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版本更新 · v4arXiv:2511.04556

Flow Reconstruction from Sparse Measurements in Urban Drainage Networks: A Simulation-Based Evaluation of Data-Driven Sparse Sensing

Zihang Ding, Amit Kumar, Imran Md. Azizul Islam, Mila Avellar Montezuma, Ruihang Zhang, Kun Zhang

原文摘要

Urbanization and increasingly frequent intense storms are placing stress on urban drainage networks. While dense monitoring is desirable, practical constraints in time, budget, and technology hinder its full implementation. How to monitor and predict flow conditions across the entire network under constrained resources is a major challenge. To address this, we evaluated data-driven sparse sensing (DSS), which selects sensor locations and reconstructs network-wide flow from the same reduced basis, in a 76-conduit urban drainage network. Simulations of a calibrated SWMM model were used to construct an event-weighted spatial basis using singular value decomposition (SVD), select rank-specific layouts using pivoted QR, and define the reconstruction decoder. Applied to 225 held-out simulations combining 25 plausible calibrated parameter sets with nine rainfall events, monitoring 5 of the 76 conduits (6.6%) achieved a median system-level Nash-Sutcliffe efficiency (NSE) of 0.992 (10th percentile 0.973) for observed storms, and a median NSE of 0.967 at the active unmonitored conduits. Pivoted QR performed comparably to genetic-algorithm layouts without requiring iterative layout searches, whereas largest-upstream-area placement failed with the DSS decoder and trailed QR even with a weighted regression decoder. The DSS flow reconstruction performance was insensitive to prescribed multiplicative Gaussian measurement noise, additive noise up to about 2 L/s and a 10 L/s detection floor, but showed location-dependent responses to loss of a monitored conduit, which a refitted regression decoder largely removed. This study supports the use of DSS, driven by physics-based simulations, for deciding how many flow sensors to install and where.

cs.AI · cs.CE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.34436

Hitting sets From Derivations: Waring Rank and Noncommutative ABPs

Shubham Bhardwaj, Foram Lakhani, Partha Mukhopadhyay, Ramprasad Saptharishi, Pratik Shastri, Amit Kumar Sinhababu

原文摘要

We give an explicit hitting set of size $\operatorname{poly}(n,d,r)$ for the class of $n$-variate degree-$d$ polynomials whose partial derivative space is bounded by $r$, over any field $\mathbb{F}$ of characteristic zero (or large enough). In particular, this yields a polynomial sized hitting set for the class of depth-$3$ powering circuits. The proof extends to the setting of noncommutative ABPs and provides a polynomial sized hitting set (of matrices) for the class of noncommututive polynomials that can be computed by polynomial sized noncommutative ABPs (this was also one of the results reported by Open AI~\cite{OpenAI26} in their release on 6 Oct 2026). The main technical insight is the construction of a ``formal derivation'' and properties of the associated Wronskian with respect to this derivation, which was previously studied by Moura \cite{Moura_2004} in a very different context. The proofs in this paper are elementary and completely self-contained. AI disclosure: A subset of the stewards obtained a weaker statement (hitting sets for polynomials with bounded partial derivative space) during conversations \cite{astra_proof} with OpenAI GPT-6 Astra. Extensions of this idea to the broader class of noncommutative ABPs were obtained by the other stewards (via further conversations with Astra), prior to OpenAI's dump on 6 Oct 2026. The proofs presented in this writeup is a rewriting (in the authors' words) of the proofs for both settings obtained by the models in a form that we believe is understandable to researchers. Although the proof is technically identical to OpenAI's release, we believe this exposition is self-contained, simpler, and more valuable to the community. The absurdity of the situation is not lost on us.

cs.CC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09506

On the Effectiveness and Limitations of Open-Weight LLMs for Low-Resource Offensive Language Detection in Danish

Elnur Adl Zarabi, Amit Kumar, Adi Singh, Sierra Nelson, Megan Stubbs-Richardson, Taoran Ji

原文摘要

We study the effectiveness and robustness of open-weight large language models (LLMs) for Danish offensive language detection, an underexplored low-resource task. We evaluate four model families (Llama, Mistral, Gemma, and Qwen) across eleven prompting strategies spanning five design categories, cross-prompt generalization, Danish orthographic variation, and robustness under balanced and naturally imbalanced class distributions. We compare against three BERT-family baselines with distinct pre-training strategies, evaluated via both masked language model prompting and supervised fine-tuning. Even without task-specific fine-tuning, LLMs can surpass all three fine-tuned BERT baselines, and lightweight finetuning yields substantial further gains. No single model or prompt category dominates, underscoring that prompt design interacts with model pre-training in ways that cannot be predicted a priori. Semantically grounded prompts transfer more robustly, while format-constrained patterns consistently fail to generalize. ASCII transliteration of Danish-specific characters (æ, ø, å) generally degrades performance, indicating these characters encode meaningful signal. At the same time, the gap between a balanced test set (best F1 of 0.84) and the corpus's natural class skew (0.62 trained on skewed data, 0.70 trained on balanced data) -- relative drops of 26.2\% and 16.7\% -- shows that class imbalance remains a critical open challenge. Our code and configurations are available at https://github.com/PASHAADC/Danish-LLM-HateSpeech.

cs.CL

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arXiv ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:66e339191b49e8b07717b7376682f903CVF · CVPR 2026

ImageRAGTurbo: Towards One-step Text-to-Image Generation with Retrieval-Augmented Diffusion Models

Qiu, Peijie, Ramshankar, Hariharan, Ramisa, Arnau, C, Amit Kumar K, Vidal, René, Salaka, Vamsi, Bhagat, Rahul

原文摘要

Diffusion models have emerged as the leading approach for text-to-image generation. However, their iterative sampling process, which gradually morphs random noise into coherent images, introduces significant latency that limits their applicability. While recent few-step diffusion models reduce the number of sampling steps to as few as one to four steps, they often compromise image quality and prompt alignment, especially in one-step generation. Additionally, these models require computationally expensive training procedures. To address these limitations, we propose ImageRAGTurbo, a novel approach to efficiently finetune few-step diffusion models via retrieval augmentation. Given a text prompt, we retrieve relevant text-image pairs from a database and use them to condition the generation process. We argue that such retrieved examples provide rich contextual information to the UNet denoiser that helps reduce the number of denoising steps without compromising image quality. Indeed, our initial investigations show that using the retrieved content to edit the denoiser's latent space (\mathcal H -space) without additional finetuning already improves prompt fidelity. To further improve the quality of the generated images, we augment the UNet denoiser with a trainable adapter in the \mathcal H -space, which efficiently blends the retrieved content with the target prompt using a cross-attention mechanism. Experimental results on fast text-to-image generation demonstrate that our approach produces high-fidelity images without compromising latency compared to existing methods.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