OpenRaiser/Intern-Atlas huggingface dataset:
Shape Invariant 3D-Variational Autoencoder: Super Resolution in Turbulence flow
Venue: arXiv cs.LG 2025
Year: 2025

Deep learning provides a versatile suite of methods for extracting structured information from complex datasets, enabling deeper understanding of underlying fluid dynamic phenomena. The field of turbulence modeling, in particular, benefits from the growing availability of high-dimensional data obtained through experiments, field observations, and large-scale simulations spanning multiple spatio-temporal scales. This report presents a concise overview of both classical and deep learningbased approaches to turbulence modeling. It further investigates two specific challenges at the intersection of fluid dynamics and machine learning: the integration of multiscale turbulence models with deep learning architectures, and the application of deep generative models for super-resolution reconstruction
Hybrid activation functions for deep neural networks: S3 and S4 -- a novel approach to gradient flow optimization
Venue: arXiv cs.LG 2025
Year: 2025

Activation functions are critical components in deep neural networks, directly influencing gradient flow, training stability, and model performance. Traditional functions like ReLU suffer from dead neuron problems, while sigmoid and tanh exhibit vanishing gradient issues. We introduce two novel hybrid activation functions: S3 (Sigmoid-Softsign) and its improved version S4 (smoothed S3). S3 combines sigmoid for negative inputs with softsign for positive inputs, while S4 employs a smooth transition mechanism controlled by a steepness parameter k. We conducted comprehensive experiments across binary classification, multi-class classification, and regression tasks using three different neural network architectures. S4 demonstrated superior performance compared to nine baseline activation functions, achieving 97.4% accuracy on MNIST, 96.0% on Iris classification, and 18.7 MSE on Boston Housing regression. The function exhibited faster convergence (-19 for ReLU) and maintained stable gradient flow across network depths. Comparative analysis revealed S4's gradient range of [0.24, 0.59] compared to ReLU's 18% dead neurons in deep networks. The S4 activation function addresses key limitations of existing functions through its hybrid design and smooth transition mechanism. The tunable parameter k allows adaptation to different tasks and network depths, making S4 a versatile choice for deep learning applications. These findings suggest that hybrid activation functions represent a promising direction for improving neural network training dynamics.
AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock
Venue: arXiv cs.CV 2025
Year: 2025

Crops, fisheries and livestock form the backbone of global food production, essential to feed the ever-growing global population. However, these sectors face considerable challenges, including climate variability, resource limitations, and the need for sustainable management. Addressing these issues requires efficient, accurate, and scalable technological solutions, highlighting the importance of artificial intelligence (AI). This survey presents a systematic and thorough review of more than 200 research works covering conventional machine learning approaches, advanced deep learning techniques (e.g., vision transformers), and recent vision-language foundation models (e.g., CLIP) in the agriculture domain, focusing on diverse tasks such as crop disease detection, livestock health management, and aquatic species monitoring. We further cover major implementation challenges such as data variability and experimental aspects: datasets, performance evaluation metrics, and geographical focus. We finish the survey by discussing potential open research directions emphasizing the need for multimodal data integration, efficient edge-device deployment, and domain-adaptable AI models for diverse farming environments. Rapid growth of evolving developments in this field can be actively tracked on our project page: https://github.com/umair1221/AI-in-Agriculture
Programmable Data Planes for Network Security
Venue: arXiv cs.CR 2025
Year: 2025

The emergence of programmable data planes, and particularly switches supporting the P4 language, has transformed network security by enabling customized, line-rate packet processing. These switches, originally intended for flexible forwarding, now play a broader role: detecting and mitigating attacks such as DDoS and spoofing, enforcing next-generation firewall policies, and even supporting in-network cryptography and machine learning. These capabilities are made possible by techniques such as recirculate-and-truncate and lookup-table precomputation, which work around architectural constraints like limited memory and restricted instruction sets. In this paper, we systematize recent advances in security applications built on programmable switches, with an emphasis on the capabilities, challenges, and architectural workarounds. We highlight the non-obvious design techniques that make complex in-network security functions feasible despite the constraints of the hardware platform, and also comment on remaining issues and emerging research directions.
Enhancing Jailbreak Attacks on LLMs via Persona Prompts
Venue: arXiv cs.CR 2025
Year: 2025

Jailbreak attacks aim to exploit large language models (LLMs) by inducing them to generate harmful content, thereby revealing their vulnerabilities. Understanding and addressing these attacks is crucial for advancing the field of LLM safety. Previous jailbreak approaches have mainly focused on direct manipulations of harmful intent, with limited attention to the impact of persona prompts. In this study, we systematically explore the efficacy of persona prompts in compromising LLM defenses. We propose a genetic algorithm-based method that automatically crafts persona prompts to bypass LLM's safety mechanisms. Our experiments reveal that: (1) our evolved persona prompts reduce refusal rates by 50-70% across multiple LLMs, and (2) these prompts demonstrate synergistic effects when combined with existing attack methods, increasing success rates by 10-20%. Our code and data are available at https://github.com/CjangCjengh/Generic_Persona.
The role of media memorability in facilitating startups' access to venture capital funding
Venue: arXiv cs.CL 2025
Year: 2025

