Tootfinder

This https://arxiv.org/abs/2410.20448 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

New results related to cutters and to an extrapolated block-iterative method for finding a common fixed point of a collection of them
Given a Hilbert space and a finite family of operators defined on the space, the common fixed point problem (CFPP) is to find a point in the intersection of the fixed point sets of these operators. Instances of the problem have numerous applications in science and engineering. We consider an extrapolated block-iterative method with dynamic weights for solving the CFPP assuming the operators belong to a wide class of operators called cutters. Global convergence is proved in two different scenari…

This https://arxiv.org/abs/2404.17061 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Gauge origami and quiver W-algebras II: Vertex function and beyond quantum $q$-Langlands correspondence
We continue the study of generalized gauge theory called gauge origami, based on the quantum algebraic approach initiated in [arXiv:2310.08545]. In this article, we in particular explore the D2 brane system realized by the screened vertex operators of the corresponding W-algebra. The partition function of this system given by the corresponding conformal block is identified with the vertex function associated with quasimaps to Nakajima quiver varieties and generalizations, that plays a central r…

This https://arxiv.org/abs/2502.02389 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Rate-reliability tradeoff for deterministic identification
We investigate deterministic identification over arbitrary memoryless channels under the constraint that the error probabilities of first and second kind are exponentially small in the block length $n$, controlled by reliability exponents $E_1,E_2 \geq 0$. In contrast to the regime of slowly vanishing errors, where the identifiable message length scales linearithmically as $Θ(n\log n)$, here we find that for positive exponents linear scaling is restored, now with a rate that is a function of t…

This https://arxiv.org/abs/2505.18975 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization
State Space Models (SSMs), like recent Mamba2, have achieved remarkable performance and received extensive attention. However, deploying Mamba2 on resource-constrained edge devices encounters many problems: severe outliers within the linear layer challenging the quantization, diverse and irregular element-wise tensor operations, and hardware-unfriendly nonlinear functions in the SSM block. To address these issues, this paper presents FastMamba, a dedicated accelerator on FPGA with hardware-algo…

This https://arxiv.org/abs/2305.03876 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

The nil-Brauer category
We introduce the nil-Brauer category and prove a basis theorem for its morphism spaces. This basis theorem is an essential ingredient required to prove that nil-Brauer categorifies the split iquantum group of rank one. As this iquantum group is a basic building block for $\imath$-quantum groups of higher rank, we expect that the nil-Brauer category will play a role in future developments related to the categorification of quantum symmetric pairs.

LCB-CV-UNet: Enhanced Detector for High Dynamic Range Radar Signals
Yanbin Wang, Xingyu Chen, Yumiao Wang, Xiang Wang, Chuanfei Zang, Guolong Cui, Jiahuan Liu
https://arxiv.org/abs/2505.23454

LCB-CV-UNet: Enhanced Detector for High Dynamic Range Radar Signals
We propose the LCB-CV-UNet to tackle performance degradation caused by High Dynamic Range (HDR) radar signals. Initially, a hardware-efficient, plug-and-play module named Logarithmic Connect Block (LCB) is proposed as a phase coherence preserving solution to address the inherent challenges in handling HDR features. Then, we propose the Dual Hybrid Dataset Construction method to generate a semi-synthetic dataset, approximating typical HDR signal scenarios with adjustable target distributions. Si…

Decoupling Periodic Systems: An Algebraic Approach
Vladim\'ir Ku\v{c}era
https://arxiv.org/abs/2505.23616 https://arxiv.org/pdf/2…

Decoupling Periodic Systems: An Algebraic Approach
This paper addresses the problem of row-by-row (or diagonal) decoupling of discrete-time linear multi-input multi-output systems with periodic time-varying coefficients using periodic state feedback. Previous solutions have tackled row-by-row decoupling using dynamic compensation for square systems and block-decoupling through regular state feedback for nonsquare systems with more outputs than inputs. While it appears likely that a row-by-row state feedback solution for square systems can be de…

This https://arxiv.org/abs/2410.20448 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

New results related to cutters and to an extrapolated block-iterative method for finding a common fixed point of a collection of them
Given a Hilbert space and a finite family of operators defined on the space, the common fixed point problem (CFPP) is to find a point in the intersection of the fixed point sets of these operators. Instances of the problem have numerous applications in science and engineering. We consider an extrapolated block-iterative method with dynamic weights for solving the CFPP assuming the operators belong to a wide class of operators called cutters. Global convergence is proved in two different scenari…

Fixed-Point Traps and Identity Emergence in Educational Feedback Systems
Faruk Alpay
https://arxiv.org/abs/2505.21038 https://arxiv.o…

Fixed-Point Traps and Identity Emergence in Educational Feedback Systems
This paper presents a formal categorical proof that exam-driven educational systems obstruct identity emergence and block creative convergence. Using the framework of Alpay Algebra II and III, we define Exam-Grade Collapse Systems (EGCS) as functorial constructs where learning dynamics $φ$ are recursively collapsed by evaluative morphisms $E$. We prove that under such collapse regimes, no nontrivial fixed-point algebra $μ_φ$ can exist, hence learner identity cannot stabilize. This creates a …

This https://arxiv.org/abs/2505.13814 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Articulatory Feature Prediction from Surface EMG during Speech Production
We present a model for predicting articulatory features from surface electromyography (EMG) signals during speech production. The proposed model integrates convolutional layers and a Transformer block, followed by separate predictors for articulatory features. Our approach achieves a high prediction correlation of approximately 0.9 for most articulatory features. Furthermore, we demonstrate that these predicted articulatory features can be decoded into intelligible speech waveforms. To our know…

This https://arxiv.org/abs/2412.20140 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Self-similarity on 4d cubic lattice
A phenomenon of "algebraic self-similarity" on 3d cubic lattice, providing what can be called an algebraic analogue of Kadanoff--Wilson theory, is shown to possess a 4d version as well. Namely, if there is a $4\times 4$ matrix $A$ whose entries are indeterminates over the field $\mathbb F_2$, then the $2\times 2\times 2\times 2$ block made of sixteen copies of $A$ reveals the existence of four direct "block spin" summands corresponding to the same matrix $A$. Moreover, these summands can be wri…

Replaced article(s) found for cs.GR. https://arxiv.org/list/cs.GR/new
[1/1]:
- HUG: Hierarchical Urban Gaussian Splatting with Block-Based Reconstruction for Large-Scale Aerial...
Mai Su, Zhongtao Wang, Huishan Au, Yilong Li, Xizhe Cao, Chengwei Pan, Yisong Chen, Guoping Wang

Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks
Manyi Li, Renshuai Tao, Yufan Liu, Chuangchuang Tan, Haotong Qin, Bing Li, Yunchao Wei, Yao Zhao
https://arxiv.org/abs/2506.20548

Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks
With the rapid advancement of deep learning, particularly through generative adversarial networks (GANs) and diffusion models (DMs), AI-generated images, or ``deepfakes", have become nearly indistinguishable from real ones. These images are widely shared across Online Social Networks (OSNs), raising concerns about their misuse. Existing deepfake detection methods overlook the ``block effects" introduced by compression in OSNs, which obscure deepfake artifacts, and primarily focus on raw images,…

PhasePoly: An Optimization Framework forPhase Polynomials in Quantum Circuits
Zihan Chen, Henry Chen, Yuwei Jin, Minghao Guo, Enhyeok Jang, Jiakang Li, Caitlin Chan, Won Woo Ro, Eddy Z. Zhang
https://arxiv.org/abs/2506.20624

