Tootfinder

lkml_reply: Linux kernel reply network
A network of replies among email address found on the Linux kernel mailing list. Nodes are email addresses and each directed edge represents a reply from email address i to email address j. The date of this snapshot is uncertain.
This network has 63399 nodes and 1096440 edges.
Tags: Social, Communication, Unweighted, Multigraph, Timestamps

Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Afra Kilic, Kim Batselier
https://arxiv.org/abs/2507.11136 …

Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Tensor Network (TN) Kernel Machines speed up model learning by representing parameters as low-rank TNs, reducing computation and memory use. However, most TN-based Kernel methods are deterministic and ignore parameter uncertainty. Further, they require manual tuning of model complexity hyperparameters like tensor rank and feature dimensions, often through trial-and-error or computationally costly methods like cross-validation. We propose Bayesian Tensor Network Kernel Machines, a fully probabil…

Eh, that's it, I guess. No 300 million dollar unveiling event for this
kernel, I'm afraid, but you're still supposed to think of this as the
"happening of the century" (at least until the next kernel comes along).
-- Linus, in the announcement for 1.3.27

Automatic reproducing kernel and regularization for learning convolution kernels
Haibo Li, Fei Lu
https://arxiv.org/abs/2507.11944 https://

Automatic reproducing kernel and regularization for learning convolution kernels
Learning convolution kernels in operators from data arises in numerous applications and represents an ill-posed inverse problem of broad interest. With scant prior information, kernel methods offer a natural nonparametric approach with regularization. However, a major challenge is to select a proper reproducing kernel, especially as operators and data vary. We show that the input data and convolution operator themselves induce an automatic, data-adaptive RKHS (DA-RKHS), obviating manual kernel …

Kernel Learning for Mean-Variance Trading Strategies
Owen Futter, Nicola Muca Cirone, Blanka Horvath
https://arxiv.org/abs/2507.10701 https://

Kernel Learning for Mean-Variance Trading Strategies
In this article, we develop a kernel-based framework for constructing dynamic, pathdependent trading strategies under a mean-variance optimisation criterion. Building on the theoretical results of (Muca Cirone and Salvi, 2025), we parameterise trading strategies as functions in a reproducing kernel Hilbert space (RKHS), enabling a flexible and non-Markovian approach to optimal portfolio problems. We compare this with the signature-based framework of (Futter, Horvath, Wiese, 2023) and demonstrat…

I decided that kernel module to flash an LED on disk activity had no business being a kernel module now since it's not pulling privileged data from inside the kernel to do its thing. It's a userspace binary that does the exact same thing (polling /proc/diskstats) now. It should work on any machine that has an LED controllable via /sys/class/led/ (e.g. power, caps lock, whatever)

Heat Kernel Goes Topological
Maximilian Krahn, Vikas Garg
https://arxiv.org/abs/2507.12380 https://arxiv.org/pdf/2507.12380

Heat Kernel Goes Topological
Topological neural networks have emerged as powerful successors of graph neural networks. However, they typically involve higher-order message passing, which incurs significant computational expense. We circumvent this issue with a novel topological framework that introduces a Laplacian operator on combinatorial complexes (CCs), enabling efficient computation of heat kernels that serve as node descriptors. Our approach captures multiscale information and enables permutation-equivariant represen…

On kernel isomorphisms of $m$-Cayley digraphs and finite $2$PCI-groups
Xing Zhang, Yan-Quan Feng, Jin-Xin Zhou, Fu-Gang Yin
https://arxiv.org/abs/2506.12306

On kernel isomorphisms of $m$-Cayley digraphs and finite $2$PCI-groups
The isomorphism problem for digraphs is a fundamental problem in graph theory. In this paper, we consider this problem for $m$-Cayley digraphs which are generalization of Cayley digraphs. Let $m$ be a positive integer. A digraph admitting a group $G$ of automorphisms acting semiregularly on the vertices with exactly $m$ orbits is called an $m$-Cayley digraph of $G$. In our previous paper, we developed a theory for $m$-Cayley isomorphisms of $m$-Cayley digraphs, and classified finite $m$CI-group…

Solvability Issues of Two Classes of Quasilinear and Nonlinear Integral Equations with a Sum-Difference Kernel on the Half-Line
A. Kh. Khachatryan, Kh. A. Khachatryan, H. S. Petrosyan
https://arxiv.org/abs/2507.12231

Solvability Issues of Two Classes of Quasilinear and Nonlinear Integral Equations with a Sum-Difference Kernel on the Half-Line
This paper investigates two classes of quasilinear and essentially nonlinear integral equations with a sum-difference kernel on the half-line. Such equations arise in various areas of physics, including the theory of radiative transfer in spectral lines, the dynamic theory of p-adic strings, and the kinetic theory of gases within the framework of the modified Bhatnagar-Gross-Krook (BGK) model. Under specific conditions on the kernel and the nonlinear terms, we establish constructive existence t…

My kernel deadcoding is now upto w; and I thought w,x,y,z were going to be obscure and small - nope, 'w' is full of wireless stuff, and loads of wireless chips also start with 'w'.

Bayesian wavelet shrinkage for low SNR data based on the Epanechnikov kernel
Fidel Aniano Causil Barrios, Alex Rodrigo dos Santos Sousa
https://arxiv.org/abs/2507.11718

Bayesian wavelet shrinkage for low SNR data based on the Epanechnikov kernel
Consider the univariate nonparametric regression model with additive Gaussian noise and the representation of the unknown regression function in terms of a wavelet basis. We propose a shrinkage rule to estimate the wavelet coefficients obtained by mixing a point mass function at zero with the Epanechnikov distribution as a prior for the coefficients. The proposed rule proved to be suitable for application in scenarios with low signal-to-noise ratio datasets and outperformed standard and Bayesia…

"Come to mind the names of violent battles: DR-DOS, Taligent, AmigaOS, Newton OS, Windows for Pen Computing, Copland, Vino, NeXTSTEP, Novell NetWare, JavaOS, BeOS, DoJ vs Microsoft, Rhapsody, POSIX, Linux is communism, Linux is a cancer, Samizdat, Windows Vista, SCO, Symbian, Solaris, OpenSolaris, systemd, Windows Phone, MeeGo, Tizen, Firefox OS, Sailfish OS. Countless mythical man-month hours were lost. Millions of lines of code were fired."