Media reputation plays an important role in attracting venture capital investment. However, prior research has focused too narrowly on general media exposure, limiting our understanding of how media truly influences funding decisions. As informed decision-makers, venture capitalists respond to more nuanced aspects of media content. We introduce the concept of media memorability - the media's ability to imprint a startup's name in the memory of relevant investors. Using data from 197 UK startups in the micro and nanotechnology sector (funded between 1995 and 2004), we show that media memorability significantly influences investment outcomes. Our findings suggest that venture capitalists rely on detailed cues such as a startup's distinctiveness and connectivity within news semantic networks. This contributes to research on entrepreneurial finance and media legitimation. In practice, startups should go beyond frequent media mentions to strengthen brand memorability through more targeted, meaningful coverage highlighting their uniqueness and relevance within the broader industry conversation.
Multidimensional Assessment of Takeover Performance in Conditionally Automated Driving
Venue: arXiv cs.HC 2025
Year: 2025

When automated driving systems encounter complex situations beyond their operational capabilities, they issue takeover requests, prompting drivers to resume vehicle control and return to the driving loop as a critical safety backup. However, this control transition places significant demands on drivers, requiring them to promptly respond to takeover requests while executing high-quality interventions. To ensure safe and comfortable control transitions, it is essential to develop a deep understanding of the key factors influencing various takeover performance aspects. This study evaluates drivers' takeover performance across three dimensions: response efficiency, user experience, and driving safety - using a driving simulator experiment. EXtreme Gradient Boosting (XGBoost) models are used to investigate the contributions of two critical factors, i.e., Situational Awareness (SA) and Spare Capacity (SC), in predicting various takeover performance metrics by comparing the predictive results to the baseline models that rely solely on basic Driver Characteristics (DC). The results reveal that (i) higher SA enables drivers to respond to takeover requests more quickly, particularly for reflexive responses; and (ii) SC shows a greater overall impact on takeover quality than SA, where higher SC generally leads to enhanced subjective rating scores and objective execution trajectories. These findings highlight the distinct yet complementary roles of SA and SC in shaping performance components, offering valuable insights for optimizing human-vehicle interactions and enhancing automated driving system design.
Opinion formation in Wikipedia Ising networks
Venue: arXiv cs.SI 2025
Year: 2025

We study properties of opinion formation on Wikipedia Ising Networks. Each Wikipedia article is represented as a node and links are formed by citations of one article to another generating a directed network of a given language edition with millions of nodes. Ising spins are placed at each node and their orientation up or down is determined by a majority vote of connected neighbors. At the initial stage there are only a few nodes from two groups with fixed competing opinions up and down while other nodes are assumed to have no initial opinion with no effect on the vote. The competition of two opinions is modeled by an asynchronous Monte Carlo process converging to a spin polarized steady-state phase. This phase remains stable with respect to small fluctuations induced by an effective temperature of the Monte Carlo process. The opinion polarization at the steady-state provides opinion (spin) preferences for each node. In the framework of this Ising Network Opinion Formation model we analyze the influence and competition between political leaders, world countries and social concepts. This approach is also generalized to the competition between three groups of different opinions described by three colors, for example Donald Trump, Vladimir Putin, Xi Jinping or USA, Russia, China within English, Russian and Chinese editions of Wikipedia of March 2025. We argue that this approach provides a generic description of opinion formation in various complex networks.
Towards Safe and Comfortable Vehicle Control Transitions: A Systematic Review of Takeover Time, Time Budget, and Takeover Performance
Venue: arXiv cs.HC 2025
Year: 2025

Conditionally automated driving systems require human drivers to disengage from non-driving-related activities and resume vehicle control within limited time budgets when encountering scenarios beyond system capabilities. Ensuring safe and comfortable transitions is critical for reducing driving risks and improving user experience. However, takeovers involve complex human-vehicle interactions, resulting in substantial variability in drivers' responses, especially in takeover time, defined as the duration needed to regain control. This variability presents challenges in setting sufficient time budgets that are neither too short (risking safety and comfort) nor too long (reducing driver alertness and transition efficiency). Although previous research has examined the role of time budgets in influencing takeover time and performance, few studies have systematically addressed how to determine sufficient time budgets that adapt to diverse scenarios and driver needs. This review supports such efforts by examining the entire takeover sequence, including takeover time, time budget, and takeover performance. Specifically, we (i) synthesize causal factors influencing takeover time and propose a taxonomy of its determinants using the task-capability interface model; (ii) review existing work on fixed and adaptive time budgets, introducing the concept of the takeover buffer to describe the gap between takeover time and allocated time budget; (iii) present a second taxonomy to support standardized and context-sensitive measurement of takeover performance; (iv) propose a conceptual model describing the relationships among takeover time, time budget, and performance; and (v) outline a research agenda with six directions.
Cell-Probe Lower Bounds via Semi-Random CSP Refutation: Simplified and the Odd-Locality Case
Venue: arXiv cs.CC 2025
Year: 2025