PhasePoly: An Optimization Framework forPhase Polynomials in Quantum Circuits
Quantum computing has transformative computational power to make classically intractable computing feasible. As the algorithms that achieve practical quantum advantage are beyond manual tuning, quantum circuit optimization has become extremely important and integrated into today's quantum software stack. This paper focuses on a critical type of quantum circuit optimization -- phase-polynomial optimization. Phase polynomials represents a class of building-block circuits that appear frequently in…

Quantum master equation for nanoelectromechanical systems beyond the wide-band limit
Sofia Sevitz, Federico Cerisola, Janet Anders
https://arxiv.org/abs/2506.20593

Quantum master equation for nanoelectromechanical systems beyond the wide-band limit
Coupling the vibrations of an oscillator to electronic transport is a key building block for nanoelectromechanical systems. They describe many nanoscale electrical components such as molecular junctions. Inspired by recent experimental developments, we derive a quantum master equation that describes nanoelectromechanical systems in a generally overlooked situation: when the electronic transport is slower than the natural frequency of the oscillator. Here, a semi-classical model is no longer val…

Efficient Strategy Synthesis for MDPs via Hierarchical Block Decomposition
Alexandros Evangelidis, Gricel V\'azquez, Simos Gerasimou
https://arxiv.org/abs/2506.17792

Efficient Strategy Synthesis for MDPs via Hierarchical Block Decomposition
Software-intensive systems, such as software product lines and robotics, utilise Markov decision processes (MDPs) to capture uncertainty and analyse sequential decision-making problems. Despite the usefulness of conventional policy synthesis methods, they fail to scale to large state spaces. Our approach addresses this issue and accelerates policy synthesis in large MDPs by dynamically refining the MDP and iteratively selecting the most fragile MDP regions for refinement. This iterative procedu…

Bayesian community detection in assortative stochastic block model with unknown number of communities
Martina Amongero, Pierpaolo De Blasi
https://arxiv.org/abs/2506.19576

Bayesian community detection in assortative stochastic block model with unknown number of communities
Structured data in the form of networks is increasingly common in a number of fields, including social sciences, biology, physics, computer science, and many others. A key task in network analysis is community detection, which typically consists of dividing the nodes into groups such that nodes within a group are strongly connected, while connections between groups are relatively scarcer. A generative model well-suited for the formation of such communities is the assortative stochastic block mo…

Faster Low-Rank Approximation and Kernel Ridge Regression via the Block-Nystr\"om Method
Sachin Garg, Micha{\l} Derezi\'nski
https://arxiv.org/abs/2506.17556

Faster Low-Rank Approximation and Kernel Ridge Regression via the Block-Nyström Method
The Nyström method is a popular low-rank approximation technique for large matrices that arise in kernel methods and convex optimization. Yet, when the data exhibits heavy-tailed spectral decay, the effective dimension of the problem often becomes so large that even the Nyström method may be outside of our computational budget. To address this, we propose Block-Nyström, an algorithm that injects a block-diagonal structure into the Nyström method, thereby significantly reducing its computati…

On Exponential Random Graph Models with Dyadic Independence
Kayvan Sadeghi
https://arxiv.org/abs/2506.20458 https://arxiv.org/pdf/250…

On Exponential Random Graph Models with Dyadic Independence
We show that the only exponential random graph model with n nodal parameters, dyads being independent, and the natural assumption of permutation-equivariant nodal parametrization is the \b{eta} model. In addition, we show that an exponential random graph model with similar assumptions but with fewer than n block parameters is the additive stochastic block model. We also provide similar results for directed networks

Monolithic and Block Overlapping Schwarz Preconditioners for the Incompressible Navier--Stokes Equations
Alexander Heinlein, Axel Klawonn, Jascha Knepper, Lea Sa{\ss}mannshausen
https://arxiv.org/abs/2506.16179

Monolithic and Block Overlapping Schwarz Preconditioners for the Incompressible Navier--Stokes Equations
Monolithic preconditioners applied to the linear systems arising during the solution of the discretized incompressible Navier--Stokes equations are typically more robust than preconditioners based on incomplete block factorizations. Lower number of iterations and a reduced sensitivity to parameters like velocity and viscosity can significantly outweigh the additional cost for their setup. Different monolithic preconditioning techniques are introduced and compared to a selection of block precond…

Block Coordinate Descent Network Simplex for Optimal Transport
Lingrui Li, Nobuo Yamashita
https://arxiv.org/abs/2506.21231 https://a…

Block Coordinate Descent Network Simplex for Optimal Transport
We propose the Block Coordinate Descent Network Simplex (BCDNS) method for solving large-scale discrete Optimal Transport (OT) problems. BCDNS integrates the Network Simplex (NS) algorithm with a block coordinate descent (BCD) strategy, decomposing the full problem into smaller subproblems per iteration and reusing basis variables to ensure feasibility. We prove that BCDNS terminates in a finite number of iterations with an exact optimal solution, and we characterize its per-iteration complexit…

Flexible MOF Generation with Torsion-Aware Flow Matching
Nayoung Kim, Seongsu Kim, Sungsoo Ahn
https://arxiv.org/abs/2505.17914 https://

Flexible MOF Generation with Torsion-Aware Flow Matching
Designing metal-organic frameworks (MOFs) with novel chemistries is a long-standing challenge due to their large combinatorial space and the complex 3D arrangements of building blocks. While recent deep generative models have enabled scalable MOF generation, they assume (1) a fixed set of building blocks and (2) known ground-truth local block-wise 3D coordinates. However, this limits their ability to (1) design novel MOFs and (2) generate the structure using novel building blocks. We propose a …

Normal and Dynamical Typicality in a Random Matrix Model
L\'aszl\'o Erd\H{o}s, Joscha Henheik, Cornelia Vogel
https://arxiv.org/abs/2506.17177 http…

Normal and Dynamical Typicality in a Random Matrix Model
We prove normal typicality and dynamical typicality for a (centered) random block-band matrix model with block-dependent variances. A key feature of our model is that we achieve intermediate equilibration times, an aspect that has not been proven rigorously in any model before. Our proof builds on recently established concentration estimates for products of resolvents of Wigner type random matrices [arXiv:2403.10359] and an intricate analysis of the deterministic approximation.

Identifying Heterogeneity in Distributed Learning
Zelin Xiao, Jia Gu, Song Xi Chen
https://arxiv.org/abs/2506.16394 https://arxiv.org…

Identifying Heterogeneity in Distributed Learning
We study methods for identifying heterogeneous parameter components in distributed M-estimation with minimal data transmission. One is based on a re-normalized Wald test, which is shown to be consistent as long as the number of distributed data blocks $K$ is of a smaller order of the minimum block sample size {and the level of heterogeneity is dense}. The second one is an extreme contrast test (ECT) based on the difference between the largest and smallest component-wise estimated parameters amo…

Block Tensor Decomposition: A dual grid scheme with formal O(N3) for THC decomposition of molecular systems
Yueyang Zhang, Xuewei Xiong, Wei Wu, Peifeng Su
https://arxiv.org/abs/2506.19392

Block Tensor Decomposition: A dual grid scheme with formal O(N3) for THC decomposition of molecular systems
Accurate and fast treatment of electron-electron interactions remains a central challenge in electronic structure theory because post-Hartree-Fock methods often suffered from the computational cost for 4-index electron repulsion integrals (ERIs). Low-rank approaches such as tensor hyper-contraction (THC) and interpolative separable density fitting (ISDF) have been proposed for Hartree-Fock exchange and correlation's calculations. Their application to molecular systems remains inefficient due to…

Odd-indexed Fibonacci numbers via pattern-avoiding permutations
Juan B. Gil, Felix H. Xu, William Y. Zhu
https://arxiv.org/abs/2506.15800 https://

Odd-indexed Fibonacci numbers via pattern-avoiding permutations
In this paper, we consider several combinatorial problems whose enumeration leads to the odd-indexed Fibonacci numbers, including certain types of Dyck paths, block fountains, directed column-convex polyominoes, and set partitions with no crossings and no nestings. Our goal is to provide bijective maps to pattern-avoiding permutations and derive generating functions that track certain positional statistics at the permutation level.