Aftermath Of The Kernel Wars
During the 1980s and 90s, computer companies waged an open war for supremacy. We are witnesses of an age that could be described as post-apocalyptic. The dust has settled for the past 20 years, with a small ignition of hostilities around 2010, between members of the mobile duopoly and some late challengers. But to a large degree, we can say that the Kernel Wars are over, and we are privileged observers of their aftermath. But everyone was surprised to discover the winner emerging from the ashes…

PSA: In case you run #Ubuntu 24.10 with #ZFS on Root, DO NOT TRY TO UPGRADE YET! You are very likely to run into a #Linux kernel deadlock mid-flight during the upgrade which leaves you with a broken system. …

System deadlock during Ubuntu upgrade 24.10→25.04 with ZFS root due to update-grub calling `ls /.zfs/snapshot/…` via `10_linux_zfs` causing kernel lock at mount.zfs · Issue #17337 · openzfs/zfs
System information Type Version/Name Distribution Name Ubuntu Distribution Version 24.10 Kernel Version 6.11.0-25-generic Architecture x86_64 OpenZFS Version zfs-2.2.6-1ubuntu1.1, zfs-kmod-2.2.6-1u...

Obscure kernel bug use-after-free and then the VLAI severity told me "maybe important" before I read the drama #kernel

[CVE-2025-38001] Exploiting All Google kernelCTF Instances And Debian 12 With A 0-Day For $82k: A RBTree Family Drama (Part One: LTS & COS)
CVE-2025-38001 is a Use-After-Free vulnerability in the Linux network packet scheduler, specifically in the HFSC queuing discipline. When the HFSC qdisc is utilized with NETEM and NETEM packet duplication is enabled, using HFSC_RSC it is possible to cause a double class insertion in the HFSC eligible tree. Under normal conditions, this would lead to an infinite loop in hfsc_dequeue() due to an RBTree cycle. However, by adding TBF as root qdisc, it is possible to prevent packets from being deque…

Markov approximation for controlled Hawkes Jump-Diffusions with general kernels
Mahmoud Khabou, Mehdi Talbi
https://arxiv.org/abs/2507.11294 https://

Markov approximation for controlled Hawkes Jump-Diffusions with general kernels
We present a Markov approximation for jump-diffusions whose jump part consists in a Hawkes process with intensity driven by a general (possibly non-monotone) kernel. Under minimal integrability conditions, the kernel can be approximated by a linear combination of exponential functions. This implies that Hawkes jump-diffusions can be approximated with Markov jump-diffusions. We illustrate the usefulness of this approximation by applying it to a class of stochastic control problems.

¿alguien con #openSUSE #tumbleweed ha tenido este problema con la última actualización del kernel?
Lo he solucionado arrancando con el anterior y desinstalšndolo

LISA -- A Modern Proof System
Simon Guilloud, Sankalp Gambhir, Viktor Kun\v{c}ak
https://arxiv.org/abs/2507.11167 https://arxiv.org/p…

LISA -- A Modern Proof System
We present LISA, a proof system and proof assistant for constructing proofs in schematic first-order logic and axiomatic set theory. The logical kernel of the system is a proof checker for first-order logic with equality and schematic predicate and function symbols. It implements polynomial-time proof checking and uses the axioms of ortholattices (which implies the irrelevance of the order of conjuncts and disjuncts and additional propositional laws). The kernel supports the notion of theorems …

Quantum Circuits for the Metropolis-Hastings Algorithm
Baptiste Claudon, Pablo Rodenas-Ruiz, Jean-Philip Piquemal, Pierre Monmarch\'e
https://arxiv.org/abs/2506.11576

Quantum Circuits for the Metropolis-Hastings Algorithm
Szegedy's quantization of a reversible Markov chain provides a quantum walk whose mixing time is quadratically smaller than that of the classical walk. Quantum computers are therefore expected to provide a speedup of Metropolis-Hastings (MH) simulations. Existing generic methods to implement the quantum walk require coherently computing the acceptance probabilities of the underlying Markov kernel. However, reversible computing methods require a number of qubits that scales with the complexity o…

Linux-Kernel 6.15: Näherer Blick auf Neuerungen
Linux 6.15 steigert erneut die Performance, bietet ein einheitliches Firmware-Management und macht "klar Schiff" bei x86.
https://www.

The Central Emission Vertex of two gravitons
Damiano Barcaro, Vittorio Del Duca
https://arxiv.org/abs/2506.11822 https://arxiv.org/pd…

The Central Emission Vertex of two gravitons
It has recently been shown that there exists an $s$-channel sequence of classical corrections to the $H$ diagram computed long ago by Amati, Ciafaloni and Veneziano. At leading logarithmic accuracy, those corrections feature the gravity BFKL kernel as a crucial element, and may be computed through either rapidity renormalisation group equations or amplitudes built through $s$-channel unitarity cuts. In this paper, we evaluate six-graviton amplitudes in next-to-multi-Regge kinematics, and comput…

On the optimality conditions for a fractional diffusive equation with a nonlocal term
Jasarat Gasimov, Nazim Mahmudov
https://arxiv.org/abs/2507.11058 http…

On the optimality conditions for a fractional diffusive equation with a nonlocal term
We study a bilinear OCP for an evolution equation governed by the fractional Laplacian of order $0 < s < 1$, incorporating a nonlocal time component modeled by an integral kernel. After establishing well-posedness of the problem, we analyze the properties of the control-to-state operator. We prove the existence of at least one optimal control and derive both first-order and second-order optimality conditions, which ensure local uniqueness. Under further assumptions, we also demonstrate that glo…

Kernel Density Balancing
John Park, Ning Hao, Yue Selena Niu, Ming Hu
https://arxiv.org/abs/2506.12626 https://arxiv.org/pdf/2506.126…

Kernel Density Balancing
High-throughput chromatin conformation capture (Hi-C) data provide insights into the 3D structure of chromosomes, with normalization being a crucial pre-processing step. A common technique for normalization is matrix balancing, which rescales rows and columns of a Hi-C matrix to equalize their sums. Despite its popularity and convenience, matrix balancing lacks statistical justification. In this paper, we introduce a statistical model to analyze matrix balancing methods and propose a kernel-bas…

