Tootfinder

#Phototagging in #Portbou at the Walter Benjamin Memorial above the cliffs next to the cemetery. The phototag shows a shot of one of the beaches in Banyuls-sur-mer (France), from where #refugees reached the border tow…

Generalizing Verifiable Instruction Following
Valentina Pyatkin, Saumya Malik, Victoria Graf, Hamish Ivison, Shengyi Huang, Pradeep Dasigi, Nathan Lambert, Hannaneh Hajishirzi
https://arxiv.org/abs/2507.02833

Generalizing Verifiable Instruction Following
A crucial factor for successful human and AI interaction is the ability of language models or chatbots to follow human instructions precisely. A common feature of instructions are output constraints like ``only answer with yes or no" or ``mention the word `abrakadabra' at least 3 times" that the user adds to craft a more useful answer. Even today's strongest models struggle with fulfilling such constraints. We find that most models strongly overfit on a small set of verifiable constraints from …

Embedded DevOps: A Survey on the Application of DevOps Practices in Embedded Software and Firmware Development
Parthiv Katapara, Anand Sharma
https://arxiv.org/abs/2507.00421

Embedded DevOps: A Survey on the Application of DevOps Practices in Embedded Software and Firmware Development
The adoption of DevOps practices in embedded systems and firmware development is emerging as a response to the growing complexity of modern hardware--software co-designed products. Unlike cloud-native applications, embedded systems introduce challenges such as hardware dependency, real-time constraints, and safety-critical requirements. This literature review synthesizes findings from 20 academic and industrial sources to examine how DevOps principles--particularly continuous integration, conti…

Exploring Societal Concerns and Perceptions of AI: A Thematic Analysis through the Lens of Problem-Seeking
Naomi Omeonga wa Kayembe
https://arxiv.org/abs/2505.23930

Exploring Societal Concerns and Perceptions of AI: A Thematic Analysis through the Lens of Problem-Seeking
This study introduces a novel conceptual framework distinguishing problem-seeking from problem-solving to clarify the unique features of human intelligence in contrast to AI. Problem-seeking refers to the embodied, emotionally grounded process by which humans identify and set goals, while problem-solving denotes the execution of strategies aimed at achieving such predefined objectives. The framework emphasizes that while AI excels at efficiency and optimization, it lacks the orientation derived…

Bridging the gap: consistent modeling of protoplanetary disk heating and gap formation by planet-induced spiral shocks
Satoshi Okuzumi, Takayuki Muto, Ryosuke T. Tominaga, Shizu Shimizu
https://arxiv.org/abs/2507.18283

Bridging the gap: consistent modeling of protoplanetary disk heating and gap formation by planet-induced spiral shocks
A giant planet embedded in a protoplanetary disk excites spiral density waves, which steepen into shocks as they propagate away from the planet. These shocks lead to secular disk heating and gap opening, both of which can have important implications for the evolution of solids near the planet. To date, these two effects have largely been modeled independently. In this study, we present a self-consistent model that unifies these processes by linking shock heating and angular momentum deposition …

When a video from #Peertube appears in my feed here in #Mastodon, it only shows the title and the URL, nothing more. This is not really helpful.
Would it be possible, that Mastodon shows something like the first 300 characters from the description (500 characters minus 120 for the peertube …

Deep reinforcement learning for efficient exploration of combinatorial structural design spaces
Chloe S. H. Hong, Keith J. Lee, Caitlin T. Mueller
https://arxiv.org/abs/2507.22804

Deep reinforcement learning for efficient exploration of combinatorial structural design spaces
This paper proposes a reinforcement learning framework for performance-driven structural design that combines bottom-up design generation with learned strategies to efficiently search large combinatorial design spaces. Motivated by the limitations of conventional top-down approaches such as optimization, the framework instead models structures as compositions of predefined elements, aligning form finding with practical constraints like constructability and component reuse. With the formulation …

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection
Ji Qi, Xinchang Zhang, Dingqi Ye, Yongjia Ruan, Xin Guo, Shaowen Wang, Haifeng Li
https://arxiv.org/abs/2506.20599

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection
The rapid advancement of generative artificial intelligence is producing fake remote sensing imagery (RSI) that is increasingly difficult to detect, potentially leading to erroneous intelligence, fake news, and even conspiracy theories. Existing forgery detection methods typically rely on single visual features to capture predefined artifacts, such as spatial-domain cues to detect forged objects like roads or buildings in RSI, or frequency-domain features to identify artifacts from up-sampling …

Dependency Network-Based Portfolio Design with Forecasting and VaR Constraints
Zihan Lin, Haojie Liu, Randall R. Rojas
https://arxiv.org/abs/2507.20039 https://

Dependency Network-Based Portfolio Design with Forecasting and VaR Constraints
This study proposes a novel portfolio optimization framework that integrates statistical social network analysis with time series forecasting and risk management. Using daily stock data from the S&P 500 (2020-2024), we construct dependency networks via Vector Autoregression (VAR) and Forecast Error Variance Decomposition (FEVD), transforming influence relationships into a cost-based network. Specifically, FEVD breaks down the VAR's forecast error variance to quantify how much each stock's shock…

Building crypto portfolios with agentic AI
Antonino Castelli, Paolo Giudici, Alessandro Piergallini
https://arxiv.org/abs/2507.20468 https://arxiv.org/pdf/…

Building crypto portfolios with agentic AI
The rapid growth of crypto markets has opened new opportunities for investors, but at the same time exposed them to high volatility. To address the challenge of managing dynamic portfolios in such an environment, this paper presents a practical application of a multi-agent system designed to autonomously construct and evaluate crypto-asset allocations. Using data on daily frequencies of the ten most capitalized cryptocurrencies from 2020 to 2025, we compare two automated investment strategies. …