An adversary bound for quantum signal processing
Lorenzo Laneve
https://arxiv.org/abs/2506.20484 https://arxiv.org/pdf/2506.20484

An adversary bound for quantum signal processing
Quantum signal processing (QSP) and quantum singular value transformation (QSVT), have emerged as unifying frameworks in the context of quantum algorithm design. These techniques allow to carry out efficient polynomial transformations of matrices block-encoded in unitaries, involving a single ancilla qubit. Recent efforts try to extend QSP to the multivariate setting (M-QSP), where multiple matrices are transformed simultaneously. However, this generalization faces problems not encountered in t…

Semidefinite Programming for the Asymmetric Stochastic Block Model
Julia Gaudio, Phawin Prongpaophan
https://arxiv.org/abs/2506.18754 https://

Semidefinite Programming for the Asymmetric Stochastic Block Model
We consider semidefinite programming (SDP) for the binary stochastic block model with equal-sized communities. Prior work of Hajek, Wu, and Xu proposed an SDP (sym-SDP) for the symmetric case where the intra-community edge probabilities are equal, and showed that the SDP achieves the information-theoretic threshold for exact recovery under the symmetry assumption. A key open question is whether SDPs can be used to achieve exact recovery for non-symmetric block models. In order to inform the des…

Ladies and gentlemen and others, this is why I recommend hosting your own forge, like forgejo: https://mastodon.social/@mcc/114536667832141959
Also, as I recently discovered: Github git implementation is pretty dumb and reports unsolvable conflicts that are automatically solved by…

Estimating Extreme Wave Surges in the Presence of Missing Data
James H. McVittie, Orla A. Murphy
https://arxiv.org/abs/2506.15970 https://

Estimating Extreme Wave Surges in the Presence of Missing Data
The block maxima approach, which consists of dividing a series of observations into equal sized blocks to extract the block maxima, is commonly used for identifying and modelling extreme events using the generalized extreme value (GEV) distribution. In the analysis of coastal wave surge levels, the underlying data which generate the block maxima typically have missing observations. Consequently, the observed block maxima may not correspond to the true block maxima yielding biased estimates of t…

Low-Complexity Ordered Reliability Direct Error Pattern Testing (ORDEPT) Decoding with Likelihood Thresholding
Reza Hadavian, Dmitri Truhachev
https://arxiv.org/abs/2506.20079

Low-Complexity Ordered Reliability Direct Error Pattern Testing (ORDEPT) Decoding with Likelihood Thresholding
We propose a reduced complexity approach to pattern-based soft decoding of block codes. We start from the ORDEPT decoding algorithm which tests a list of partial error patterns organized in the order of their likelihood and attempts to complete the patterns creating candidate codewords. We then propose an early termination criterion. Once a candidate codeword is found, its log-likelihood difference to the received sequence is compared to a preset threshold and the decoding decision is instantly…

The Rich Get Richer in Bitcoin Mining Induced by Blockchain Forks
Akira Sakurai, Kazuyuki Shudo
https://arxiv.org/abs/2506.13360 https://

The Rich Get Richer in Bitcoin Mining Induced by Blockchain Forks
Bitcoin is a representative decentralized currency system. For the security of Bitcoin, fairness in the distribution of mining rewards plays a crucial role in preventing the concentration of computational power in a few miners. Here, fairness refers to the distribution of block rewards in proportion to contributed computational resources. If miners with greater computational resources receive disproportionately higher rewards, i.e., if the Rich Get Richer (TRGR) phenomenon holds in Bitcoin, it …

PhyBlock: A Progressive Benchmark for Physical Understanding and Planning via 3D Block Assembly
Liang Ma, Jiajun Wen, Min Lin, Rongtao Xu, Xiwen Liang, Bingqian Lin, Jun Ma, Yongxin Wang, Ziming Wei, Haokun Lin, Mingfei Han, Meng Cao, Bokui Chen, Ivan Laptev, Xiaodan Liang
https://arxiv.org/abs/2506.08708

PhyBlock: A Progressive Benchmark for Physical Understanding and Planning via 3D Block Assembly
While vision-language models (VLMs) have demonstrated promising capabilities in reasoning and planning for embodied agents, their ability to comprehend physical phenomena, particularly within structured 3D environments, remains severely limited. To close this gap, we introduce PhyBlock, a progressive benchmark designed to assess VLMs on physical understanding and planning through robotic 3D block assembly tasks. PhyBlock integrates a novel four-level cognitive hierarchy assembly task alongside …

Asymptotic theory for the likelihood-based block maxima method in time series
David L. Carl, Simone A. Padoan, Stefano Rizzelli
https://arxiv.org/abs/2506.17448

Asymptotic theory for the likelihood-based block maxima method in time series
This paper develops a rigorous asymptotic framework for likelihood-based inference in the Block Maxima (BM) method for stationary time series. While Bayesian inference under the BM approach has been widely studied in the independence setting, no asymptotic theory currently exists for time series. Further results are needed to establish that BM method can be applied with the kind of dependent time series models relevant to applied fields. To address this gap we first establish a comprehensive li…

Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
Piotr Gai\'nski, Oussama Boussif, Andrei Rekesh, Dmytro Shevchuk, Ali Parviz, Mike Tyers, Robert A. Batey, Micha{\l} Koziarski
https://arxiv.org/abs/2506.19865

Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
Template-based molecular generation offers a promising avenue for drug design by ensuring generated compounds are synthetically accessible through predefined reaction templates and building blocks. In this work, we tackle three core challenges in template-based GFlowNets: (1) minimizing synthesis cost, (2) scaling to large building block libraries, and (3) effectively utilizing small fragment sets. We propose \textbf{Recursive Cost Guidance}, a backward policy framework that employs auxiliary m…

Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning
Yujia Huo, Jianchun Liu, Hongli Xu, Zhenguo Ma, Shilong Wang, Liusheng Huang
https://arxiv.org/abs/2506.05977

Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning
Federated fine-tuning (FedFT) of large language models (LLMs) has emerged as a promising solution for adapting models to distributed data environments while ensuring data privacy. Existing FedFT methods predominantly utilize parameter-efficient fine-tuning (PEFT) techniques to reduce communication and computation overhead. However, they often fail to adequately address the catastrophic forgetting, a critical challenge arising from continual adaptation in distributed environments. The tradit…

Krylov and core transformation algorithms for an inverse eigenvalue problem to compute recurrences of multiple orthogonal polynomials
Amin Faghih, Michele Rinelli, Marc Van Barel, Raf Vandebril, Robbe Vermeiren
https://arxiv.org/abs/2506.19796

Krylov and core transformation algorithms for an inverse eigenvalue problem to compute recurrences of multiple orthogonal polynomials
In this paper, we develop algorithms for computing the recurrence coefficients corresponding to multiple orthogonal polynomials on the step-line. We reformulate the problem as an inverse eigenvalue problem, which can be solved using numerical linear algebra techniques. We consider two approaches: the first is based on the link with block Krylov subspaces and results in a biorthogonal Lanczos process with multiple starting vectors; the second consists of applying a sequence of Gaussian eliminati…

Higher-Oder Splitting Schemes for Fluids with Variable Viscosity
Richard Schussnig, Niklas Fehn, Douglas Ramalho Queiroz Pacheco, Martin Kronbichler
https://arxiv.org/abs/2506.14424