Wondering why the number of downloaded vanilla #Linux mainline #kernel packages[1] for aarch64 and ppc64le on Fedora 42 already overtook 40 and 41.
🤷
BTW, if anyone wants me to build kernels for s390x as well, please let me know. It's likely not more work for me than enabling a c…

I've finished my deadcoding pass on the #linux kernel; 139 insertions( ), 21936 deletions(-) on Linus's tree, there's another 2300 that are/have been in -next, and I've got about another 30 patches that have been posted but I need to prod the maintainers more; so it _might_ hit 25kloc removed. Which sounds a lot until you realise it's only about 0.06% of the kernel.
So p…

This https://arxiv.org/abs/2504.06151 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csOS_…

Zerrow: True Zero-Copy Arrow Pipelines in Bauplan
Bauplan is a FaaS-based lakehouse specifically built for data pipelines: its execution engine uses Apache Arrow for data passing between the nodes in the DAG. While Arrow is known as the "zero copy format", in practice, limited Linux kernel support for shared memory makes it difficult to avoid copying entirely. In this work, we introduce several new techniques to eliminate nearly all copying from pipelines: in particular, we implement a new kernel module that performs de-anonymization, thus eli…

On computation of capacities
Mohamed M S Nasser, Matti Vuorinen
https://arxiv.org/abs/2507.11648 https://arxiv.org/pdf/2507.11648

On computation of capacities
We give a survey of computation of the conformal capacity of planar condensers, generalized capacity, and logarithmic capacity with emphasis on our recent work 2020-2025. We also discuss some applications of our method based on the boundary integral equation with the generalized Neumann kernel to the computation of several other conformal invariants: harmonic measure, modulus of a quadrilateral, reduced modulus, hyperbolic capacity, and elliptic capacity. Here the solution of mixed Dirichlet-Ne…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/8]:
Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling

Adaptive estimation in regression models for weakly dependent data and explanatory variable with known density
Karine Bertin, Lisandro Fermin, Miguel Padrino
https://arxiv.org/abs/2507.11725

Adaptive estimation in regression models for weakly dependent data and explanatory variable with known density
This article is dedicated to the estimation of the regression function when the explanatory variable is a weakly dependent process whose correlation coefficient exhibits exponential decay and has a known bounded density function. The accuracy of the estimation is measured using pointwise risk. A data-driven procedure is proposed using kernel estimation with bandwidth selected via the Goldenshluger-Lepski approach. We demonstrate that the resulting estimator satisfies an oracle-type inequality a…

Went ahead and upgraded the Proxmox cluster from kernel version `6.8` to `6.14`. Seemed like a good idea, didn't have any issues with VM migrations so that was nice.
#proxmox #homelab

Self-gravity in thin protoplanetary discs: 1. The smoothing-length approximation versus the exact self-gravity kernel
S. Rendon Restrepo, T. Rometsch, U. Ziegler, O. Gressel
https://arxiv.org/abs/2506.10812

Self-gravity in thin protoplanetary discs: 1. The smoothing-length approximation versus the exact self-gravity kernel
Planet-forming discs often contain structures like spiral arms, typically linked to the disc's gravitational forces. In 2D models, an ad hoc softening prescription is commonly used for self-gravity, but this overlooks the vertical structure's impact, suppresses the Newtonian nature of gravity at short distances and doesn't respect Newton's third law. To address these issues, associated with a Plummer potential approximation, we developed an exact self-gravity kernel for thin, hydrostatically …

from my link log —
Gerrit, GitButler, and Jujutsu projects collaborating on a change-id commit footer.
https://lore.kernel.org/git/CAESOdVAspxUJKGAA58i0tvks4ZOfoGf1Aa5gPr0FXzdcywqUUw@mail.gmail.com/
saved 2025-…

Extending Nonlocal Kinetic Energy Density Functionals to Isolated Systems via a Density-Functional-Dependent Kernel
Liang Sun, Mohan Chen
https://arxiv.org/abs/2507.08442

Extending Nonlocal Kinetic Energy Density Functionals to Isolated Systems via a Density-Functional-Dependent Kernel
The Wang-Teter-like nonlocal kinetic energy density functional (KEDF) in the framework of orbital-free density functional theory, while successful in some bulk systems, exhibits a critical Blanc-Cances instability [J. Chem. Phys. 122, 214106 (2005)] when applied to isolated systems, where the total energy becomes unbounded from below. We trace this instability to the use of an ill-defined average charge density, which causes the functional to simultaneously violate the scaling law and the posit…

New preprint!
User-space library rootkits revisited: Are user-space detection mechanisms futile?
https://arxiv.org/abs/2506.07827

lkml_thread: Linux kernel mailing list
A bipartite network of contributions by users to threads on the Linux kernel mailing list. A left node is a person, and a right node is a thread, and each timestamped edge (i,j,t) denotes that user i contributed to thread j at time t. The date of the snapshot is not given.
This network has 379554 nodes and 1565683 edges.
Tags: Social, Communication, Unweighted, Timestamps

The new Linux anthem will be "He's an idiot, but he's ok", as performed by
Monthy Python. You'd better start practicing.
-- Linus Torvalds, announcing another kernel patch

Replaced article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- Active Deep Kernel Learning of Molecular Properties: Realizing Dynamic Structural Embeddings
Ayana Ghosh, Maxim Ziatdinov, Sergei V. Kalinin

A Mathematical Optimization Approach to Multisphere Support Vector Data Description
V\'ictor Blanco, Inmaculada Espejo, Ra\'ul P\'aez, Antonio M. Rodr\'iguez-Ch\'ia
https://arxiv.org/abs/2507.11106

A Mathematical Optimization Approach to Multisphere Support Vector Data Description
We present a novel mathematical optimization framework for outlier detection in multimodal datasets, extending Support Vector Data Description approaches. We provide a primal formulation, in the shape of a Mixed Integer Second Order Cone model, that constructs Euclidean hyperspheres to identify anomalous observations. Building on this, we develop a dual model that enables the application of the kernel trick, thus allowing for the detection of outliers within complex, non-linear data structures.…

