

Direct numerical simulation of complete transition to turbulence with a fluid at supercritical pressure
The objective of this work is to investigate the unexplored laminar-to-turbulent transition of a heated flat-plate boundary layer with a fluid at supercritical pressure. Two temperature ranges are considered: a subcritical case, where the fluid remains entirely in the liquid-like regime, and a transcritical case, where the pseudo-critical (Widom) line is crossed and pseudo-boiling occurs. Fully compressible direct numerical simulations are used to study (i) the linear and nonlinear instabilitie…

Averaged models for compressible two-phase stratified flows on thin domains
Nicolas Seguin (ANGUS), Khaled Saleh (I2M, AMU), Pierrick Le Vourc'H (ANGUS)
https://arxiv.org/abs/2506.08542

Averaged models for compressible two-phase stratified flows on thin domains
This paper deals with the derivation of compressible two-phase flow models. We use a thin domain approximation of a two-layer configuration governed by the Navier-Stokes equations, following the works [H. B. Stewart and B. Wendroff, J. Comp. Phys., 56 (1984)] and [V. H. Ransom and D. L. Hicks, J. Comput. Phys., 75 (1988)]. In order to recover source terms and two-velocity models directly from this asymptotic analysis, we remove the continuity of the tangential component of the velocity at the i…

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…

Understanding and Controlling Repetition Neurons and Induction Heads in In-Context Learning
Nhi Hoai Doan, Tatsuya Hiraoka, Kentaro Inui
https://arxiv.org/abs/2507.07810

Understanding and Controlling Repetition Neurons and Induction Heads in In-Context Learning
This paper investigates the relationship between large language models' (LLMs) ability to recognize repetitive input patterns and their performance on in-context learning (ICL). In contrast to prior work that has primarily focused on attention heads, we examine this relationship from the perspective of skill neurons, specifically repetition neurons. Our experiments reveal that the impact of these neurons on ICL performance varies depending on the depth of the layer in which they reside. By comp…

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…

Thickness-Dependent Spin Pumping in YIG/W$_{90}$Ti$_{10}$ Bilayers
Marielle Hachem, Zeinab Harajli, Samih Isber, Mohammad Haidar
https://arxiv.org/abs/2507.06831

Thickness-Dependent Spin Pumping in YIG/W$_{90}$Ti$_{10}$ Bilayers
We investigate the spin pumping efficiency in YIG/YIG/W$_{90}$Ti$_{10}$ bilayers by measuring the thickness dependence of both the YIG and WTi layers using broadband ferromagnetic resonance (FMR) spectroscopy. The deposition of a 5-nm WTi layer leads to enhanced Gilbert damping in thinner YIG films, indicating efficient spin current injection. From the spin pumping contribution to the damping of the YIG/WTi bilayer, we determine an effective spin mixing conductance of $ 3.3 \times 10^{18}~\math…

Optimal transitional mechanisms of incompressible separated shear layers subject to external disturbances
Flavio Savarino, Denis Sipp, Georgios Rigas
https://arxiv.org/abs/2507.04525

Optimal transitional mechanisms of incompressible separated shear layers subject to external disturbances
Optimal transitional mechanisms are analysed for an incompressible shear layer developing over a short, pressure gradient-induced laminar separation bubble (LSB) with peak reversed flow of 2%. Although the bubble remains globally stable, the shear layer destabilises due to the amplification of external time- and spanwise-periodic disturbances. Using linear resolvent analysis (RA), we demonstrate that the pressure gradient modifies boundary layer receptivity, shifting from Tollmien-Schlichting (…

Thermochemical models of outer core convection with heterogeneous core-mantle boundary heat flux
Souvik Naskar, Jonathan E. Mound, Christopher J. Davies, Andrew T. Clarke
https://arxiv.org/abs/2507.03538

Thermochemical models of outer core convection with heterogeneous core-mantle boundary heat flux
Thermochemical convection in Earth's outer core is driven by the crystallisation of the inner core that releases latent heat and light elements. A key question in core dynamics is whether a stable layer exists just below the core-mantle boundary. Recent core convection simulations, accounting for CMB heterogeneities, propose locally stable regions (or regional inversion lenses, RILs) rather than a global layer, allowing both stable and unstable regions to coexist. In this study, we consider a s…

A Multi-Resolution Dynamic Game Framework for Cross-Echelon Decision-Making in Cyber Warfare
Ya-Ting Yang, Quanyan Zhu
https://arxiv.org/abs/2507.03021 htt…