Higher-Oder Splitting Schemes for Fluids with Variable Viscosity
This article investigates matrix-free higher-order discontinuous Galerkin (DG) discretizations of the Navier-Stokes equations for incompressible flows with variable viscosity. The viscosity field may be prescribed analytically or governed by a rheological law, as often found in biomedical or industrial applications. We compare several linearized variants of saddle point block systems and projection-based splitting time integration schemes in terms of their computational performance. Compared to…

From Block to Byte: Transforming PCIe SSDs with CXL Memory Protocol and Instruction Annotation
Miryeong Kwon, Donghyun Gouk, Junhyeok Jang, Jinwoo Baek, Hyunwoo You, Sangyoon Ji, Hongjoo Jung, Junseok Moon, Seungkwan Kang, Seungjun Lee, Myoungsoo Jung
https://arxiv.org/abs/2506.15613

From Block to Byte: Transforming PCIe SSDs with CXL Memory Protocol and Instruction Annotation
This paper explores how Compute Express Link (CXL) can transform PCIe-based block storage into a scalable, byte-addressable working memory. We address the challenges of adapting block storage to CXL's memory-centric model by emphasizing cacheability as a key enabler and advocating for Type 3 endpoint devices, referred to as CXL-SSDs. To validate our approach, we prototype a CXL-SSD on a custom FPGA platform and propose annotation mechanisms, Determinism and Bufferability, to enhance performance…

This https://arxiv.org/abs/2503.24207 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Definability of mad families of vector spaces and two local Ramsey theories
Let $E$ be a vector space over a countable field of dimension $\aleph_0$. Two infinite-dimensional subspaces $V,W \subseteq E$ are almost disjoint if $V \cap W$ is finite-dimensional. This paper provides some improvements on results about the definability of maximal almost disjoint families (mad families) of subspaces in [17]. We show that a full mad family of block subspaces exists assuming either $\frak{p} = \max\{\frak{b},\frak{s}\}$ or a positive answer to a problem in [17], improving Smyth…

Effective Stimulus Propagation in Neural Circuits: Driver Node Selection
Bulat Batuev, Arsenii Onuchin, Sergey Sukhov
https://arxiv.org/abs/2506.13615 http…

Effective Stimulus Propagation in Neural Circuits: Driver Node Selection
Precise control of signal propagation in modular neural networks represents a fundamental challenge in computational neuroscience. We establish a framework for identifying optimal control nodes that maximize stimulus transmission between weakly coupled neural populations. Using spiking stochastic block model networks, we systematically compare driver node selection strategies - including random sampling and topology-based centrality measures (degree, betweenness, closeness, eigenvector, harmoni…

This https://arxiv.org/abs/2502.04097 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Impermanent loss and Loss-vs-Rebalancing II
This paper examines the relationship between impermanent loss (IL) and loss-versus-rebalancing (LVR) in automated market makers (AMMs). Our main focus is on statistical properties, the impact of fees, the role of block times, and, related to the latter, the continuous time limit. We find there are three relevant regimes: (i) very short times where LVR and IL are identical; (ii) intermediate time where LVR and IL show distinct distribution functions but are connected via the central limit theore…

Ah yes, I really dislike it when, as someone who is autistic, I don’t look before I accidentally block the entire Mastodon.social domain instead of just blocking a specific bot user.
Great, now over 100 lost me in their feeds. My day is ruined.
Domain unblocked, please boost for my visibility again!
#ActuallyAutistic

Environmental extreme risk modeling via sub-sampling block maxima
Tuoyuan Cheng, Xiao Peng, Achmad Choiruddin, Xiaogang He, Kan Chen
https://arxiv.org/abs/2506.14556

Environmental extreme risk modeling via sub-sampling block maxima
This paper introduces a novel sub-sampling block maxima technique to model and characterize environmental extreme risks. We examine the relationships between block size and block maxima statistics derived from the Gaussian and generalized Pareto distributions. We introduce a weighted least square estimator for extreme value index (EVI) and evaluate its performance using simulated auto-correlated data. We employ the second moment of block maxima for plateau finding in EVI and extremal index (EI)…

This https://arxiv.org/abs/2505.05358 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Empirical Analysis of Transaction Conflicts in Ethereum and Solana for Parallel Execution
This paper presents a comprehensive analysis of historical data across two popular blockchain networks: Ethereum and Solana. Our study focuses on two key aspects: transaction conflicts and the maximum theoretical parallelism within historical blocks. We aim to quantify the degree of transaction parallelism and assess how effectively it can be exploited by systematically examining block-level characteristics, both within individual blocks and across different historical periods. In particular, t…

Accelerating ground-state auxiliary-field quantum Monte Carlo simulations by delayed update and block force-bias update
Hao Du, Yuan-Yao He
https://arxiv.org/abs/2506.09611

Accelerating ground-state auxiliary-field quantum Monte Carlo simulations by delayed update and block force-bias update
Ground-state auxiliary-field quantum Monte Carlo (AFQMC) methods have become key numerical tools for studying quantum phases and phase transitions in interacting many-fermion systems. Despite the broad applicability, the efficiency of these algorithms is often limited by the bottleneck associated with the {\it local update} of the field configuration. In this work, we propose two novel update schemes, the {\it delayed update} and {\it block force-bias update}, both of which can generally and ef…

Local perturbations of block Toeplitz matrices
Lars Koekenbier, Hermann Schulz-Baldes
https://arxiv.org/abs/2506.12757 https://arxiv.…

Local perturbations of block Toeplitz matrices
This work is about the asymptotic spectral theory of tridiagonal Toeplitz matrices with matrix entries, with periodicity broken on a finite number of entries. Varying the ranks of these perturbations allow to interpolate between open boundary and circulant Toeplitz matrices. While the continuous part of the limit spectrum only depends on these ranks and no other aspect of the perturbation, the outliers of the spectrum depend continuously on the local perturbation. The proof is essentially based…

High Socioeconomic Status is Associated with Diverse Consumption across Brands and Price Levels
Yuanmo He, Milena Tsvetkova
https://arxiv.org/abs/2506.13840

High Socioeconomic Status is Associated with Diverse Consumption across Brands and Price Levels
Consumption practices are determined by a combination of economic, social, and cultural forces. We posit that lower economic constraints leave more room to diversify consumption along cultural and social aspects in the form of omnivorous or lifestyle-based niche consumption. We provide empirical evidence for this diversity hypothesis by analysing millions of mobile-tracked visits from thousands of Census Block Groups to thousands of stores in New York State. The results show that high income is…

The Rich Get Richer in Bitcoin Mining Induced by Blockchain Forks
Akira Sakurai, Kazuyuki Shudo
https://arxiv.org/abs/2506.13360 https://

The Rich Get Richer in Bitcoin Mining Induced by Blockchain Forks
Bitcoin is a representative decentralized currency system. For the security of Bitcoin, fairness in the distribution of mining rewards plays a crucial role in preventing the concentration of computational power in a few miners. Here, fairness refers to the distribution of block rewards in proportion to contributed computational resources. If miners with greater computational resources receive disproportionately higher rewards, i.e., if the Rich Get Richer (TRGR) phenomenon holds in Bitcoin, it …

Replaced article(s) found for math.NA. https://arxiv.org/list/math.NA/new
[1/1]:
- A block Recycled GMRES method with investigations into aspects of solver performance
Michael L. Parks, Kirk M. Soodhalter, Daniel B. Szyld

Micelle Forming Linear-Dendritic Block Copolymers: A Theoretical Comparison between Random Hyperbranched and Precise Dendrimer Polymer Architectures
Marios Giannakou, Oleg V. Borisov, Friederike Schmid
https://arxiv.org/abs/2506.01765