Last-Pair Swapping Polar Codes: A Structure to Improve Polarization on Channels with Memory
Yinuo Mei, Yangyong Zhang, Daiming Qu
https://arxiv.org/abs/2506.11535

Last-Pair Swapping Polar Codes: A Structure to Improve Polarization on Channels with Memory
A new structure of polar codes is proposed for channels with memory to improve polarization and approach the polarization efficiency of conventional polar codes on memoryless channels. We classify trellis channels, a particular type of finite state channels into two categories, called sub-injective trellis channels and non-sub-injective trellis channels, where an explicit polarization loss is observed on the former. For sub-injective trellis channels, we substitute the last kernel of each layer…

Parsimonious Compactly Supported Covariance Models in the Gauss Hypergeometric Class: Identifiability, Reparameterizations, and Asymptotic Properties
Moreno Bevilacqua, Christian Caama\~no-Carrillo, Tarik Faouzi, Xavier Emery
https://arxiv.org/abs/2506.13646

Parsimonious Compactly Supported Covariance Models in the Gauss Hypergeometric Class: Identifiability, Reparameterizations, and Asymptotic Properties
We study the covariance model belonging to the Gauss hypergeometric ($GH$) class, a highly flexible and compactly supported correlation model (kernel). This class includes the well-known Generalized Wendland ($GW$) and Matérn ($MT$) kernels as special cases. First, we provide necessary and sufficient conditions for the validity of the $\mathcal{GH}$ model. We then demonstrate that this family of models suffers from identifiability issues under both increasing and fixed-domain asymptotics. To…

Join Nikolay Sivko at this year's Berlin Buzzwords for his talk, in which he will delve into delay accounting — a frequently overlooked feature that provides valuable insights into CPU time shortages and application latency. Discover how to use these kernel metrics to improve performance analysis and system optimisation.
Learn more:

Manifolds with kinks and the asymptotic behavior of the graph Laplacian operator with Gaussian kernel
Susovan Pal, David Tewodrose
https://arxiv.org/abs/2507.07751

Manifolds with kinks and the asymptotic behavior of the graph Laplacian operator with Gaussian kernel
We introduce manifolds with kinks, a class of manifolds with possibly singular boundary that notably contains manifolds with smooth boundary and corners. We derive the asymptotic behavior of the Graph Laplace operator with Gaussian kernel and its deterministic limit on these spaces as bandwidth goes to zero. We show that this asymptotic behavior is determined by the inward sector of the tangent space and, as special cases, we derive its behavior near interior and singular points. Lastly, we sho…

Vaya susto me ha dado la última actualización de OpenSUSE y el kernel panic que me daba en el arranque. He usado el kernel anterior y sin priblemas...

Heat kernel bounds for the fractional Laplacian with Hardy potential in angular momentum channels
Krzysztof Bogdan, Konstantin Merz
https://arxiv.org/abs/2506.08115

Heat kernel bounds for the fractional Laplacian with Hardy potential in angular momentum channels
Motivated by the study of relativistic atoms, we prove sharp heat kernel bounds for the Hardy operator $(-Δ)^{α/2}-κ|x|^{-α}$ acting on functions of the form $u(|x|) |x|^{\ell} Y_{\ell,m}(x/|x|)$ in $L^2(\R^d)$, when $α\in(0,2]\cap(0,d+2\ell)$.

On Ray Reordering Techniques for Faster GPU Ray Tracing
Daniel Meister, Jakub Bok\v{s}ansk\'y, Michael Guthe, Ji\v{r}\'i Bittner
https://arxiv.org/abs/2506.11273

On Ray Reordering Techniques for Faster GPU Ray Tracing
We study ray reordering as a tool for increasing the performance of existing GPU ray tracing implementations. We focus on ray reordering that is fully agnostic to the particular trace kernel. We summarize the existing methods for computing the ray sorting keys and discuss their properties. We propose a novel modification of a previously proposed method using the termination point estimation that is well-suited to tracing secondary rays. We evaluate the ray reordering techniques in the context o…

Deformations of OP ensembles in a bulk critical scaling
Caio E. Candido, Victor Alves, Thomas Chouteau, Charles F. Santos, Guilherme L. F. Silva
https://arxiv.org/abs/2506.05622

Deformations of OP ensembles in a bulk critical scaling
We study orthogonal polynomial ensembles whose weights are deformations of exponential weights, in the limit of a large number of particles. The deformation symbols we consider affect local fluctuations of the ensemble around a bulk point of the limiting spectrum. We identify the limiting kernel in terms of a solution to an integrable non-local differential equation. This novel kernel is the correlation kernel of a conditional thinned process starting from the Sine point process, and it is also…

Wondering why the aarch64 and ppc64le numbers for my #Linux #kernel vanilla mainline copr (left in the screenshot) for #Fedora are higher than for the mainline-wo-mergew copr; they should be lower,…

New set of #Gentoo #Linux Distribution Kernels (6.1.143, 6.6.96, 6.12.36, 6.15.5) is out. This set brings some major changes:
• I've backported a bunch of changes from sys-kernel/gentoo-kernel to sys-kernel/vanilla-kernel that were missing — notably wider architecture support.
• I've added default #RISCV configs to 6.12 (in addition to 6.15), since Fedora had them.
• All three packages are based off the baseline kernel tarball upstream patch (vanilla-kernel used to fetch patch-level tarball every time, and gentoo-kernel* used genpatches for patch versions). This should reduce disk space and bandwidth use.
• All three packages now support verify-sig. Rather than verifying the uncompressed tarball signature, we now use upstream `sha256sums.asc` file to verify the compressed tarball and patch.
• sys-kernel/gentoo-kernel* now repackages genpatches. This means patchset that's much leaner and faster to apply (since we just fetch and use the combined upstream patch rather than including point patches). This also means that we are able to release Distribution Kernels before gentoo-sources are done.
The changes still need to be done to 5.15 and 5.10 branches — we're going to do for the next upstream releases of these.
#kernel

Isoperimetric Inequality on Manifolds with Quadratically Decaying Curvature
Dangyang He
https://arxiv.org/abs/2506.13365 https://arxi…

