February 2026

arXiv:2602.12491v1 Announce Type: new Abstract: In this article, we enforce space group symmetries in Fourier series to rigorously prove the existence of smooth, periodic solutions in partial differential equations (PDEs) with hexagonal and triangular symmetries. In particular, we provide the necessary analytical and numerical tools to construct Fourier series of functions on the hexagonal lattice. This allows one to build approximate solutions that are periodic. Moreover, to generate the periodic tiling, we can use one symmetric hexagon for […]

Ver mais

arXiv:2602.12489v1 Announce Type: new Abstract: We propose a novel method for establishing correspondence between two sequences of 2D images. One particular application of this technique is slice-level content navigation, where the goal is to localize specific 2D slices within a 3D volume or determine the anatomical coverage of a 3D scan based on its 2D slices. This serves as an important preprocessing step for various diagnostic tasks, as well as for automatic registration and segmentation pipelines. Our approach […]

Ver mais

arXiv:2602.12487v1 Announce Type: new Abstract: We present a quadrotor dynamics Gaussian Process (GP) with gradient information that achieves real-time inference via state-space partitioning and approximation, and that includes aerodynamic effects using data from mid-fidelity potential flow simulations. While traditional GP-based approaches provide reliable Bayesian predictions with uncertainty quantification, they are computationally expensive and thus unsuitable for real-time simulations. To address this challenge, we integrate gradient information to improve accuracy and introduce a novel partitioning and approximation strategy to […]

Ver mais

arXiv:2602.12486v1 Announce Type: new Abstract: Humans appear to represent objects for intuitive physics with coarse, volumetric bodies” that smooth concavities – trading fine visual details for efficient physical predictions – yet their internal structure is largely unknown. Segmentation models, in contrast, optimize pixel-accurate masks that may misalign with such bodies. We ask whether and when these models nonetheless acquire human-like bodies. Using a time-to-collision (TTC) behavioral paradigm, we introduce a comparison pipeline and alignment metric, then vary model […]

Ver mais

arXiv:2602.12485v1 Announce Type: new Abstract: The success of businesses depends on their ability to convert consumers into loyal customers. A customer’s value proposition is a primary determinant in this process, requiring a balance between affordability and long-term brand equity. Broad marketing campaigns can erode perceived brand value and reduce return on investment, while existing economic algorithms often misidentify highly engaged customers as ideal targets, leading to inefficient engagement and conversion outcomes. This work introduces a two-stage multi-model architecture […]

Ver mais

arXiv:2602.12484v1 Announce Type: new Abstract: Grapes are among the most economically and culturally significant fruits on a global scale, and table grapes and wine are produced in significant quantities in Europe and Asia. The production and quality of grapes are significantly impacted by grape diseases such as Bacterial Rot, Downy Mildew, and Powdery Mildew. Consequently, the sustainable management of a vineyard necessitates the early and precise identification of these diseases. Current automated methods, particularly those that are based […]

Ver mais

arXiv:2602.12483v1 Announce Type: new Abstract: Randomized Kaczmarz (RK) is a simple and fast solver for consistent overdetermined systems, but it is known to be fragile under noise. We study overdetermined $mtimes n$ linear systems with a sparse set of corrupted equations, $ {bf A}{bf x}^star = {bf b}, $where only $tilde{bf b} = {bf b} + boldsymbol{varepsilon}$ is observed with $|boldsymbol{varepsilon}|_0 le beta m$. The recently introduced QuantileRK (QRK) algorithm addresses this issue by testing residuals against a […]

Ver mais

arXiv:2602.12482v1 Announce Type: new Abstract: We give a geometric construction of neural networks that separate disjoint compact subsets of $Bbb R^n$, and use it to obtain a constructive universal approximation theorem. Specifically, we show that networks with two hidden layers and either a sigmoidal activation (i.e., strictly monotone bounded continuous) or the ReLU activation can approximate any real-valued continuous function on an arbitrary compact set $KsubsetBbb R^n$ to any prescribed accuracy in the uniform norm. For finite $K$, […]

Ver mais

arXiv:2602.12481v1 Announce Type: new Abstract: Online advertising platforms must decide how to allocate multiple ads across limited screen real estate, where each ad’s effectiveness depends not only on its own placement but also on nearby ads competing for user attention. Such spatial externalities – arising from proximity, clutter, or crowding – can significantly alter welfare and revenue outcomes, yet existing auction and allocation models typically treat ad slots as independent or ordered along a single dimension. We introduce […]

Ver mais

arXiv:2602.12480v1 Announce Type: new Abstract: The proliferation of Transformer models is often constrained by the significant computational and memory bandwidth demands of deployment. To address this, we present MXFormer, a novel, hybrid, weight-stationary Compute-in-Memory (CIM) accelerator that provides high throughput and efficiency for fixed-model inference on large short-sequence Transformers. Our architecture’s foundation is the use of ultra-dense Charge-Trap Transistors (CTTs) in Microscaling MXFP4 CIM arrays, uniquely enabling the on-chip storage of up to hundreds of millions of parameters […]

Ver mais