Micelle Forming Linear-Dendritic Block Copolymers: A Theoretical Comparison between Random Hyperbranched and Precise Dendrimer Polymer Architectures
Hyperbranched block copolymers offer a simpler and more efficient synthesis route compared to more traditional dendritic systems, while still providing exceptional control over surface functionality and self-assembly. This makes them ideal candidates for engineering nanoparticles with tailored properties for applications such as drug delivery and sensing. Here we use self-consistent field calculations to compare the micelle structures formed by copolymers with a polydisperse hyperbranched (LHBC…

BPS polynomials and Welschinger invariants
H\"ulya Arg\"uz, Pierrick Bousseau
https://arxiv.org/abs/2506.02770 https://arxi…

BPS polynomials and Welschinger invariants
We generalize Block-Göttsche polynomials, originally defined for toric del Pezzo surfaces, to arbitrary surfaces. To do this, we show that these polynomials arise as special cases of BPS polynomials, defined for any surface $S$ as Laurent polynomials in a formal variable $q$ encoding the BPS invariants of the $3$-fold $S \times \mathbb{P}^1$. We conjecture that for surfaces $S_n$ obtained by blowing up $\mathbb{P}^2$ at $n$ general points, the evaluation of BPS polynomials at $q=-1$ yields Wel…

Residual 1D CNN for Low SFR Surface Density Regression: A Design Note
Po-Chieh Yu
#toXiv_bot_toot

Weighted Parameter Estimators of the Generalized Extreme Value Distribution in the Presence of Missing Observations
James H. McVittie, Orla A. Murphy
https://arxiv.org/abs/2506.15964

Weighted Parameter Estimators of the Generalized Extreme Value Distribution in the Presence of Missing Observations
Missing data occur in a variety of applications of extreme value analysis. In the block maxima approach to an extreme value analysis, missingness is often handled by either ignoring missing observations or dropping a block of observations from the analysis. However, in some cases, missingness may occur due to equipment failure during an extreme event, which can lead to bias in estimation. In this work, we propose weighted maximum likelihood and weighted moment-based estimators for the generaliz…

Safe Screening Rules for Group SLOPE
Runxue Bao, Quanchao Lu, Yanfu Zhang
https://arxiv.org/abs/2506.09451 https://arxiv.org/pdf/2506…

Safe Screening Rules for Group SLOPE
Variable selection is a challenging problem in high-dimensional sparse learning, especially when group structures exist. Group SLOPE performs well for the adaptive selection of groups of predictors. However, the block non-separable group effects in Group SLOPE make existing methods either invalid or inefficient. Consequently, Group SLOPE tends to incur significant computational costs and memory usage in practical high-dimensional scenarios. To overcome this issue, we introduce a safe screening …

Non-conformal Line Defect (Shell Operator) in AdS$_3$/CFT$_2$: Spinning and Higher Point Correlators
Yuefeng Liu, Boyang Yu
https://arxiv.org/abs/2506.12711

Non-conformal Line Defect (Shell Operator) in AdS$_3$/CFT$_2$: Spinning and Higher Point Correlators
Recently, a special type of non-conformal line defect, known as thin-shell operator, has played a key role in demonstrating the chaotic nature of the high energy sector in AdS$_3$/CFT$_2$. The chaotic nature was revealed concretely through a matching among the vacuum Virasoro block in holographic CFT$_2$, ETH analysis, and gravitational on-shell partition function in AdS$_3$ with nontrivial backreaction. In this work, we generalize this matching in two ways. First, we compute two-point correlat…

A Chimera method for high-fidelity simulation of turbulent flows
Andrea Di Mascio, Sergio Pirozzoli
https://arxiv.org/abs/2506.05914 https://

A Chimera method for high-fidelity simulation of turbulent flows
We develop a block-structured solver for high-fidelity simulation of flows in complex geometries, based on overlapping (Chimera) meshes. The key components of the algorithm are a baseline dissipation-free central discretization and selective high-order filtering, which ensure uniform accuracy and minimal numerical diffusion. These favorable properties are preserved through efficient interpolation across overlapping blocks. Numerical tests demonstrate that the method guarantees a uniform order o…

This https://arxiv.org/abs/2505.09400 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Structured coalescents, coagulation equations and multi-type branching processes
Consider a structured population consisting of $d$ colonies, with migration rates that are proportional to a positive parameter $K$. We sample $N_K$ individuals distributed evenly across the $d$ colonies and trace their ancestral lineages back. Within a colony, we assume that pairs of ancestral lineages coalesce at a constant rate, as in a Kingman's coalescent. We identify each ancestral lineage with the set, or block, of its descendants in the sample and we encode the state of the system using…

On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization
Undral Byambadalai, Tomu Hirata, Tatsushi Oka, Shota Yasui
https://arxiv.org/abs/2506.05945

On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization
This paper focuses on the estimation of distributional treatment effects in randomized experiments that use covariate-adaptive randomization (CAR). These include designs such as Efron's biased-coin design and stratified block randomization, where participants are first grouped into strata based on baseline covariates and assigned treatments within each stratum to ensure balance across groups. In practice, datasets often contain additional covariates beyond the strata indicators. We propose a fl…

Dynamic Fee for Reducing Impermanent Loss in Decentralized Exchanges
Irina Lebedeva, Dmitrii Umnov, Yury Yanovich, Ignat Melnikov, George Ovchinnikov
https://arxiv.org/abs/2506.03001

Dynamic Fee for Reducing Impermanent Loss in Decentralized Exchanges
Decentralized exchanges (DEXs) are crucial to decentralized finance (DeFi) as they enable trading without intermediaries. However, they face challenges like impermanent loss (IL), where liquidity providers (LPs) see their assets' value change unfavorably within a liquidity pool compared to outside it. To tackle these issues, we propose dynamic fee mechanisms over traditional fixed-fee structures used in automated market makers (AMM). Our solution includes asymmetric fees via block-adaptive, dea…

Quantum block Krylov subspace projector algorithm for computing low-lying eigenenergies
Maria Gabriela Jord\~ao Oliveira, Nina Glaser
https://arxiv.org/abs/2506.09971

Quantum block Krylov subspace projector algorithm for computing low-lying eigenenergies
Determining eigenvalues is a computationally expensive task that is crucial for countless applications in natural sciences. Toward this end, we introduce the quantum block Krylov subspace projector (QBKSP) algorithm, a multireference quantum variant of the Lanczos algorithm designed to accurately compute low-lying eigenvalues, including degenerate states. We present three different compact quantum circuits to evaluate the required expectation values, each suited to different problem settings. T…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new/
[1/2]:
Optimal Noise Reduction in Dense Mixed-Membership Stochastic Block Models under Diverging Spiked ...