A recent work (Korten, Pitassi, and Impagliazzo, FOCS 2025) established an insightful connection between static data structure lower bounds, range avoidance of $\text{NC}^0$ circuits, and the refutation of pseudorandom CSP instances, leading to improvements to some longstanding lower bounds in the cell-probe/bit-probe models. Here, we improve these lower bounds in certain cases via a more streamlined reduction to XOR refutation, coupled with handling the odd-arity case. Our result can be viewed as a complete derandomization of the state-of-the-art semi-random $k$-XOR refutation analysis (Guruswami, Kothari and Manohar, STOC 2022, Hsieh, Kothari and Mohanty, SODA 2023), which complements the derandomization of the even-arity case obtained by Korten et al. As our main technical statement, we show that for any multi-output constant-depth circuit that substantially stretches its input, its output is very likely far from strings sampled from distributions with sufficient independence, and further this can be efficiently certified. Via suitable shifts in perspectives, this gives applications to cell-probe lower bounds and range avoidance algorithms for $\mathsf{NC}^0$ circuits.
Global Patterns of Knowledge: Language, Genre, and the Geography of Knowledge
Venue: arXiv cs.CY 2025
Year: 2025

Online platforms, particularly Wikipedia, have become critical infrastructures for providing diverse linguistic and cultural contexts. This human-curated knowledge now forms the foundation for modern AI. However, we have not yet fully explored how knowledge production capability vary across languages and domains. Here, we address this gap by applying economic complexity analysis to understand the editing history of Wikipedia platforms. This approach allows us to infer the latent mode of ``knowledge-production'' of each language community from the diversity and specialization of its contributed content. We reveal that different language communities exhibit distinct specializations, particularly in cultural subjects. Furthermore, we map the global landscape of these production modes, finding that the structure of knowledge production strongly reflects geopolitical boundaries. Our findings suggest that while a common mode of knowledge production exists for standardized topics such as science, it is more diverse for cultural topics or controversial subjects such as conspiracy theories. The association between differences in knowledge production capability and geopolitical factors implies how linguistic and cultural dynamics shape our worldview and the biases embedded in Wikipedia data, a unique, massive, and essential dataset for modern AI.
Is SHACL Suitable for Data Quality Assessment?
Venue: arXiv cs.DB 2025
Year: 2025

Knowledge graphs have been widely adopted in both enterprises, such as the Google Knowledge Graph, and open platforms like Wikidata, to represent domain knowledge and support artificial intelligence applications. They model real-world information as nodes and edges. To embrace flexibility, knowledge graphs often lack enforced schemas (i.e., ontologies), leading to potential data quality issues, such as semantically overlapping nodes. Yet ensuring their quality is essential, as issues in the data can affect applications relying on them. To assess the quality of knowledge graphs, existing works propose either high-level frameworks comprising various data quality dimensions without concrete implementations, define tools that measure data quality with ad-hoc SPARQL queries, or promote the usage of constraint languages, such as the Shapes Constraint Language (SHACL), to assess and improve the quality of the graph. Although the latter approaches claim to address data quality assessment, none of them comprehensively tries to cover all data quality dimensions. In this paper, we explore this gap by investigating the extent to which SHACL can be used to assess data quality in knowledge graphs. Specifically, we defined SHACL shapes for 69 data quality metrics proposed by Zaveri et al. [1] and implemented a prototype that automatically instantiates these shapes and computes the corresponding data quality measures from their validation results. All resources are provided for repeatability.
A Node on the Constellation: The Role of Feminist Makerspaces in Building and Sustaining Alternative Cultures of Technology Production
Venue: arXiv cs.HC 2025
Year: 2025

Feminist makerspaces offer community led alternatives to dominant tech cultures by centering care, mutual aid, and collective knowledge production. While prior CSCW research has explored their inclusive practices, less is known about how these spaces sustain themselves over time. Drawing on interviews with 18 founders and members across 8 U.S. feminist makerspaces as well as autoethnographic reflection, we examine the organizational and relational practices that support long-term endurance. We find that sustainability is not achieved through growth or institutionalization, but through care-driven stewardship, solidarity with local justice movements, and shared governance. These social practices position feminist makerspaces as prefigurative counterspaces - sites that enact, rather than defer, feminist values in everyday practice. This paper offers empirical insight into how feminist makerspaces persist amid structural precarity, and highlights the forms of labor and coalition-building that underpin alternative sociotechnical infrastructures.
DeltaVLM: Interactive Remote Sensing Image Change Analysis via Instruction-guided Difference Perception
Venue: arXiv cs.CV 2025
Year: 2025