Isoperimetric Inequality on Manifolds with Quadratically Decaying Curvature
In this paper, we investigate the reverse improvement property of Sobolev inequalities on manifolds with quadratically decaying Ricci curvature. Specifically, we establish conditions under which the uniform decay of the heat kernel implies the validity of an isoperimetric inequality. As an application, we demonstrate the existence of isoperimetric sets in generalized Grushin spaces. Our approach is built on a weak-type Sobolev inequality, gradient estimates on remote balls, and a Hardy-type glu…

'"[…] Suppose you compile an #eBPF program on #kernel version 5.3, but it fails to run on 5.4.
Why? Because each kernel version ships with its own kernel headers, which define structs and memory layouts. […]
So if a certain struct field […] sits at a different offset in another OS or ke…

This https://arxiv.org/abs/2506.04667 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

FlashDMoE: Fast Distributed MoE in a Single Kernel
The computational sparsity of Mixture-of-Experts (MoE) models enables sub-linear growth in compute cost as model size increases, offering a scalable path to training massive neural networks. However, existing implementations suffer from \emph{low GPU utilization}, \emph{significant latency overhead}, and a fundamental \emph{inability to leverage task locality}, primarily due to CPU-managed scheduling, host-initiated communication, and frequent kernel launches. To overcome these limitations, we …

KP-PINNs: Kernel Packet Accelerated Physics Informed Neural Networks
Siyuan Yang, Cheng Song, Zhilu Lai, Wenjia Wang
https://arxiv.org/abs/2506.08563 https…

KP-PINNs: Kernel Packet Accelerated Physics Informed Neural Networks
Differential equations are involved in modeling many engineering problems. Many efforts have been devoted to solving differential equations. Due to the flexibility of neural networks, Physics Informed Neural Networks (PINNs) have recently been proposed to solve complex differential equations and have demonstrated superior performance in many applications. While the L2 loss function is usually a default choice in PINNs, it has been shown that the corresponding numerical solution is incorrect and…

#Linux learning of the day:
There are two #NTFS drivers: A kernel driver called `ntfs3` and a user space driver called `ntfs-3g`.
Yeah, confusing, I know. To make matters worse, if you see just `ntfs` then it means it's actually `ntfs-3g` and not `ntfs3`.

Optimal transport, determinantal point processes and the Bergman kernel
William Driot, Laurent Decreusefond
https://arxiv.org/abs/2507.08204 https://

Optimal transport, determinantal point processes and the Bergman kernel
We study the Bergman determinantal point process from a theoretical point of view motivated by its simulation. We construct restricted and restricted-truncated variants of the Bergman kernel and show optimal transport inequalities involving the Kantorovitch-Rubinstein Wasserstein distance to show to what extent it is fair to truncate the restriction of this point process to a compact ball of radius $1 - \varepsilon $. We also investigate the deviation of the number of points of the restricted B…

I did a reboot of my #nas after an update (it sometimes stays up for months). Somehow Grub got hosed, so I did a chroot to reconfigure it.
Alas, some digging on #ZFS, and the stable kernel. Turns out the latest stable has support for it (historically it's been very touch and go - usually he…

On the heat kernel of a Cayley graph of $\operatorname{PSL}_2\mathbb{Z}$
Anders Karlsson, Kamila Kashaeva
https://arxiv.org/abs/2506.02340 https://

On the heat kernel of a Cayley graph of $\operatorname{PSL}_2\mathbb{Z}$
In this paper, we obtain an explicit formula for the heat kernel on the infinite Cayley graph of the modular group $\operatorname{PSL}_2\mathbb{Z}$, given by the presentation $\langle a,b\mid a^2=1, b^3=1\rangle$. Our approach extends the method of Chung--Yau in~\cite{MR1667452} by observing that the Cayley graph strongly and regularly covers a weighted infinite line. We solve the spectral problem on this line to obtain an integral expression for its heat kernel, and then lift this to the Cayle…

Tree-Structured Parzen Estimator Can Solve Black-Box Combinatorial Optimization More Efficiently
Kenshin Abe, Yunzhuo Wang, Shuhei Watanabe
https://arxiv.org/abs/2507.08053 https://arxiv.org/pdf/2507.08053 https://arxiv.org/html/2507.08053
arXiv:2507.08053v1 Announce Type: new
Abstract: Tree-structured Parzen estimator (TPE) is a versatile hyperparameter optimization (HPO) method supported by popular HPO tools. Since these HPO tools have been developed in line with the trend of deep learning (DL), the problem setups often used in the DL domain have been discussed for TPE such as multi-objective optimization and multi-fidelity optimization. However, the practical applications of HPO are not limited to DL, and black-box combinatorial optimization is actively utilized in some domains, e.g., chemistry and biology. As combinatorial optimization has been an untouched, yet very important, topic in TPE, we propose an efficient combinatorial optimization algorithm for TPE. In this paper, we first generalize the categorical kernel with the numerical kernel in TPE, enabling us to introduce a distance structure to the categorical kernel. Then we discuss modifications for the newly developed kernel to handle a large combinatorial search space. These modifications reduce the time complexity of the kernel calculation with respect to the size of a combinatorial search space. In the experiments using synthetic problems, we verified that our proposed method identifies better solutions with fewer evaluations than the original TPE. Our algorithm is available in Optuna, an open-source framework for HPO.
toXiv_bot_toot

Power-boosting in Specification Tests using Kernel Directional Component
Cui Rui, Li Yuhao, Song Xiaojun
https://arxiv.org/abs/2506.04900 https://

Power-boosting in Specification Tests using Kernel Directional Component
We propose power-boosting strategies for kernel-based specification tests in conditional moment models, with a focus on the Kernel Conditional Moment (KCM) test. By decomposing the KCM statistic into spectral components, we demonstrate that truncating poorly estimated directions and selecting kernels based on a non-asymptotic signal-to-noise ratio significantly improves both test power and size control. Our theoretical and simulation results demonstrate that, while divergent component weights m…

A Rigorous Error Bound for the TG Kernel in Prime Counting
Bugra Kilictas, Faruk Alpay
https://arxiv.org/abs/2506.22634 https://arxiv…

A Rigorous Error Bound for the TG Kernel in Prime Counting
We establish rigorous error bounds for prime counting using a truncated Gaussian (TG) kernel in the explicit formula framework. Our main theorem proves that the approximation error remains globally below 1/2 for all sufficiently large arguments, guaranteeing exact computation of π(x) through simple rounding, without relying on unproven hypotheses. The TG kernel construction employs Gaussian-like test functions with compact support, engineered with vanishing moments to eliminate main terms. F…

