Tootfinder

Cyberbullying Detection via Aggression-Enhanced Prompting
Aisha Saeid, Anu Sabu, Girish A. Koushik, Ferrante Neri, Diptesh Kanojia
https://arxiv.org/abs/2508.06360 https://

Cyberbullying Detection via Aggression-Enhanced Prompting
Detecting cyberbullying on social media remains a critical challenge due to its subtle and varied expressions. This study investigates whether integrating aggression detection as an auxiliary task within a unified training framework can enhance the generalisation and performance of large language models (LLMs) in cyberbullying detection. Experiments are conducted on five aggression datasets and one cyberbullying dataset using instruction-tuned LLMs. We evaluated multiple strategies: zero-shot, …

Adaptive Event-Triggered MPC for Linear Parameter-Varying Systems with State Delays, Actuator Saturation and Disturbances
Aiping Zhong, Wanlin Lu, Langwen Zhang, Ziyang Bao
https://arxiv.org/abs/2509.07384

Adaptive Event-Triggered MPC for Linear Parameter-Varying Systems with State Delays, Actuator Saturation and Disturbances
This paper proposes a unified adaptive event-triggered model predictive control (ETMPC) scheme for linear parameter-varying (LPV) systems subject to state delays, actuator saturation, and external disturbances. In existing studies, only a limited number of ETMPC methods have attempted to address either state delays or actuator saturation, and even these few methods typically lack co-design optimization between adaptive event-triggering mechanisms and the control law. To overcome these limitatio…

🇺🇦 #NowPlaying on KEXP's #StreetSounds
Georgia Anne Muldrow:
🎵 Tungsten Babalawo
#GeorgiaAnneMuldrow
https://georgiaannemuldrow.bandcamp.com/track/tungsten-babalawo
https://open.spotify.com/track/2UmB82euAyyaDVbVSZFwl0
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USD: A User-Intent-Driven Sampling and Dual-Debiasing Framework for Large-Scale Homepage Recommendations
Jiaqi Zheng, Cheng Guo, Yi Cao, Chaoqun Hou, Tong Liu, Bo Zheng
https://arxiv.org/abs/2507.06503

USD: A User-Intent-Driven Sampling and Dual-Debiasing Framework for Large-Scale Homepage Recommendations
Large-scale homepage recommendations face critical challenges from pseudo-negative samples caused by exposure bias, where non-clicks may indicate inattention rather than disinterest. Existing work lacks thorough analysis of invalid exposures and typically addresses isolated aspects (e.g., sampling strategies), overlooking the critical impact of pseudo-positive samples - such as homepage clicks merely to visit marketing portals. We propose a unified framework for large-scale homepage recommendat…

Gaussian Process-Based Active Exploration Strategies in Vision and Touch
Ho Jin Choi, Nadia Figueroa
https://arxiv.org/abs/2507.05522 https://

Gaussian Process-Based Active Exploration Strategies in Vision and Touch
Robots struggle to understand object properties like shape, material, and semantics due to limited prior knowledge, hindering manipulation in unstructured environments. In contrast, humans learn these properties through interactive multi-sensor exploration. This work proposes fusing visual and tactile observations into a unified Gaussian Process Distance Field (GPDF) representation for active perception of object properties. While primarily focusing on geometry, this approach also demonstrates …

POLARON: Precision-aware On-device Learning and Adaptive Runtime-cONfigurable AI acceleration
Mukul Lokhande, Santosh Kumar Vishvakarma
https://arxiv.org/abs/2506.08785

POLARON: Precision-aware On-device Learning and Adaptive Runtime-cONfigurable AI acceleration
The increasing complexity of AI models requires flexible hardware capable of supporting diverse precision formats, particularly for energy-constrained edge platforms. This work presents PARV-CE, a SIMD-enabled, multi-precision MAC engine that performs efficient multiply-accumulate operations using a unified data-path for 4/8/16-bit fixed-point, floating point, and posit formats. The architecture incorporates a layer adaptive precision strategy to align computational accuracy with workload sensi…

Quantum Computing for Large-scale Network Optimization: Opportunities and Challenges
Sebastian Macaluso, Giovanni Geraci, El\'ias F. Combarro, Sergi Abadal, Ioannis Arapakis, Sofia Vallecorsa, Eduard Alarc\'on
https://arxiv.org/abs/2509.07773

Quantum Computing for Large-scale Network Optimization: Opportunities and Challenges
The complexity of large-scale 6G-and-beyond networks demands innovative approaches for multi-objective optimization over vast search spaces, a task often intractable. Quantum computing (QC) emerges as a promising technology for efficient large-scale optimization. We present our vision of leveraging QC to tackle key classes of problems in future mobile networks. By analyzing and identifying common features, particularly their graph-centric representation, we propose a unified strategy involving …

AnalysisGNN: Unified Music Analysis with Graph Neural Networks
Emmanouil Karystinaios, Johannes Hentschel, Markus Neuwirth, Gerhard Widmer
https://arxiv.org/abs/2509.06654 https…

AnalysisGNN: Unified Music Analysis with Graph Neural Networks
Recent years have seen a boom in computational approaches to music analysis, yet each one is typically tailored to a specific analytical domain. In this work, we introduce AnalysisGNN, a novel graph neural network framework that leverages a data-shuffling strategy with a custom weighted multi-task loss and logit fusion between task-specific classifiers to integrate heterogeneously annotated symbolic datasets for comprehensive score analysis. We further integrate a Non-Chord-Tone prediction modu…

Trump celebrated the start of his long-awaited tariffs Thursday,
— though economists say the cost of the import taxes will probably get passed on to U.S. consumers and businesses.

Commerce Secretary Howard Lutnick said the federal government could collect $50 billion a month in tariff revenue,
which Trump administration officials hope will be a boon to the U.S.'s dismal financial health.

U.S. stocks finished mixed Thursday, as investors appeared unbothered by the …

Trump’s tariffs take effect, potentially upending global trade and prices
New import taxes kick in on goods from dozens of U.S. trading partners.

AURAD: Anatomy-Pathology Unified Radiology Synthesis with Progressive Representations
Shuhan Ding, Jingjing Fu, Yu Gu, Naiteek Sangani, Mu Wei, Paul Vozila, Nan Liu, Jiang Bian, Hoifung Poon
https://arxiv.org/abs/2509.04819

AURAD: Anatomy-Pathology Unified Radiology Synthesis with Progressive Representations
Medical image synthesis has become an essential strategy for augmenting datasets and improving model generalization in data-scarce clinical settings. However, fine-grained and controllable synthesis remains difficult due to limited high-quality annotations and domain shifts across datasets. Existing methods, often designed for natural images or well-defined tumors, struggle to generalize to chest radiographs, where disease patterns are morphologically diverse and tightly intertwined with anatom…