This https://arxiv.org/abs/2412.07901 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Homophily Within and Across Groups
Homophily-the tendency to connect with similar others-plays a central role in shaping network structure and function. It is often treated as a uniform, global parameter, independent from other structural features. Here, we propose a maximum-entropy framework that decomposes homophily into contributions within and across groups, with the stochastic block model emerging as a special case. Our exponential-family formulation, parameterized by group size, fits real-world social networks well and all…

This https://arxiv.org/abs/2407.19353 has been replaced.
initial toot: https://mastoxiv.page/@arX…

A spring-block theory of feature learning in deep neural networks
Feature-learning deep nets progressively collapse data to a regular low-dimensional geometry. How this emerges from the collective action of nonlinearity, noise, learning rate, and other factors, has eluded first-principles theories built from microscopic neuronal dynamics. We exhibit a noise-nonlinearity phase diagram that identifies regimes where shallow or deep layers learn more effectively and propose a macroscopic mechanical theory that reproduces the diagram and links feature learning acr…

Energy Efficiency Optimization of Finite Block Length STAR-RIS-aided MU-MIMO Broadcast Channels
Mohammad Soleymani, Ignacio Santamaria, Eduard Jorswieck, Robert Schober, Lajos Hanzo
https://arxiv.org/abs/2506.11594

Energy Efficiency Optimization of Finite Block Length STAR-RIS-aided MU-MIMO Broadcast Channels
Energy-efficient designs are proposed for multi-user (MU) multiple-input multiple-output (MIMO) broadcast channels (BC), assisted by simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RIS) operating at finite block length (FBL). In particular, we maximize the sum energy efficiency (EE), showing that STAR-RIS can substantially enhance it. Our findings demonstrate that the gains of employing STAR-RIS increase when the codeword length and the maximum tolerable b…

Monotone and nonmonotone linearized block coordinate descent methods for nonsmooth composite optimization problems
Yassine Nabou, Lahcen El Bourkhissi, Sebastian U. Stich, Tuomo Valkonen
https://arxiv.org/abs/2506.12397

Monotone and nonmonotone linearized block coordinate descent methods for nonsmooth composite optimization problems
In this paper, we introduce both monotone and nonmonotone variants of LiBCoD, a \textbf{Li}nearized \textbf{B}lock \textbf{Co}ordinate \textbf{D}escent method for solving composite optimization problems. At each iteration, a random block is selected, and the smooth components of the objective are linearized along the chosen block in a Gauss-Newton approach. For the monotone variant, we establish a global sublinear convergence rate to a stationary point under the assumption of bounded iterates. …

Flag-transitive point-primitive quasi-symmetric $2$-designs and exceptional groups of Lie type
Jianbing Lu
https://arxiv.org/abs/2506.10266 https://…

Flag-transitive point-primitive quasi-symmetric $2$-designs and exceptional groups of Lie type
Let $\mathcal{D}$ be a non-trivial quasi-symmetric $2$-design with two block intersection numbers $x=0$ and $2\leq y\leq10$, and suppose that $G$ is an automorphism group of $\mathcal{D}$. If $G$ is flag-transitive and point-primitive, then it is known that $G$ is either of affine type or almost simple type. In this paper, we show that the socle of $G$ cannot be a finite simple exceptional group of Lie type.

Hardware Accelerated Neural Block Texture Compression with Cooperative Vectors
Belcour Laurent, Benyoub Anis
https://arxiv.org/abs/2506.06040 https://

Hardware Accelerated Neural Block Texture Compression with Cooperative Vectors
In this work, we present an extension to the neural texture compression method of Weinreich and colleagues [2024]. Like them, we leverage existing block compression methods which permit to use hardware texture filtering to store a neural representation of physically-based rendering (PBR) texture sets (including albedo, normal maps, roughness, etc.). However, we show that low dynamic range block compression formats still make the solution viable. Thanks to this, we show that we can achieve highe…

Online matching on stochastic block model
Maria Cherifa (MAP5, CREST), Cl\'ement Calauz\`enes (CREST), Vianney Perchet (CREST)
https://arxiv.org/abs/2506.04921

Online matching on stochastic block model
While online bipartite matching has gained significant attention in recent years, existing analyses in stochastic settings fail to capture the performance of algorithms on heterogeneous graphs, such as those incorporating inter-group affinities or other social network structures. In this work, we address this gap by studying online bipartite matching within the stochastic block model (SBM). A fixed set of offline nodes is matched to a stream of online arrivals, with connections governed probabi…

This https://arxiv.org/abs/2505.20404 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Co-Design of Soft Gripper with Neural Physics
For robot manipulation, both the controller and end-effector design are crucial. Soft grippers are generalizable by deforming to different geometries, but designing such a gripper and finding its grasp pose remains challenging. In this paper, we propose a co-design framework that generates an optimized soft gripper's block-wise stiffness distribution and its grasping pose, using a neural physics model trained in simulation. We derived a uniform-pressure tendon model for a flexure-based soft fin…

Accelerating Large-Scale Regularized High-Order Tensor Recovery
Wenjin Qin, Hailin Wang, Jingyao Hou, Jianjun Wang
https://arxiv.org/abs/2506.09594 https:/…

Accelerating Large-Scale Regularized High-Order Tensor Recovery
Currently, existing tensor recovery methods fail to recognize the impact of tensor scale variations on their structural characteristics. Furthermore, existing studies face prohibitive computational costs when dealing with large-scale high-order tensor data. To alleviate these issue, assisted by the Krylov subspace iteration, block Lanczos bidiagonalization process, and random projection strategies, this article first devises two fast and accurate randomized algorithms for low-rank tensor approx…

Tensor Manipulation Unit (TMU): Reconfigurable, Near-Memory Tensor Manipulation for High-Throughput AI SoC
Weiyu Zhou, Zheng Wang, Chao Chen, Yike Li, Yongkui Yang, Zhuoyu Wu, Anupam Chattopadhyay
https://arxiv.org/abs/2506.14364

Tensor Manipulation Unit (TMU): Reconfigurable, Near-Memory Tensor Manipulation for High-Throughput AI SoC
While recent advances in AI SoC design have focused heavily on accelerating tensor computation, the equally critical task of tensor manipulation, centered on high,volume data movement with minimal computation, remains underexplored. This work addresses that gap by introducing the Tensor Manipulation Unit (TMU), a reconfigurable, near-memory hardware block designed to efficiently execute data-movement-intensive operators. TMU manipulates long datastreams in a memory-to-memory fashion using a RIS…

This https://arxiv.org/abs/2412.03290 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Holographic reconstruction for AdS Wilson line networks and scalar Witten diagrams
We find a holographic reconstruction formula for gravitational Wilson line network operators in AdS$_2$ evaluated between Ishibashi states of the algebra $sl(2,\mathbb{R})$. It is given in integral form where the integrand is the global conformal block multiplied by a smearing function which is the product of the scalar bulk-to-boundary propagators. The integral can be explicitly calculated as multidimensional series of which arguments are rational functions of endpoint coordinates. In the case…

Mazzaroth: A High-Throughput DAG Consensus with State Root
Haohan Li
https://arxiv.org/abs/2506.01960 https://arxiv.org/pdf/2506.0196…

Mazzaroth: A High-Throughput DAG Consensus with State Root
Nakamoto Consensus achieves a decentralized ledger through a single-chain blockchain, assuming a maximum network delay, which limits block generation speed, resulting in low throughput. \cite{pg2018} (PG) enhances throughput using a blockDAG structure, but its probabilistic confirmation restricts smart contract applications. To address this, Mazzaroth proposes a Pow-based blockDAG consensus, employing a linear ordering algorithm to compute the \cite{eth} and achieve state finality, thereby supp…

DeepPolar : Breaking the BER-BLER Trade-off with Self-Attention and SMART (SNR-MAtched Redundancy Technique) decoding
Shubham Srivastava, Adrish Banerjee
https://arxiv.org/abs/2506.10166

DeepPolar+: Breaking the BER-BLER Trade-off with Self-Attention and SMART (SNR-MAtched Redundancy Technique) decoding
DeepPolar codes have recently emerged as a promising approach for channel coding, demonstrating superior bit error rate (BER) performance compared to conventional polar codes. Despite their excellent BER characteristics, these codes exhibit suboptimal block error rate (BLER) performance, creating a fundamental BER-BLER trade-off that severely limits their practical deployment in communication systems. This paper introduces DeepPolar+, an enhanced neural polar coding framework that systematicall…