Prenormal categories
Sandra Mantovani, Mariano Messora
https://arxiv.org/abs/2507.03459 https://arxiv.org/pdf/2507.03459

Prenormal categories
In this paper we introduce the notion of (pointed) prenormal category, modelled after regular categories, but with the key notions of coequaliser and kernel pair replaced by those of cokernel and kernel. This framework provides a natural setting for extending certain classical results in algebra. We study the fundamental properties of prenormal categories, including a characterisation in terms of a factorisation system involving normal epimorphisms, and a categorical version of Noether's so-cal…

Dynamic Similarity Graph Construction with Kernel Density Estimation
Steinar Laenen, Peter Macgregor, He Sun
https://arxiv.org/abs/2507.01696 https://

Dynamic Similarity Graph Construction with Kernel Density Estimation
In the kernel density estimation (KDE) problem, we are given a set $X$ of data points in $\mathbb{R}^d$, a kernel function $k: \mathbb{R}^d \times \mathbb{R}^d \rightarrow \mathbb{R}$, and a query point $\mathbf{q} \in \mathbb{R}^d$, and the objective is to quickly output an estimate of $\sum_{\mathbf{x} \in X} k(\mathbf{q}, \mathbf{x})$. In this paper, we consider $\textsf{KDE}$ in the dynamic setting, and introduce a data structure that efficiently maintains the estimates for a set of query p…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new/
[1/6]:
Goal Kernel Planning: Linearly-Solvable Non-Markovian Policies for Logical Tasks with Goal-Condit...

Ah fuck, people are using LLMs for kernel code. They really are going to fuck over everything, aren't they?
https://lwn.net/SubscriberLink/1026558/9f3079fb392c4a9a/

Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels
Donovan Slabbert, Dean Brand, Francesco Petruccione
https://arxiv.org/abs/2507.07018

Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels
We compare a parameterized quantum kernel (pQK) with a quantum leaky integrate-and-fire (QLIF) neuromorphic computing approach that employs either the Victor-Purpura or van Rossum kernel in a spectral clustering task, as well as the classical radial basis function (RBF) kernel. Performance evaluation includes label-based classification and clustering metrics, as well as optimal number of clusters predictions for each dataset based on an elbow-like curve as is typically used in $K$-means cluster…

lkml_thread: Linux kernel mailing list
A bipartite network of contributions by users to threads on the Linux kernel mailing list. A left node is a person, and a right node is a thread, and each timestamped edge (i,j,t) denotes that user i contributed to thread j at time t. The date of the snapshot is not given.
This network has 379554 nodes and 1565683 edges.
Tags: Social, Communication, Unweighted, Timestamps

Kernel Trace Distance: Quantum Statistical Metric between Measures through RKHS Density Operators
Arturo Castellanos, Anna Korba, Pavlo Mozharovskyi, Hicham Janati
https://arxiv.org/abs/2507.06055

Kernel Trace Distance: Quantum Statistical Metric between Measures through RKHS Density Operators
Distances between probability distributions are a key component of many statistical machine learning tasks, from two-sample testing to generative modeling, among others. We introduce a novel distance between measures that compares them through a Schatten norm of their kernel covariance operators. We show that this new distance is an integral probability metric that can be framed between a Maximum Mean Discrepancy (MMD) and a Wasserstein distance. In particular, we show that it avoids some pitfa…

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…

Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling
Chia-Ming Lee, Bo-Cheng Qiu, Ting-Yao Chen, Ming-Han Sun, Fang-Ying Lin, Jung-Tse Tsai, I-An Tsai, Yu-Fan Lin, Chih-Chung Hsu
https://arxiv.org/abs/2507.01564

Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling
We present our solution for the Multi-Source COVID-19 Detection Challenge, which classifies chest CT scans from four distinct medical centers. To address multi-source variability, we employ the Spatial-Slice Feature Learning (SSFL) framework with Kernel-Density-based Slice Sampling (KDS). Our preprocessing pipeline combines lung region extraction, quality control, and adaptive slice sampling to select eight representative slices per scan. We compare EfficientNet and Swin Transformer architectur…

If you're on kernel 6.15 or greater and still want to use your ThinkPad's power LED as a disk activity indicator, I've got your back. New version of the kernel module out now: https://github.com/theodric/block-led-trigger

This https://arxiv.org/abs/2110.15035 has been replaced.
link: https://scholar.google.com/scholar?q=a

Varieties with ample Frobenius-trace kernel
In the search of a projective analog of Kunz's theorem and a Frobenius-theoretic analog of Mori--Hartshorne's theorem, we investigate the positivity of the kernel of the Frobenius trace (equivalently, the negativity of the cokernel of the Frobenius endomorphism) on a smooth projective variety over an algebraically closed field of positive characteristic. For instance, such kernel is ample for projective spaces. Conversely, we show that for curves, surfaces, and threefolds the Frobenius trace ke…

Kernel-based Greedy Approximation of Parametric Elliptic Boundary Value Problems
Bernard Haasdonk, Gabriele Santin, Tizian Wenzel
https://arxiv.org/abs/2507.06731

Kernel-based Greedy Approximation of Parametric Elliptic Boundary Value Problems
We recently introduced a scale of kernel-based greedy schemes for approximating the solutions of elliptic boundary value problems. The procedure is based on a generalized interpolation framework in reproducing kernel Hilbert spaces and was coined PDE-$β$-greedy procedure, where the parameter $β\geq 0$ is used in a greedy selection criterion and steers the degree of function adaptivity. Algebraic convergence rates have been obtained for Sobolev-space kernels and solutions of finite smoothness.…

Okay. Gentoo feedback time. MocaccinoOS was okay, but systemd. And after a kernel change (to their mainline) didn't boot. Done.
Redcore has a great looking Qtile live-session. One maintainer, installer from rofi didn't start up. Done.
Calculate Linux: actually pretty good; smooth install, good documentation. OpenRC, switch to the latest kernel went well. Good. Very close to core Gentoo. But.
If so, why not pull out that Gentoo live installer, go from there, use your pr…