Accurate interpretation of land-cover changes in multi-temporal satellite imagery is critical for real-world scenarios. However, existing methods typically provide only one-shot change masks or static captions, limiting their ability to support interactive, query-driven analysis. In this work, we introduce remote sensing image change analysis (RSICA) as a new paradigm that combines the strengths of change detection and visual question answering to enable multi-turn, instruction-guided exploration of changes in bi-temporal remote sensing images. To support this task, we construct ChangeChat-105k, a large-scale instruction-following dataset, generated through a hybrid rule-based and GPT-assisted process, covering six interaction types: change captioning, classification, quantification, localization, open-ended question answering, and multi-turn dialogues. Building on this dataset, we propose DeltaVLM, an end-to-end architecture tailored for interactive RSICA. DeltaVLM features three innovations: (1) a fine-tuned bi-temporal vision encoder to capture temporal differences; (2) a visual difference perception module with a cross-semantic relation measuring (CSRM) mechanism to interpret changes; and (3) an instruction-guided Q-former to effectively extract query-relevant difference information from visual changes, aligning them with textual instructions. We train DeltaVLM on ChangeChat-105k using a frozen large language model, adapting only the vision and alignment modules to optimize efficiency. Extensive experiments and ablation studies demonstrate that DeltaVLM achieves state-of-the-art performance on both single-turn captioning and multi-turn interactive change analysis, outperforming existing multimodal large language models and remote sensing vision-language models. Code, dataset and pre-trained weights are available at https://github.com/hanlinwu/DeltaVLM.
A Fuzzy Set-based Approach for Matching Hand-Drawing Shapes of Touch-based Gestures for Graphical Passwords
Venue: arXiv cs.HC 2025
Year: 2025

This paper presents a two-dimension fuzzy set based approach for matching touch-based gestures using fuzzy cued click point technique. The pro posed approach aims mainly to improve the acceptance of the most closed inac curate hand drawn gestures generated by the user compared with a predefined referenced gesture value that is stored in the user profile. Commonly, gestures are used in order to facilitate the interactive capabilities between humans and computerized systems. Unfortunately, most of current gesturing techniques don't deal at the same level of inaccuracy of gesturing, resulted from the nature of hu man fingers and hands movements. This paper aims, in a more flexible manner, to tackle the inaccuracy problem existed with gesture-based interactions between humans and a computerized system.
Approximating the quantum value of an LCS game is RE-hard
Venue: arXiv cs.CC 2025
Year: 2025

We generalize Håstad's long-code test for projection games and show that it remains complete and sound against entangled provers. Combined with a result of Dong et al. \cite{Dong25}, which establishes that $\MIP^*=\RE$ with constant-length answers, we derive that $\LIN^*_{1-ε,s}=\RE$, for some $1/2< s<1$ and for every sufficiently small $ε>0$, where LIN refers to linearity (over $\mathbb{F}_2$) of the verifier predicate. Achieving the same result with $ε=0$ would imply the existence of a non-hyperlinear group.
Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Venue: arXiv cs.CL 2025
Year: 2025

In this report, we introduce Falcon-H1, a new series of large language models (LLMs) featuring hybrid architecture designs optimized for both high performance and efficiency across diverse use cases. Unlike earlier Falcon models built solely on Transformer or Mamba architectures, Falcon-H1 adopts a parallel hybrid approach that combines Transformer-based attention with State Space Models (SSMs), known for superior long-context memory and computational efficiency. We systematically revisited model design, data strategy, and training dynamics, challenging conventional practices in the field. Falcon-H1 is released in multiple configurations, including base and instruction-tuned variants at 0.5B, 1.5B, 1.5B-deep, 3B, 7B, and 34B parameters. Quantized instruction-tuned models are also available, totaling over 30 checkpoints on Hugging Face Hub. Falcon-H1 models demonstrate state-of-the-art performance and exceptional parameter and training efficiency. The flagship Falcon-H1-34B matches or outperforms models up to 70B scale, such as Qwen3-32B, Qwen2.5-72B, and Llama3.3-70B, while using fewer parameters and less data. Smaller models show similar trends: the Falcon-H1-1.5B-Deep rivals current leading 7B-10B models, and Falcon-H1-0.5B performs comparably to typical 7B models from 2024. These models excel across reasoning, mathematics, multilingual tasks, instruction following, and scientific knowledge. With support for up to 256K context tokens and 18 languages, Falcon-H1 is suitable for a wide range of applications. All models are released under a permissive open-source license, underscoring our commitment to accessible and impactful AI research.