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An Analysis of the Riemann Problem for a $2 \times 2$ System of Keyfitz-Kranzer Type Balance Laws With a Time-Dependent Source Term
Josh Culver, Aubrey Ayres, Evan Halloran, Ryan Lin, Emily Peng, Charis Tsikkou
https://arxiv.org/abs/2508.10347

An Analysis of the Riemann Problem for a $2 \times 2$ System of Keyfitz-Kranzer Type Balance Laws With a Time-Dependent Source Term
We consider a system consisting of one conservation law and one balance law with a time-dependent source term, and provide a comprehensive analysis of Riemann solutions, including the non-classical overcompressive delta shocks. The minimal yet representative structure of the system captures essential features of transport under density constraints and, despite its simplicity, serves as a versatile prototype for crowd-limited transport processes across diverse contexts, including biological aggr…

Beyond Stellar Rank: Control Parameters for Scalable Optical Non-Gaussian State Generation
Fumiya Hanamura, Kan Takase, Hironari Nagayoshi, Ryuhoh Ide, Warit Asavanant, Kosuke Fukui, Petr Marek, Radim Filip, Akira Furusawa
https://arxiv.org/abs/2509.06255

Beyond Stellar Rank: Control Parameters for Scalable Optical Non-Gaussian State Generation
Advanced quantum technologies rely on non-Gaussian states of light, essential for universal quantum computation, fault-tolerant error correction, and quantum sensing. Their practical realization, however, faces hurdles: simulating large multi-mode generators is computationally demanding, and benchmarks such as the \emph{stellar rank} do not capture how effectively photon detections yield useful non-Gaussianity. We address these challenges by introducing the \emph{non-Gaussian control parameters…

> As currently constructed, AI is an oligarchy-enriching, worker-immiserating, energy-depleting, brain-rotting economic bubble in waiting. Democrats can get on the public’s side here.
I haven't even read the article but (considering how strongly I agree with the premises here) I'm mostly just sad about how clearly this highlights the financial capture of the DNC
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Democrats Must Oppose the AI Industry - The American Prospect

Democrats Must Oppose the AI Industry
As currently constructed, AI is an oligarchy-enriching, worker-immiserating, energy-depleting, brain-rotting economic bubble in waiting. Democrats can get on the public’s side here.

TSPC: A Two-Stage Phoneme-Centric Architecture for code-switching Vietnamese-English Speech Recognition
Minh N. H. Nguyen, Anh Nguyen Tran, Dung Truong Dinh, Nam Van Vo
https://arxiv.org/abs/2509.05983

TSPC: A Two-Stage Phoneme-Centric Architecture for code-switching Vietnamese-English Speech Recognition
Code-switching (CS) presents a significant challenge for general Auto-Speech Recognition (ASR) systems. Existing methods often fail to capture the subtle phonological shifts inherent in CS scenarios. The challenge is particularly difficult for language pairs like Vietnamese and English, where both distinct phonological features and the ambiguity arising from similar sound recognition are present. In this paper, we propose a novel architecture for Vietnamese-English CS ASR, a Two-Stage Phoneme-C…

Constraint Hypergraphs as a Unifying Framework for Digital Twins
John Morris, Douglas L. Van Bossuyt, Edward Louis, Gregory Mocko, John Wagner
https://arxiv.org/abs/2507.05494

Constraint Hypergraphs as a Unifying Framework for Digital Twins
Digital twins, used to represent physical systems, have been lauded as tools for understanding reality. Complex system behavior is typically captured in domain-specific models crafted by subject experts. Contemporary methods for employing models in a digital twin require prescriptive interfaces, resulting in twins that are difficult to connect, redeploy, and modify. The limited interoperability of these twins has prompted calls for a universal framework enabling observability across model aggre…

PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation
Jiajun He, Naoki Sawada, Koichi Miyazaki, Tomoki Toda
https://arxiv.org/abs/2509.04357 https…

PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation
Automatic speech recognition (ASR) systems struggle with domain-specific named entities, especially homophones. Contextual ASR improves recognition but often fails to capture fine-grained phoneme variations due to limited entity diversity. Moreover, prior methods treat entities as independent tokens, leading to incomplete multi-token biasing. To address these issues, we propose Phoneme-Augmented Robust Contextual ASR via COntrastive entity disambiguation (PARCO), which integrates phoneme-aware …

On the isomorphism problem for generalized Baumslag-Solitar groups: angles
Dario Ascari, Montserrat Casals-Ruiz, Ilya Kazachkov
https://arxiv.org/abs/2508.03231 https://

On the isomorphism problem for generalized Baumslag-Solitar groups: angles
We introduce a new isomorphism invariant for generalized Baumslag-Solitar (GBS) groups, which we call the limit angle. Unlike previously known invariants, which are primarily algebraic, the limit angle admits a dynamical interpretation, arising exclusively in the case of two interacting edges. This invariant captures subtle geometric behavior that does not manifest in configurations with more interacting edges. As an application, we use the limit angle to obtain a classification of GBS groups w…

Distance-informed Neural Processes
Aishwarya Venkataramanan, Joachim Denzler
https://arxiv.org/abs/2508.18903 https://arxiv.org/pdf/2508.18903

Distance-informed Neural Processes
We propose the Distance-informed Neural Process (DNP), a novel variant of Neural Processes that improves uncertainty estimation by combining global and distance-aware local latent structures. Standard Neural Processes (NPs) often rely on a global latent variable and struggle with uncertainty calibration and capturing local data dependencies. DNP addresses these limitations by introducing a global latent variable to model task-level variations and a local latent variable to capture input similar…

MLSD: A Novel Few-Shot Learning Approach to Enhance Cross-Target and Cross-Domain Stance Detection
Parush Gera, Tempestt Neal
https://arxiv.org/abs/2509.03725 https://

MLSD: A Novel Few-Shot Learning Approach to Enhance Cross-Target and Cross-Domain Stance Detection
We present the novel approach for stance detection across domains and targets, Metric Learning-Based Few-Shot Learning for Cross-Target and Cross-Domain Stance Detection (MLSD). MLSD utilizes metric learning with triplet loss to capture semantic similarities and differences between stance targets, enhancing domain adaptation. By constructing a discriminative embedding space, MLSD allows a cross-target or cross-domain stance detection model to acquire useful examples from new target domains. We …