This https://arxiv.org/abs/2503.14372 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Online ResNet-Based Adaptive Control for Nonlinear Target Tracking
A generalized ResNet architecture for adaptive control of nonlinear systems with black box uncertainties is developed. The approach overcomes limitations in existing methods by incorporating pre-activation shortcut connections and a zeroth layer block that accommodates different input-output dimensions. The developed Lyapunov-based adaptation law establishes exponential convergence to a neighborhood of the target state despite unknown dynamics and disturbances. Furthermore, the theoretical resu…

Character degrees in principal blocks for distinct primes
Annika Bartelt
https://arxiv.org/abs/2506.06860 https://arxiv.org/pdf/2506.…

Character degrees in principal blocks for distinct primes
Let $G$ be a finite group of order divisible by two distinct primes $p$ and $q$. We show that $G$ possesses a non-trivial irreducible character of degree not divisible by $p$ nor $q$ lying in both the principal $p$- and $q$-block whenever $G$ is one of the following: an alternating group $\mathfrak{A}_n$, $n\geq 4$, a symmetric group $\mathfrak{S}_n$, $n\geq 3$, or a finite simple classical group of type A, B, or C, defined in characteristic distinct from $p$ and $q$. This extends earlier resul…

HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention
Miguel Camelo Botero, Esra Aycan Beyazit, Nina Slamnik-Krije\v{s}torac, Johann M. Marquez-Barja
https://arxiv.org/abs/2506.13408

HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention
Accurate channel estimation is critical for high-performance Orthogonal Frequency-Division Multiplexing systems such as 5G New Radio, particularly under low signal-to-noise ratio and stringent latency constraints. This letter presents HELENA, a compact deep learning model that combines a lightweight convolutional backbone with two efficient attention mechanisms: patch-wise multi-head self-attention for capturing global dependencies and a squeeze-and-excitation block for local feature refinement…

FastFLUX: Pruning FLUX with Block-wise Replacement and Sandwich Training
Fuhan Cai, Yong Guo, Jie Li, Wenbo Li, Xiangzhong Fang, Jian Chen
https://arxiv.org/abs/2506.10035

FastFLUX: Pruning FLUX with Block-wise Replacement and Sandwich Training
Recent advancements in text-to-image (T2I) generation have led to the emergence of highly expressive models such as diffusion transformers (DiTs), exemplified by FLUX. However, their massive parameter sizes lead to slow inference, high memory usage, and poor deployability. Existing acceleration methods (e.g., single-step distillation and attention pruning) often suffer from significant performance degradation and incur substantial training costs. To address these limitations, we propose FastFLU…

A note on two cycles of consecutive even lengths in graphs
Binlong Li, Yufeng Pan, Lingjuan Shi
https://arxiv.org/abs/2506.08692 https://

A note on two cycles of consecutive even lengths in graphs
Bondy and Vince proved that a graph of minimum degree at least three contains two cycles whose lengths differ by one or two, which was conjectured by Erdős. Gao, Li, Ma and Xie gave an average degree counterpart of Bondy-Vince's result, stating that every $n$-vertex graph with at least $\frac{5}{2}(n-1)$ edges contains two cycles of consecutive even lengths, unless $4|(n-1)$ and every block of $G$ is a clique $K_5$. This confirms the case $k=2$ of Verstraëte's conjecture, which states th…

Multi-Timescale Gradient Sliding for Distributed Optimization
Junhui Zhang, Patrick Jaillet
https://arxiv.org/abs/2506.15387 https://…

Multi-Timescale Gradient Sliding for Distributed Optimization
We propose two first-order methods for convex, non-smooth, distributed optimization problems, hereafter called Multi-Timescale Gradient Sliding (MT-GS) and its accelerated variant (AMT-GS). Our MT-GS and AMT-GS can take advantage of similarities between (local) objectives to reduce the communication rounds, are flexible so that different subsets (of agents) can communicate at different, user-picked rates, and are fully deterministic. These three desirable features are achieved through a block-d…

This https://arxiv.org/abs/2405.15089 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Targeted Nakamoto: A Bitcoin Protocol to Balance Network Security and Energy Consumption
In a Proof-of-Work blockchain such as Bitcoin mining hashrate is increasing in the block reward. An increase in hashrate reduces network vulnerability to attack (a reduction in security cost) while increasing carbon emissions and electricity cost (an increase in externalities cost). This implies a tradeoff in total cost at different levels of hashrate and the existence of a hashrate interval where total cost is minimized. Targeted Nakamoto is a Proof-of-Work protocol augmentation that incentivi…

Effective criteria for entanglement witnesses in small dimensions
{\L}ukasz Grzelka, {\L}ukasz Skowronek, Karol \.Zyczkowski
https://arxiv.org/abs/2506.09298

Effective criteria for entanglement witnesses in small dimensions
We present an effective set of necessary and sufficient criteria for block-positivity of matrices of order $4$ over $\mathbb{C}$. The approach is based on Sturm sequences and quartic polynomial positivity conditions presented in recent literature. The procedure allows us to test whether a given $4\times 4$ complex matrix corresponds to an entanglement witness, and it is exact when the matrix coefficients belong to the rationals, extended by $\mathrm{i}$. The method can be generalized to $\mathc…

Network Cross-Validation for Nested Models by Edge-Sampling: Selection Consistency
Bokai Yang
https://arxiv.org/abs/2506.14244 https://

Network Cross-Validation for Nested Models by Edge-Sampling: Selection Consistency
In the network literature, a wide range of statistical models have been proposed to exploit structural patterns in the data. Therefore, model selection between different models is a fundamental problem. Cross-validation is a powerful candidate to solve this problem, and Li et al. have already proposed an edge-sampling procedure to choose the number of communities in the block model framework. In this paper, we propose a penalized edge-sampling cross-validation framework for nested network model…

This https://arxiv.org/abs/2411.07077 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A stable one-synchronization variant of reorthogonalized block classical Gram--Schmidt
The block classical Gram--Schmidt (BCGS) algorithm and its reorthogonalized variant are widely-used methods for computing the economic QR factorization of block columns $X$ due to their lower communication cost compared to other approaches such as modified Gram--Schmidt and Householder QR. To further reduce communication, i.e., synchronization, there has been a long ongoing search for a variant of reorthogonalized BCGS variant that achieves $O(u)$ loss of orthogonality while requiring only \emp…

Bipartite entanglement extracted from multimode squeezed light generated in lossy waveguides
Denis A. Kopylov, Torsten Meier, Polina R. Sharapova
https://arxiv.org/abs/2506.09587 …

Bipartite entanglement extracted from multimode squeezed light generated in lossy waveguides
Entangled two-mode Gaussian states constitute an important building block for continuous variable quantum computing and communication protocols and are thus of high demand for many experiments. In this work, we study such kind of states which are extracted from multimode light generated via type-II parametric down-conversion (PDC) in lossy waveguides. For such states, we demonstrate that the squeezing quantifies entanglement and we construct a measurement basis which results in the maximal bipa…

Semi-supervised Community Detection using Glauber Dynamics for an Ising Model
Konstantin Avrachenkov, Diego Goldsztajn
https://arxiv.org/abs/2506.09223 htt…

Semi-supervised Community Detection using Glauber Dynamics for an Ising Model
We consider graphs with two communities and analyze an algorithm for learning the community labels when the edges of the graph and only a small fraction of the labels are known in advance. The algorithm is based on the Glauber dynamics for an Ising model where the energy function includes a quadratic penalty on the magnetization. The analysis focuses on graphs sampled from a Stochastic Block Model (SBM) with slowly growing mean degree. We derive a mean-field limit for the magnetization of each …

This https://arxiv.org/abs/2412.16387 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Information Limits of Joint Community Detection and Finite Group Synchronization
The emerging problem of joint community detection and group synchronization, with applications in signal processing and machine learning, has been extensively studied in recent years. Previous research has predominantly focused on a statistical model that extends the stochastic block model~(SBM) by incorporating additional group transformations. In its simplest form, the model randomly generates a network of size $n$ that consists of two equal-sized communities, where each node $i$ is associate…