Replaced article(s) found for cs.PF. https://arxiv.org/list/cs.PF/new
[1/1]:
- Cloud Computing Energy Consumption Prediction Based on Kernel Extreme Learning Machine Algorithm ...
Yuqing Wang, Xiao Yang

from my link log —
Why is my Chumby’s CPU usage always 100%?
https://www.downtowndougbrown.com/2024/04/why-is-my-cpu-usage-always-100-upgrading-my-chumby-8-kernel-part-9/
saved 2025-01-13

Replaced article(s) found for cs.CG. https://arxiv.org/list/cs.CG/new/
[1/1]:
Dimension-Independent Kernel {\epsilon}-Covers
https://

Scalable Neural Quantum State based Kernel Polynomial Method for Optical Properties from the First Principle
Wei Liu, Rui-Hao Bi, Wenjie Dou
https://arxiv.org/abs/2506.07430

Scalable Neural Quantum State based Kernel Polynomial Method for Optical Properties from the First Principle
Variational optimization of neural-network quantum state representations has achieved FCI-level accuracy for ground state calculations, yet computing optical properties involving excited states remains challenging. In this work, we present a neural-network-based variational quantum Monte Carlo approach for ab-initio absorption spectra. We leverage parallel batch autoregressive sampling and GPU-supported local energy parallelism to efficiently compute ground states of complex systems. By integra…

What is going on in the land of Fedora Linux?
First, the latest kernel in Fedora 42 recently went from "working" to "broken" with regard to the video screen on Intel(R) Core(TM) Ultra 7 155H on an ASUSTeK COMPUTER INC. NUC14RVK-B/NUC14RVBU7
Second, the update repositories now all have invalid checksums. (Update: this is now corrected.)
Fedora is Redhat is IBM - you would think that they could do some regression testing and maybe do a better locking of t…

Replaced article(s) found for math.FA. https://arxiv.org/list/math.FA/new
[1/1]:
- On the Probabilistic Approximation in Reproducing Kernel Hilbert Spaces
Dongwei Chen, Kai-Hsiang Wang

Dirichlet kernel density estimation for strongly mixing sequences on the simplex
Hanen Daayeb, Salah Khardani, Fr\'ed\'eric Ouimet
https://arxiv.org/abs/2506.08816

Dirichlet kernel density estimation for strongly mixing sequences on the simplex
This paper investigates the theoretical properties of Dirichlet kernel density estimators for compositional data supported on simplices, for the first time addressing scenarios involving time-dependent observations characterized by strong mixing conditions. We establish rigorous results for the asymptotic normality and mean squared error of these estimators, extending previous findings from the independent and identically distributed (iid) context to the more general setting of strongly mixing …

arse:
'Accept State: remote can't support version # {2}
Program Version (Minimum): 3'
Jun 11 15:07:08 major sh[1012]: rpc.nfsd: Writing version string to kernel: 2 3 4 4.1 4.2
Jun 11 15:07:08 major sh[1012]: rpc.nfsd: Setting version failed: errno 22 (Invalid argument)
OK, so Debian Trixie has NFSv2 server configured out of the kernel; (it has the client as a module)

Generalized Poisson kernel and solution of the Dirichlet problem for the radial Schr\"odinger equation
V\'ictor A Vicente-Ben\'itez
https://arxiv.org/abs/2506.10273

Generalized Poisson kernel and solution of the Dirichlet problem for the radial Schrödinger equation
We present an explicit construction of the solution to the Dirichlet boundary value problem for the radial Schrödinger equation in the unit ball, with a complex-valued potential $V$ satisfying the condition $\int_0^1r|V(r)|dr<\infty$. The solution is based on the construction of an explicit orthogonal set of solutions for the radial equation. In the case of a Dirichlet problem with boundary data in $W^{\frac{1}{2},2}(\mathbb{S}^{d-1})$, the solution is expressed as a series expansion…

lkml_thread: Linux kernel mailing list
A bipartite network of contributions by users to threads on the Linux kernel mailing list. A left node is a person, and a right node is a thread, and each timestamped edge (i,j,t) denotes that user i contributed to thread j at time t. The date of the snapshot is not given.
This network has 379554 nodes and 1565683 edges.
Tags: Social, Communication, Unweighted, Timestamps

Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
Marcos Matabuena, Rahul Ghosal, Pavlo Mozharovskyi, Oscar Hernan Madrid Padilla, Jukka-Pekka Onnela
https://arxiv.org/abs/2506.08325

Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
Depth measures are powerful tools for defining level sets in emerging, non--standard, and complex random objects such as high-dimensional multivariate data, functional data, and random graphs. Despite their favorable theoretical properties, the integration of depth measures into regression modeling to provide prediction regions remains a largely underexplored area of research. To address this gap, we propose a novel, model-free uncertainty quantification algorithm based on conditional depth mea…

Linux 6.15-rc4 is out:
https://lore.kernel.org/lkml/CAHk-=wg8VMvbaq6EECWxP7OcrGtUmauFQCjeLT24pXh-H=o-4Q@mail.gmail.com/
'"So let's see if this rc ends up avoiding any silly issues - things certainly loo…

Improved error bounds for Koopman operator and reconstructed trajectories approximations with kernel-based methods
Diego Olgu\'in, Axel Osses, H\'ector Ram\'irez
https://arxiv.org/abs/2506.09266

Improved error bounds for Koopman operator and reconstructed trajectories approximations with kernel-based methods
In this article, we propose a new error bound for Koopman operator approximation using Kernel Extended Dynamic Mode Decomposition. The new estimate is $O(N^{-1/2})$, with a constant related to the probability of success of the bound, given by Hoeffding's inequality, similar to other methodologies, such as Philipp et al. Furthermore, we propose a \textit{lifting back} operator to obtain trajectories generated by embedding the initial state and iterating a linear system in a higher dimension. Thi…

Using SBPF to Accelerate Kernel Memory Access From Userspace
Boming Kong, Zhizhou Zhang, Jonathan Balkind
https://arxiv.org/abs/2506.22632 https://

