Tootfinder

Steelers OC Arthur Smith on offseason changes: 'I've had to adapt' https://www.nfl.com/news/steelers-oc-arthur-smith-on-offseason-changes-i-ve-had-to-adapt

Predictive Control Strategies for Sustaining Innovation Adoption on Multilayer Social Networks
Martina Alutto, Qiulin Xu, Fabrizio Dabbene, Hideaki Ishii, Chiara Ravazzi
https://arxiv.org/abs/2509.01457

Predictive Control Strategies for Sustaining Innovation Adoption on Multilayer Social Networks
This paper studies an optimal control problem for an adoption-opinion model that couples innovation adoption with opinion formation on a multilayer network. Adoption spreads through social contagion and perceived benefits, while opinions evolve via social interactions and feedback from adoption levels. Individuals may abandon adoption due to dissatisfaction or external constraints, potentially hindering diffusion. We analyze system equilibria and their stability, identifying conditions under wh…

Adaptive Iterative Soft-Thresholding Algorithm with the Median Absolute Deviation
Yining Feng, Ivan Selesnick
https://arxiv.org/abs/2507.02084 https://

Adaptive Iterative Soft-Thresholding Algorithm with the Median Absolute Deviation
The adaptive Iterative Soft-Thresholding Algorithm (ISTA) has been a popular algorithm for finding a desirable solution to the LASSO problem without explicitly tuning the regularization parameter $λ$. Despite that the adaptive ISTA is a successful practical algorithm, few theoretical results exist. In this paper, we present the theoretical analysis on the adaptive ISTA with the thresholding strategy of estimating noise level by median absolute deviation. We show properties of the fixed points …

Adapting Language Models to Indonesian Local Languages: An Empirical Study of Language Transferability on Zero-Shot Settings
Rifki Afina Putri
https://arxiv.org/abs/2507.01645

Adapting Language Models to Indonesian Local Languages: An Empirical Study of Language Transferability on Zero-Shot Settings
In this paper, we investigate the transferability of pre-trained language models to low-resource Indonesian local languages through the task of sentiment analysis. We evaluate both zero-shot performance and adapter-based transfer on ten local languages using models of different types: a monolingual Indonesian BERT, multilingual models such as mBERT and XLM-R, and a modular adapter-based approach called MAD-X. To better understand model behavior, we group the target languages into three categori…

LobRA: Multi-tenant Fine-tuning over Heterogeneous Data
Sheng Lin, Fangcheng Fu, Haoyang Li, Hao Ge, Xuanyu Wang, Jiawen Niu, Yaofeng Tu, Bin Cui
https://arxiv.org/abs/2509.01193

LobRA: Multi-tenant Fine-tuning over Heterogeneous Data
With the breakthrough of Transformer-based pre-trained models, the demand for fine-tuning (FT) to adapt the base pre-trained models to downstream applications continues to grow, so it is essential for service providers to reduce the cost of processing FT requests. Low-rank adaption (LoRA) is a widely used FT technique that only trains small-scale adapters and keeps the base model unaltered, conveying the possibility of processing multiple FT tasks by jointly training different LoRA adapters wit…

SPENCER: Self-Adaptive Model Distillation for Efficient Code Retrieval
Wenchao Gu, Zongyi Lyu, Yanlin Wang, Hongyu Zhang, Cuiyun Gao, Michael R. Lyu
https://arxiv.org/abs/2508.00546

SPENCER: Self-Adaptive Model Distillation for Efficient Code Retrieval
Code retrieval aims to provide users with desired code snippets based on users' natural language queries. With the development of deep learning technologies, adopting pre-trained models for this task has become mainstream. Considering the retrieval efficiency, most of the previous approaches adopt a dual-encoder for this task, which encodes the description and code snippet into representation vectors, respectively. However, the model structure of the dual-encoder tends to limit the model's perf…

Speech Adaptation Relies More on Time Than Practice
https://scienmag.com/speech-adaptation-relies-more-on-time-than-practice/

Ein paar Tricks, die mittlerweile zwischen Android und Linux echt leicht funktionieren (wenn man testen oder präsentieren will), darunter auch Bedienung mit Maus und Tastatur. Und Internet ohne Wifi (reverse tethering):
1. Android über Ethernet ins Internet verbinden mit USB-C-Ethernet-Adapter, z.B.
https://www.ebay.de/itm/3656612918…

Why Do Decision Makers (Not) Use AI? A Cross-Domain Analysis of Factors Impacting AI Adoption
Rebecca Yu, Valerie Chen, Ameet Talwalkar, Hoda Heidari
https://arxiv.org/abs/2508.00723

Why Do Decision Makers (Not) Use AI? A Cross-Domain Analysis of Factors Impacting AI Adoption
Growing excitement around deploying AI across various domains calls for a careful assessment of how human decision-makers interact with AI-powered systems. In particular, it is essential to understand when decision-makers voluntarily choose to consult AI tools, which we term decision-maker adoption. We interviewed experts across four domains -- medicine, law, journalism, and the public sector -- to explore current AI use cases and perceptions of adoption. From these interviews, we identify key …

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking
Shyma Alhuwaider, Motasem Alfarra, Juan C. Perez, Merey Ramazanova, Bernard Ghanem
https://arxiv.org/abs/2509.02182

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking
We introduce a novel tracklet-based dataset for benchmarking test-time adaptation (TTA) methods. The aim of this dataset is to mimic the intricate challenges encountered in real-world environments such as images captured by hand-held cameras, self-driving cars, etc. The current benchmarks for TTA focus on how models face distribution shifts, when deployed, and on violations to the customary independent-and-identically-distributed (i.i.d.) assumption in machine learning. Yet, these benchmarks fa…