Strongly Consistent Community Detection in Popularity Adjusted Block Models
Quan Yuan, Binghui Liu, Danning Li, Lingzhou Xue
https://arxiv.org/abs/2506.07224

Strongly Consistent Community Detection in Popularity Adjusted Block Models
The Popularity Adjusted Block Model (PABM) provides a flexible framework for community detection in network data by allowing heterogeneous node popularity across communities. However, this flexibility increases model complexity and raises key unresolved challenges, particularly in effectively adapting spectral clustering techniques and efficiently achieving strong consistency in label recovery. To address these challenges, we first propose the Thresholded Cosine Spectral Clustering (TCSC) algor…

Efficient multigrid solvers for mixed-degree local discontinuous Galerkin multiphase Stokes problems
Robert I. Saye
https://arxiv.org/abs/2506.09933 https:…

Efficient multigrid solvers for mixed-degree local discontinuous Galerkin multiphase Stokes problems
We design and investigate efficient multigrid solvers for multiphase Stokes problems discretised via mixed-degree local discontinuous Galerkin methods. Using the template of a standard multigrid V-cycle, we develop a smoother analogous to element-wise block Gauss-Seidel, except the diagonal block inverses are replaced with an approximation that balances the smoothing of the velocity and pressure variables, factoring in the unequal scaling of the various Stokes system operators, and optimised vi…

A Neural Network-aided Low Complexity Chase Decoder for URLLC
Enrico Testi, Enrico Paolini
https://arxiv.org/abs/2506.10513 https://a…

A Neural Network-aided Low Complexity Chase Decoder for URLLC
Ultra-reliable low-latency communications (URLLC) demand decoding algorithms that simultaneously offer high reliability and low complexity under stringent latency constraints. While iterative decoding schemes for LDPC and Polar codes offer a good compromise between performance and complexity, they fall short in approaching the theoretical performance limits in the typical URLLC short block length regime. Conversely, quasi-ML decoding schemes for algebraic codes, like Chase-II decoding, exhibit …

This https://arxiv.org/abs/2405.10886 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Three-mode tunable coupler for superconducting two-qubit gates
Building a scalable universal high-performance quantum processor is a formidable challenge. In particular, the problem of realizing fast high-perfomance two-qubit gates of high-fidelity remains needful. Here we propose a building block for a scalable quantum processor consisting of two transmons and a tunable three-mode coupler allowing for a ZZ interaction control. We experimentally demonstrate the native CZ gate with the pulse duration of 60 ns achieving the two-qubit gate fidelity above 98%,…

Efficient and Robust Block Designs for Order-of-Addition Experiments
Chang-Yun Lin
https://arxiv.org/abs/2506.07096 https://arxiv.org…

Efficient and Robust Block Designs for Order-of-Addition Experiments
Designs for Order-of-Addition (OofA) experiments have received growing attention due to their impact on responses based on the sequence of component addition. In certain cases, these experiments involve heterogeneous groups of units, which necessitates the use of blocking to manage variation effects. Despite this, the exploration of block OofA designs remains limited in the literature. As experiments become increasingly complex, addressing this gap is essential to ensure that the designs accura…

Memory Access Vectors: Improving Sampling Fidelity for CPU Performance Simulations
Sriyash Caculo, Mahesh Madhav, Jeff Baxter
https://arxiv.org/abs/2506.02344

Memory Access Vectors: Improving Sampling Fidelity for CPU Performance Simulations
Accurate performance projection of large-scale benchmarks is essential for CPU architects to evaluate and optimize future processor designs. SimPoint sampling, which uses Basic Block Vectors (BBVs), is a widely adopted technique to reduce simulation time by selecting representative program phases. However, BBVs often fail to capture the behavior of applications with extensive array-indirect memory accesses, leading to inaccurate projections. In particular, the 523.xalancbmk_r benchmark exhibits…

This https://arxiv.org/abs/2506.03947 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Block Alpha-Circulant Preconditioners for All-at-Once Diffusion-Based Covariance Operators
Covariance matrices are central to data assimilation and inverse methods derived from statistical estimation theory. Previous work has considered the application of an all-at-once diffusion-based representation of a covariance matrix operator in order to exploit inherent parallellism in the underlying problem. In this paper, we provide practical methods to apply block $α$-circulant preconditioners to the all-at-once system for the case where the main diffusion operation matrix cannot be readil…

Eigenvalue bounds for preconditioned symmetric multiple saddle-point matrices
L. Bergamaschi, A. Martinez, J. W. Pearson, A. Potschka
https://arxiv.org/abs/2506.02816

Eigenvalue bounds for preconditioned symmetric multiple saddle-point matrices
We develop eigenvalue bounds for symmetric, block tridiagonal multiple saddle-point linear systems, preconditioned with block diagonal matrices. We extend known results for $3 \times 3$ block systems [Bradley and Greif, IMA J.\ Numer. Anal. 43 (2023)] and for $4 \times 4$ systems [Pearson and Potschka, IMA J. Numer. Anal. 44 (2024)] to an arbitrary number of blocks. Moreover, our results generalize the bounds in [Sogn and Zulehner, IMA J. Numer. Anal. 39 (2018)], developed for an arbitrary numb…

Coordinated Multi-BS SSB Beam Design for Enhanced Initial Access Coverage
Hakime BarghiZanjani, Bikshapathi Gouda, Antti T\"olli
https://arxiv.org/abs/2506.02760

Coordinated Multi-BS SSB Beam Design for Enhanced Initial Access Coverage
Ensuring strong synchronization signal block (SSB) coverage is essential for reliable user equipment (UE) connection during initial access. While techniques such as power boosting and network densification are commonly used, this work explores joint transmission (JT) of SSBs as an alternative to enhance coverage. Although JT is widely applied in data transmission, its use for SSBs has not been explored due to the lack of channel state information, which prevents coherent signal alignment across…

This https://arxiv.org/abs/2408.00651 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A Dirichlet stochastic block model for composition-weighted networks
Network data are observed in various applications where the individual entities of the system interact with or are connected to each other, and often these interactions are defined by their associated strength or importance. Clustering is a common task in network analysis that involves finding groups of nodes displaying similarities in the way they interact with the rest of the network. However, most clustering methods use the strengths of connections between entities in their original form, ig…

This https://arxiv.org/abs/2506.00951 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Physics-Informed Neural Networks for the Relativistic Burgers Equation in the Exterior of a Schwarzschild Black Hole
We introduce a Physics-Informed Neural Networks(PINN) to solve a relativistic Burgers equation in the exterior domain of a Schwarzschild black hole. Our main contribution is a PINN architecture that is able to simulate shock wave formations in such curved spacetime, by training a shock- aware network block and introducing a Godunov-inspired residuals in the loss function. We validate our method with numerical experiments with different kinds of initial conditions. We show its ability to reprodu…

An Array Decomposition Method for Finite Arrays with Electrically Connected Elements for fast Toeplitz Solvers
Lucas {\AA}kerstedt, Harald Hultin, B. L. G. Jonsson
https://arxiv.org/abs/2506.04710

An Array Decomposition Method for Finite Arrays with Electrically Connected Elements for fast Toeplitz Solvers
A large part of the geometry of array antennas is often partially defined by finite translational symmetries. Applying the method of moments (MoM) with the RWG-like element on an appropriately structured mesh to these arrays results in an impedance matrix where the main part exhibits a multilevel block Toeplitz structure. This article introduces a memory-efficient construction method that effectively represents and reuses impedance calculations. The proposed method, applicable to electrically c…