Using SBPF to Accelerate Kernel Memory Access From Userspace
The cost of communication between the operating system kernel and user applications has long blocked improvements in software performance. Traditionally, operating systems encourage software developers to use the system call interface to transfer (or initiate transfer of) data between user applications and the kernel. This approach not only hurts performance at the software level due to memory copies between user space address spaces and kernel space address spaces, it also hurts system perform…

Linux is when your computer doesn't boot, you think it's a kernel bug, you spend half the day troubleshooting, you identify an actual hardware defect, you go out and buy new hardware, and then you find out it actually was a kernel bug all along.
#linux #kernel

A Unified Theory of Compositionality, Modularity, and Interpretability in Markov Decision Processes
Thomas J. Ringstrom, Paul R. Schrater
https://arxiv.org/abs/2506.09499

A Unified Theory of Compositionality, Modularity, and Interpretability in Markov Decision Processes
We introduce Option Kernel Bellman Equations (OKBEs) for a new reward-free Markov Decision Process. Rather than a value function, OKBEs directly construct and optimize a predictive map called a state-time option kernel (STOK) to maximize the probability of completing a goal while avoiding constraint violations. STOKs are compositional, modular, and interpretable initiation-to-termination transition kernels for policies in the Options Framework of Reinforcement Learning. This means: 1) STOKs can…

FlashDMoE: Fast Distributed MoE in a Single Kernel
Osayamen Jonathan Aimuyo, Byungsoo Oh, Rachee Singh
https://arxiv.org/abs/2506.04667 https://

FlashDMoE: Fast Distributed MoE in a Single Kernel
The computational sparsity of Mixture-of-Experts (MoE) models enables sub-linear growth in compute cost as model size increases, offering a scalable path to training massive neural networks. However, existing implementations suffer from \emph{low GPU utilization}, \emph{significant latency overhead}, and a fundamental \emph{inability to leverage task locality}, primarily due to CPU-managed scheduling, host-initiated communication, and frequent kernel launches. To overcome these limitations, we …

lkml_reply: Linux kernel reply network
A network of replies among email address found on the Linux kernel mailing list. Nodes are email addresses and each directed edge represents a reply from email address i to email address j. The date of this snapshot is uncertain.
This network has 63399 nodes and 1096440 edges.
Tags: Social, Communication, Unweighted, Multigraph, Timestamps

Divergence-Kernel method for scores of random systems
Angxiu Ni
https://arxiv.org/abs/2507.04035 https://arxiv.org/pdf/2507.04035

Divergence-Kernel method for scores of random systems
We derive the divergence-kernel formula for the scores of random dynamical systems, then formally pass to the continuous-time limit of SDEs. Our formula works for multiplicative noise systems over any period of time; it does not require hyperbolicity. We also consider several special cases: (1) for additive noise, we give a pure kernel formula; (2) for short-time, we give a pure divergence formula; (3) we give a formula which does not involve scores of the initial distribution. Based on the new…

Kernel 6.16-rc1 seems to fix all the problems I was having with 6.15.x ¯\_(ツ)_/¯

This https://arxiv.org/abs/2409.00915 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On the Pinsker bound of inner product kernel regression in large dimensions
Building on recent studies of large-dimensional kernel regression, particularly those involving inner product kernels on the sphere $\mathbb{S}^{d}$, we investigate the Pinsker bound for inner product kernel regression in such settings. Specifically, we address the scenario where the sample size $n$ is given by $αd^γ(1+o_{d}(1))$ for some $α, γ>0$. We have determined the exact minimax risk for kernel regression in this setting, not only identifying the minimax rate but also the exact consta…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new/
[4/6]:
Dimension-Independent Kernel {\epsilon}-Covers
https://

Minimum-norm interpolation for unknown surface reconstruction
Alex Shiu Lun Chu, Leevan Ling, Ka Chun Cheung
https://arxiv.org/abs/2507.08632 https://

Minimum-norm interpolation for unknown surface reconstruction
We study algorithms to estimate geometric properties of raw point cloud data through implicit surface representations. Given that any level-set function with a constant level set corresponding to the surface can be used for such estimations, numerical methods need not specify a unique target function for the domain-type interpolation problems. In this paper, we focus on kernel-based interpolation by radial basis functions (RBF) and reformulate the uniquely solvable interpolation problem into a …

Forward Reverse Kernel Regression for the Schr\"{o}dinger bridge problem
Denis Belomestny, John. Schoenmakers
https://arxiv.org/abs/2507.00640 https:/…

Forward Reverse Kernel Regression for the Schrödinger bridge problem
In this paper, we study the Schrödinger Bridge Problem (SBP), which is central to entropic optimal transport. For general reference processes and begin--endpoint distributions, we propose a forward-reverse iterative Monte Carlo procedure to approximate the Schrödinger potentials in a nonparametric way. In particular, we use kernel based Monte Carlo regression in the context of Picard iteration of a corresponding fixed point problem. By preserving in the iteration positivity and contractivity …

This https://arxiv.org/abs/2412.09080 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On the number of modes of Gaussian kernel density estimators
We consider the Gaussian kernel density estimator with bandwidth $β^{-\frac12}$ of $n$ iid Gaussian samples. Using the Kac-Rice formula and an Edgeworth expansion, we prove that the expected number of modes on the real line scales as $Θ(\sqrt{β\logβ})$ as $β,n\to\infty$ provided $n^c\lesssim β\lesssim n^{2-c}$ for some constant $c>0$. An impetus behind this investigation is to determine the number of clusters to which Transformers are drawn in a metastable state.

This https://arxiv.org/abs/2501.09500 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Lattice Rules Meet Kernel Cubature
Rank-1 lattice rules are a class of equally weighted quasi-Monte Carlo methods that achieve essentially linear convergence rates for functions in a reproducing kernel Hilbert space (RKHS) characterized by square-integrable first-order mixed partial derivatives. In this work, we explore the impact of replacing the equal weights in lattice rules with optimized cubature weights derived using the reproducing kernel. We establish a theoretical result demonstrating a doubled convergence rate in the o…

lkml_reply: Linux kernel reply network
A network of replies among email address found on the Linux kernel mailing list. Nodes are email addresses and each directed edge represents a reply from email address i to email address j. The date of this snapshot is uncertain.
This network has 63399 nodes and 1096440 edges.
Tags: Social, Communication, Unweighted, Multigraph, Timestamps