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2512.12778
FPGA-based SmartNICs and IoT devices integrated with soft-processors for executing network functions have been introduced to overcome hardware-reconfigurability limitations in DPUs (Data Processing Units) and MCUs (Microcontroller Units), respectively. However, existing FPGA-based SmartNICs and IoT
2512.12805
Transformers exhibit a notable property of size generalization, demonstrating an ability to extrapolate from smaller token sets to significantly longer ones. This behavior has been documented across diverse applications, including point clouds, graphs, and natural language. Despite its empirical suc
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2512.19114
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2512.19390
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4-Doodle: Text to 3D Sketches that Move!
Abstract and deep analysis are available in the full post.
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A brief review of the Deep BSDE method for solving high-dimensional partial differential equations
High-dimensional partial differential equations (PDEs) pose significant challenges for numerical computation due to the curse of dimensionality, which limits the applicability of traditional mesh-based methods. Since 2017, the Deep BSDE method has introduced deep learning techniques that enable the
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A Census of Massive Stars Across the Hertzsprung-Russell Diagram of Nearby Galaxies: What We Know and What We Dont
When we look at a nearby galaxy, we see a mixture of foreground stars and bona fide extragalactic stars. I will describe what we need to do to get meaningful statistics on the massive star populations across the H-R diagram. Such a census provides the means of a very powerful test of massive star
Astrophysics
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A chilean seismic regionalization through a Kohonen neural network
A study of seismic regionalization for central Chile based on a neural network is presented. A scenario with six seismic regions is obtained, independently of the size of the neighborhood or the reach of the correlation between the cells of the grid. The high correlation between the spatial distribu
Network
Physics
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A Class of Mixed Integrable Models
The algebraic structure of the integrable mixed mKdV/sinh-Gordon model is discussed and textit{}extended to the AKNS/Lund-Regge model and to its corresponding supersymmetric versions. The integrability of the models is guaranteed from the zero curvature representation and some soliton solutions are
Model
HEP-TH
Nonlinear Sciences
A Comparative Study of Classical and Post-Quantum Cryptographic Algorithms in the Era of Quantum Computing
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A computer vision-based approach to enhance seismic catalogues
In recent years, AI and deep learning earthquake detectors, combined with an increasing number of dense seismic networks deployed worldwide, have further contributed to the creation of massive seismic catalogs, significantly lowering their magnitude of completeness. However, these automated catalogs
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A Conversation with Jayaram Sethuraman
Jayaram Sethuraman was born in the town of Hubli in Bombay Province (now Karnataka State) on October 3, 1937. His early years were spent in Hubli and in 1950 his family moved to Madras (now renamed Chennai). He graduated from Madras University in 1957 with a B.Sc. (Hons) degree in statistics and he
Statistics
A Detailed Study on LLM Biases Concerning Corporate Social Responsibility and Green Supply Chains
Organizations increasingly use Large Language Models (LLMs) to improve supply chain processes and reduce environmental impacts. However, LLMs have been shown to reproduce biases regarding the prioritization of sustainable business strategies. Thus, it is important to identify underlying training dat
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A Discrete Macro-Element Method (DMEM) for the nonlinear structural assessment of masonry arches
The structural response of masonry arches is strongly dominated by the arch geometry, the stone block dimensions and the interaction with backfill material or surrounding walls. Due to their intrinsic discontinuous nature, the nonlinear structural response of these key historical structures can be e
Computational Engineering
Computer Science
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A dual-stage constitutive modeling framework based on finite strain data-driven identification and physics-augmented neural networks
Abstract and deep analysis are available in the full post.
Network
Model
Framework
Data
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A Financial Brain Scan of the LLM
Emerging techniques in computer science make it possible to 'brain scan' large language models (LLMs), identify the plain-English concepts that guide their reasoning, and steer them while holding other factors constant. We show that this approach can map LLM-generated economic forecasts to concepts
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A Framework for Fair Evaluation of Variance-Aware Bandit Algorithms
Multi-armed bandit (MAB) problems serve as a fundamental building block for more complex reinforcement learning algorithms. However, evaluating and comparing MAB algorithms remains challenging due to the lack of standardized conditions and replicability. This is particularly problematic for variance
Framework
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A Lightweight Combination of Semantics for Non-deterministic Functions
The use of non-deterministic functions is a distinctive feature of modern functional logic languages. The semantics commonly adopted is call-time choice, a notion that at the operational level is related to the sharing mechanism of lazy evaluation in functional languages. However, there are situatio
Programming Languages
Computer Science
A LoD of Gaussians: Unified Training and Rendering for Ultra-Large Scale Reconstruction with External Memory
Abstract and deep analysis are available in the full post.
< Category Statistics (Total: 5306) >
Astrophysics
29
Computer Science
249
Condensed Matter
41
Economics
4
Electrical Engineering and Systems Science
104
General Relativity
4
General Research
4352
HEP-EX
4
HEP-PH
3
HEP-TH
8
MATH-PH
10
Mathematics
197
Nonlinear Sciences
5
Physics
147
Quantitative Biology
79
Quantitative Finance
3
Quantum Physics
18
Statistics
96