A Multi-Resolution Dynamic Game Framework for Cross-Echelon Decision-Making in Cyber Warfare
Cyber warfare has become a critical dimension of modern conflict, driven by society's increasing dependence on interconnected digital and physical infrastructure. Effective cyber defense often requires decision-making at different echelons, where the tactical layer focuses on detailed actions such as techniques, tactics, and procedures, while the strategic layer addresses long-term objectives and coordinated planning. Modeling these interactions at different echelons remains challenging due to …

This https://arxiv.org/abs/2506.03978 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Structured Pruning for Diverse Best-of-N Reasoning Optimization
Model pruning in transformer-based language models, traditionally viewed as a means of achieving computational savings, can enhance the model's reasoning capabilities. In this work, we uncover a surprising phenomenon: the selective pruning of certain attention heads leads to improvements in reasoning performance, particularly on challenging tasks. Motivated by this observation, we propose SPRINT, a novel contrastive learning framework that dynamically selects the optimal head and layer to prune…

PANDAS: Peer-to-peer, Adaptive Networking for Data Availability Sampling within Ethereum Consensus Timebounds
Matthieu Pigaglio, Onur Ascigil, Micha{\l} Kr\'ol, Sergi Rene, Felix Lange, Kaleem Peeroo, Ramin Sadre, Vladimir Stankovic, Etienne Rivi\`ere
https://arxiv.org/abs/2507.00824…

PANDAS: Peer-to-peer, Adaptive Networking for Data Availability Sampling within Ethereum Consensus Timebounds
Layer-2 protocols can assist Ethereum's limited throughput, but globally broadcasting layer-2 data limits their scalability. The Danksharding evolution of Ethereum aims to support the selective distribution of layer-2 data, whose availability in the network is verified using randomized data availability sampling (DAS). Integrating DAS into Ethereum's consensus process is challenging, as pieces of layer-2 data must be disseminated and sampled within four seconds of the beginning of each consensu…

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…

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…

Reliable and Real-Time Highway Trajectory Planning via Hybrid Learning-Optimization Frameworks
Yujia Lu, Chong Wei, Lu Ma
https://arxiv.org/abs/2508.04436 https://

Reliable and Real-Time Highway Trajectory Planning via Hybrid Learning-Optimization Frameworks
Autonomous highway driving presents a high collision risk due to fast-changing environments and limited reaction time, necessitating reliable and efficient trajectory planning. This paper proposes a hybrid trajectory planning framework that integrates the adaptability of learning-based methods with the formal safety guarantees of optimization-based approaches. The framework features a two-layer architecture: an upper layer employing a graph neural network (GNN) trained on real-world highway dat…

Impact of rotation on synthetic mass-radius relationships of two-layer rocky planets and water worlds
J. -M. Hur\'e, P. No\'e, C. Staelen, E. Di Folco
https://arxiv.org/abs/2507.02112

Impact of rotation on synthetic mass-radius relationships of two-layer rocky planets and water worlds
We have analyzed the effects of rotation on mass-radius relationships for single-layer and two-layer planets having a core and an envelope made of pure materials among iron, perovskite and water in solid phase. The numerical surveys use the DROP code updated with a modified polytropic equation-of-state (EOS) and investigate flattening parameters $f$ up to $0.2$. In the mass range $0.1 M_\oplus < M < 10 M_\oplus$, we find that rotation systematically shifts the curves of composition towards larg…

A Theoretical Study of (Hyper) Self-Attention through the Lens of Interactions: Representation, Training, Generalization
Muhammed Ustaomeroglu, Guannan Qu
https://arxiv.org/abs/2506.06179

A Theoretical Study of (Hyper) Self-Attention through the Lens of Interactions: Representation, Training, Generalization
Self-attention has emerged as a core component of modern neural architectures, yet its theoretical underpinnings remain elusive. In this paper, we study self-attention through the lens of interacting entities, ranging from agents in multi-agent reinforcement learning to alleles in genetic sequences, and show that a single layer linear self-attention can efficiently represent, learn, and generalize functions capturing pairwise interactions, including out-of-distribution scenarios. Our analysis r…

SATversary: Adversarial Attacks on Satellite Fingerprinting
Joshua Smailes, Sebastian K\"ohler, Simon Birnbach, Martin Strohmeier, Ivan Martinovic
https://arxiv.org/abs/2506.06119

