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🇺🇦 Auf radioeins läuft...
Jalen Ngonda:
🎵 Just as Long as Were Together
#NowPlaying #JalenNgonda
https://jalenngonda.bandcamp.com/track/just-as-long-as-were-together
https://open.spotify.com/track/6uAt2PE7YB5gHeCAFeD7G6

Translationese-index: Using Likelihood Ratios for Graded and Generalizable Measurement of Translationese
Yikang Liu, Wanyang Zhang, Yiming Wang, Jialong Tang, Pei Zhang, Baosong Yang, Fei Huang, Rui Wang, Hai Hu
https://arxiv.org/abs/2507.12260

Translationese-index: Using Likelihood Ratios for Graded and Generalizable Measurement of Translationese
In this paper, we propose the first quantitative measure for translationese -- the translationese-index (T-index) for graded and generalizable measurement of translationese, computed from the likelihood ratios of two contrastively fine-tuned language models (LMs). We use a synthesized dataset and a dataset with translations in the wild to evaluate T-index's generalizability in cross-domain settings and its validity against human judgments. Our results show that T-index is both robust and effici…

PAPR of DFT-s-OTFS with Pulse Shaping
Jialiang Zhu, Sanoopkumar P. S., Arman Farhang
https://arxiv.org/abs/2507.12210 https://arxiv.o…

PAPR of DFT-s-OTFS with Pulse Shaping
Orthogonal Time Frequency Space (OTFS) suffers from high peak-to-average power ratio (PAPR) when the number of Doppler bins is large. To address this issue, a discrete Fourier transform spread OTFS (DFT-s-OTFS) scheme is employed by applying DFT spreading across the Doppler dimension. This paper presents a thorough PAPR analysis of DFT-s-OTFS in the uplink scenario using different pulse shaping filters and resource allocation strategies. Specifically, we derive a PAPR upper bound of DFT-s-OTFS …

Elk: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques
Yiqi Liu, Yuqi Xue, Noelle Crawford, Jilong Xue, Jian Huang
https://arxiv.org/abs/2507.11506

Elk: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques
To meet the increasing demand of deep learning (DL) models, AI chips are employing both off-chip memory (e.g., HBM) and high-bandwidth low-latency interconnect for direct inter-core data exchange. However, it is not easy to explore the efficiency of these inter-core connected AI (ICCA) chips, due to a fundamental tussle among compute (per-core execution), communication (inter-core data exchange), and I/O (off-chip data access). In this paper, we develop Elk, a DL compiler framework to maximiz…

Extracting energy from plunging region of a Kerr-Taub-NUT black hole by magnetic reconnection
Zhengwei Cheng, Songbai Chen, Jiliang Jing
https://arxiv.org/abs/2507.11859

Extracting energy from plunging region of a Kerr-Taub-NUT black hole by magnetic reconnection
We have studied the energy extraction from a Kerr-Taub-NUT black hole via magnetic reconnections occurring in the plunging region. Our results show that the gravitomagnetic charge suppresses the energy extraction process through magnetic reconnection and reduces the corresponding extraction efficiency, which is opposite to those of the black hole spin and the magnetization parameter. Finally, we treat the energy extraction process through magnetic reconnection as a mechanism to revisit the prob…

The Darkfield Approach to Measuring Vacuum Birefringence and Light-by-Light Couplings -- A Proof-of-Principle Experiment
Michal Sm\'id, Pooyan Khademi, Carsten B\"ahtz, Erik Brambrink, Jindrich Chalupsky, Tom E. Cowan, Samuele Di Dio Cafiso, Sebastian G\"ode, J\"org Grenzer, Vera Hajkova, Peter Hilz, Willi Hippler, Hauke H\"opner, Alzbeta Horynova, Oliver Humphries, Simon Jelinek, Libor Juha, Felix Karbstein, Alejandro Laso-Garcia, Robert L\"otzsch, Aim\…

The Darkfield Approach to Measuring Vacuum Birefringence and Light-by-Light Couplings -- A Proof-of-Principle Experiment
Vacuum fluctuations give rise to effective nonlinear interactions between electromagnetic fields. These generically modify the characteristics of light traversing a strong-field region. X-ray free-electron lasers constitute a particularly promising probe, due to their brilliance, the possibility of precise control and favourable frequency scaling. However, the nonlinear vacuum response is very small even when probing a tightly focused high-intensity laser field with XFEL radiation and direct me…

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Erika de Casier:
🎵 Miss
#ErikadeCasier
https://jalenjones.bandcamp.com/track/erika-de-casier-miss-jalen-jones-ukg-bootleg
https://open.spotify.com/track/3iewIkYTvvaicW8iysqxuU

From Surface to Semantics: Semantic Structure Parsing for Table-Centric Document Analysis
Xuan Li, Jialiang Dong, Raymond Wong
https://arxiv.org/abs/2508.10311 https://

From Surface to Semantics: Semantic Structure Parsing for Table-Centric Document Analysis
Documents are core carriers of information and knowl-edge, with broad applications in finance, healthcare, and scientific research. Tables, as the main medium for structured data, encapsulate key information and are among the most critical document components. Existing studies largely focus on surface-level tasks such as layout analysis, table detection, and data extraction, lacking deep semantic parsing of tables and their contextual associations. This limits advanced tasks like cross-paragrap…

Community Report from the 2025 SNOLAB Future Projects Workshop
M. D. Diamond, P. Abbamonte, A. Arvanitaki, D. M. Asner, D. Balut, D. Baxter, C. Blanco, D. Boreham, M. Boulay, B. Broerman, T. Brunner, E. Caden, A. Chavarria, M. Chen, J. P. Davis, A. Drlica-Wagner, J. Estrada, N. Fatemighomi, J. Foster, D. Freedman, C. Gao, J. Hall, S. Hall, W. Halperin, M. Hirschel, N. Hoch, Z. Hong, A. Ianni, C. Jillings, D. Johnson, Y. Kahn, C. B. Krauss, T. Laframboise, M. Lai, M. R. Lapointe, B. Lil…

Community Report from the 2025 SNOLAB Future Projects Workshop
SNOLAB hosts a biannual Future Projects Workshop (FPW) with the goal of encouraging future project stakeholders to present ideas, concepts, and needs for experiments or programs that could one day be hosted at SNOLAB. The 2025 FPW was held in the larger context of a 15-year planning exercise requested by the Canada Foundation for Innovation. This report collects input from the community, including both contributions to the workshop and contributions that could not be scheduled in the workshop b…

Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in Conversation
Jialong Mai, Xiaofen Xing, Yawei Li, Zhipeng Li, Jingyuan Xing, Xiangmin Xu
https://arxiv.org/abs/2507.09076

Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in Conversation
Recent research has focused on applying speech large language model (SLLM) to improve speech emotion recognition (SER). However, the inherently high frame rate in speech modality severely limits the signal processing and understanding capabilities of SLLM. For example, a SLLM with a 4K context window can only process 80 seconds of audio at 50Hz feature sampling rate before reaching its capacity limit. Input token compression methods used in SLLM overlook the continuity and inertia of emotions a…