SATversary: Adversarial Attacks on Satellite Fingerprinting
As satellite systems become increasingly vulnerable to physical layer attacks via SDRs, novel countermeasures are being developed to protect critical systems, particularly those lacking cryptographic protection, or those which cannot be upgraded to support modern cryptography. Among these is transmitter fingerprinting, which provides mechanisms by which communication can be authenticated by looking at characteristics of the transmitter, expressed as impairments on the signal. Previous works s…

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…

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias
Yuanzhe Hu, Kinshuk Goel, Vlad Killiakov, Yaoqing Yang
https://arxiv.org/abs/2506.06280

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias
Diagnosing deep neural networks (DNNs) through the eigenspectrum of weight matrices has been an active area of research in recent years. At a high level, eigenspectrum analysis of DNNs involves measuring the heavytailness of the empirical spectral densities (ESD) of weight matrices. It provides insight into how well a model is trained and can guide decisions on assigning better layer-wise training hyperparameters. In this paper, we address a challenge associated with such eigenspectrum methods:…

An Effective Equivalence Model of Analyzing PLS of Multiple Eavesdroppers Facing Low-altitude Communication Systems
Yujia Zhao, Zhiyong Feng, Kan Yu, Qixun Zhang, Dong Li
https://arxiv.org/abs/2507.05878

An Effective Equivalence Model of Analyzing PLS of Multiple Eavesdroppers Facing Low-altitude Communication Systems
In low-altitude wireless communications, the increased complexity of wireless channels and the uncertainty of eavesdroppers (Eves)--caused by diverse altitudes, speeds, and obstacles--pose significant challenges to physical layer security (PLS) technologies based on fixed-position antennas (FPAs), particularly in terms of beamforming capabilities and spatial efficiency. In contrast, movable antennas (MAs) offer a flexible solution by enabling channel reconstruction through antenna movement, eff…

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…

CAVGAN: Unifying Jailbreak and Defense of LLMs via Generative Adversarial Attacks on their Internal Representations
Xiaohu Li, Yunfeng Ning, Zepeng Bao, Mayi Xu, Jianhao Chen, Tieyun Qian
https://arxiv.org/abs/2507.06043

CAVGAN: Unifying Jailbreak and Defense of LLMs via Generative Adversarial Attacks on their Internal Representations
Security alignment enables the Large Language Model (LLM) to gain the protection against malicious queries, but various jailbreak attack methods reveal the vulnerability of this security mechanism. Previous studies have isolated LLM jailbreak attacks and defenses. We analyze the security protection mechanism of the LLM, and propose a framework that combines attack and defense. Our method is based on the linearly separable property of LLM intermediate layer embedding, as well as the essence of j…

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism
Deifilia Kieckhefen, Markus G\"otz, Lars H. Heyen, Achim Streit, Charlotte Debus
https://arxiv.org/abs/2507.05753

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism
AI-based methods have revolutionized atmospheric forecasting, with recent successes in medium-range forecasting spurring the development of climate foundation models. Accurate modeling of complex atmospheric dynamics at high spatial resolutions and longer lead times requires large neural networks and gigabyte-sized data samples, making accelerator memory and I/O-bandwidth the bottlenecks for model training. We introduce WeatherMixer, a multi-layer-perceptron-based architecture whose workload sc…

Hierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning
Hongbao Li, Ziye Jia, Sijie He, Kun Guo, Qihui Wu
https://arxiv.org/abs/2507.05722

Hierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning
With the emergence of compute-intensive and delay-sensitive applications in vehicular networks, unmanned aerial vehicles (UAVs) have emerged as a promising complement for vehicular edge computing due to the high mobility and flexible deployment. However, the existing UAV-assisted offloading strategies are insufficient in coordinating heterogeneous computing resources and adapting to dynamic network conditions. Hence, this paper proposes a dual-layer UAV-assisted edge computing architecture base…

Cooperative Mapping, Localization, and Beam Management via Multi-Modal SLAM in ISAC Systems
Hang Que, Jie Yang, Tao Du, Shuqiang Xia, Chao-Kai Wen, Shi Jin
https://arxiv.org/abs/2507.05718

Cooperative Mapping, Localization, and Beam Management via Multi-Modal SLAM in ISAC Systems
Simultaneous localization and mapping (SLAM) plays a critical role in integrated sensing and communication (ISAC) systems for sixth-generation (6G) millimeter-wave (mmWave) networks, enabling environmental awareness and precise user equipment (UE) positioning. While cooperative multi-user SLAM has demonstrated potential in leveraging distributed sensing, its application within multi-modal ISAC systems remains limited, particularly in terms of theoretical modeling and communication-layer integra…